Additionally, potential advantages can be accessed such as quality control of the dairy chain, easier bovine mastitis prognosis, lower cost, analytical frequency, and could serve as an evaluative parameter to verify the health of the mammary gland.This report describes how image processing harnessed to multivariate analysis techniques can be used as a bio-analytical tool for mastitis screening in cows using milk samples collected from 48 animals , totalizing a dataset of 144 sequential images was collected and analyzed. In this context, this methodology was developed based on the lactoperoxidase activity to assess mastitis using recorded images of a cuvette during a simple experiment and subsequent image treatments with an R statistics platform. The color of the sample changed from white to brown upon its exposure to reagents, which is a consequence of lactoperoxidase enzymatic reaction. Data analysis was performed to extract the channels from the RGB (Red-Green-Blue) color system, where the resulting dataset was evaluated with Principal Component Analysis (PCA), Multiple Linear Regression (MLR), and Second-Order Regression (SO). Interesting results in terms of enzymatic activity correlation (R Mastitis is an endemic disease, which affects the mammary glands, is characterized by an inflammatory response involving metabolic, physiological, and traumatic changes caused by pathogenic microorganisms . The conMastitis can cause changes in the ion, protein, and somatic cell concentrations, as well as in some enzymes which can affect the yield and quality of dairy products ,6.In this context, lactoperoxidase (LPO) is one of the enzymes which are active in this process. It acts in the defense of the mammary gland through the lactoperoxidase system during the initial infection stages by producing antimicrobial substances, which in turn act by destroying or inhibiting the action of pathogens ,8. ReseaMastitis control has been constantly investigated and most studies focus on vaccine development or herd management to reduce or eliminate the causative agents. The current focus of investigating the disease addresses modulating the immune response of the mammary gland to increase the gland’s resistance to invading pathogens since the udder immunity is also responsible for the clinical outcome of the disease .In recent years, computational tools in artificial intelligence, such as machine learning, are being successfully applied in diverse areas. Significant progress has been achieved in imaging recognition, including methods for monitoring animal health conditions . Deep coImaging analysis by thermal infrared was capable of detecting mastitis in dairy cows through the correlation between infrared image and somatic cell count (SCC). The accuracy of the mastitis classification algorithm was 83.33%, the sensitivity was 92.31% and the specificity was 76.47%. This method realized the accurate positioning of key parts of dairy cows and can be used for the automatic recognition of dairy cow mastitis .Colorimetric detection from digital image capture presents instrumental simplicity and has been explored in various analytical applications. Digital image capture devices such as smartphones offer some advantages, including accessibility, portability, low cost, and battery power or USB  connection with portable computers. These advantages provided by digital image capture devices encourage the use of this instrumental tool for remote analysis or in places where the availability of resources is limited. These factors favor the use of portable systems to conduct remote analyses, in which a preliminary result, even if qualitative, can be essential for a preventive diagnosis –14.Furthermore, digital image processing can refine the visual quality of the data by reducing noise, improving the detection, and, most importantly, providing a more reliable analysis. This strategy has been used in multivariate image analysis methods like principal component analysis, allowing classification (or discrimination), segmentation defect detection, and even prediction . Thus, iThe project was submitted to evaluation by the Ethics Committee on the Use of Animals at UFRN (protocol No. 051.061/2017) and all animal management practices followed the recommendations of the National Council for the Control of Animal Experimentation (CONCEA) for the protection of animals used for animal experimentation and other scientific purposes.The sample universe was represented by one (n) group of 48 samples of cow’s milk, with 32 samples coming from Jersey animals obtained from the São Miguel farm located in São Gonçalo do Amarante, RN, Brazil; 07 samples from Gir animals and another 09 from Guzerat from the Felipe Camarão Experimental Station (EMPARN)/Parnamirim, RN, Brazil. The samples were taken in triplicate from the milking system collectors and identified with the animal’s code in polyethylene bottles of approximately 40 mL. No type of preservative was used. They were packed in isothermal boxes with ice at a temperature between 2°C and 6°C and subsequently sent to the Milk Quality Laboratory (LABOLEITE/UFRN) for analysis.® Kit , qualitative lactoperoxidase determination by the methodology proposed by the Ministry of Agriculture, Livestock and Supply (MAPA) [The samples were subjected to somatic cell count analysis (SCC) using the Somaticelly (MAPA) . There w® Kit which has the operation objective to determine the somatic cell count from the viscosity of milk against a specific reagent in a direct proportion, in which the more viscous the milk, the more somatic cells it presents. The sample reading was performed according to grading the analysis flask which varied from 69 to 1970. The number in the tube at the milk level indicated the estimated amount of somatic cells in thousands.The samples were subjected to somatic cell count analysis using the SomaticellThe reagents used to qualitatively analyze the enzyme were 1% guaiacol and 10 Vol hydrogen peroxide. First, 10 mL of the milk aliquot was added to a test tube. The sample was subjected to heating at 45°C in a water bath for 5 minutes for enzymatic activation. After that period, 2.0 mL of 1% guaiacol and 3 drops of 10 Vol hydrogen peroxide. A positive result was the formation of a solution in salmon color varying between light and dark after 5 minutes. Then, the samples were transferred to a quartz cuvette to be photographed and analyzed.The quartz cuvettes with the samples were placed on a carton apparatus with opaque white bottom and background . Imaging-1 solution, and 0.1 mL of the 0.0032 mol L-1 hydrogen peroxide solution were added to the cuvette to read the sample. The reading was performed immediately and the initial absorbance value and the value after two minutes were recorded, with the change in absorbance value in that time interval being used for the enzymatic concentration calculations. The results were expressed in unit of enzymatic activity/mL, for which a unit of enzymatic activity was defined as the amount of enzyme that catalyzes the oxidation of 1μmol of ABTS per minute at room temperature. The enzymatic activity was calculated according to [A 5100PC UV/VIS spectrophotometer  was used at an absorbance of 412 nm as a function of time for two minutes to determine the enzymatic activity in milk samples. The milk samples were diluted to 1:250 in sodium phosphate buffer solution pH 7.0. Volumetric flasks of 10 mL were used in which 40 μL of milk were needed for this dilution. Each sample had its respective blank and 0.1 ml of milk in solution and 3.0 ml of ABTS solution were added to a quartz cuvette for reading. Next, 0.1 mL of the milk in solution, 3.0 mL of the ABTS 0.001 mol Lrding to .A pure bovine lactoperoxidase enzyme from Sigma-Aldrich  was used to obtain the calibration curve for the lactoperoxidase enzyme. Therefore, 1 mg of the lactoperoxidase enzyme was used and dissolved in 1 liter of the pH 7.0 phosphate buffer solution. Next, 1.0 to 9.0 mL of the enzyme in solution was manipulated and dilutions ranging from 100 to 900 μL were performed using 10 mL volumetric flasks. The method for reading the samples was similar to that performed with milk samples.c) was calculated using the equation below as an approximation to the Lambert-Beer Law:Data analysis was performed using the R 4.1.1 statistical platform. Median filtering (EBImage package) was applied in pre-processing, which reduces the amount of intensity variation between neighboring pixels in the images. The separation of RGB matrices (EBImage package) was performed by calculating the isolated color intensity for each channel as an algebraic average and the colorimetric absorbance analytical response  in the images were selected using the free software GIMP 2.8.18. Square portions of 50×50 pixels were chosen from the center of each region of the cuvette see  and reco6 and factoextra7 package) for the image data of the samples was developed using the absorbance values resolved for the RGB channels .PCA analysis  as a chromogenic agent for developing a test strip for enzymatic identification and using a color analyzer when compared to the TMB methodology by the spectrophotometric method, thus validating the method used.The enzyme calibration curve in units mLt 420 nm  establisThe digital imaging devices used in this work had the purpose of capturing sections of the sample and converting them into measurable quantities of their RGB data, thereby enabling to obtain mathematical relationships between the studied variable, in this case, the enzymatic activity, and the variables which describe the system, i.e. the RGB channels .This evaluation enables the study to detect the main similarity, association, and correlation patterns so that the images can subsequently be used to provide quantitative measures ,20–23.The primary colors in the RGB data system  can be articulated to simulate all existing colors in the visible region spectrum . In thisThe color generated by the sample can be correlated with the concentration of the substance to be analyzed, thus presenting a semi-quantitative response in which the color provided can be compared with a previously established color gradient and the concentration of the substance in the sample can be estimated by visual comparison ,23.Imaging measurements are performed with the use of an image capture device, quantifying and analyzing the color intensity in the colored region. The results are then correlated with the analyte concentration using previously constructed calibration curves ,25,26. TThe PCA model composed of scores and loadings was used as exploratory analysis to investigate a response pattern for the lactoperoxidase enzymatic determination tested in milk samples, which are used to identify the similarity/dissimilarity between samples and evaluate the contribution size of each variable for the model, respectively .Principal component analysis is one of the most widely used chemometric techniques, as it enables reducing the dimensionality of the data without losing relevant information, highlighting significant analytical signals, in addition to serving as a basis for other chemometric methods .This model has the advantages of understanding and effectively visualizing data and clarifying the interrelationship between the samples and the variables studied. One of the most useful mechanisms of the PCA is that it produces a linear transformation of the data calculated by a simple multiplication of matrices using a smaller data set and the transformation is quickly calculated for the rest of the data .The samples and RGB channels are plotted by Principal Component Analysis  and the The samples quantified for lactoperoxidase were subjected to multivariate regression analysis for the three RGB color channels and compared to the standard model. The results presented reasonable deviations in its responses considering that it is simple and low-cost. The coefficient of determination was 0.96 by MLR, as shown in 2 = 0.97 with the Rottenfusser method (which uses guaiacol and p-phenylenediamine) in comparison to the method described by [ also intribed by . The coeribed by , based o2 values of 0.96, 0.94, and 0.97, respectively, concluding that enzymatic stability decreases with increasing temperature. evaluate evaluate2 = 0.99).The coefficient of determination was 0.97 in correlating the proposed model with the somatic cell count (SCC) experimental data by MLR, which is represented in 2 value = 0.90 for the correlation of bioactive amines and somatic cell count.Considering other studies which have been conducted regarding the behavior of SCC , evaluat2 value = 0.90 when associating the current emitted by an amperometric sensor with the amount of SCC present in raw milk.In developing an electrochemical method for determining bacterial count, Listeria monocytogenes, and somatic cell count , obtaine2 = 0.99 when comparing the association between the two methods.When designing a portable somatic cell counter consisting of a camera and a miniaturized microscope , observeThe use of digital images can potentially replace equipment such as the spectrophotometer for a variety of colorimetric reactions, as they are cheaper and have easy accessibility. The ease in capturing images and obtaining them with a cell phone, digital cameras, and scanners are advantages that support its use in analytical quantification.The lactoperoxidase enzyme and the somatic cell count in milk samples from cows can be measured via images, and the enzymatic method may be used as a health indicator of the mammary gland.Therefore, lactoperoxidase can be quantified by this method and contribute as an evaluative parameter in determining mastitis in dairy cows.S1 Fig(PDF)Click here for additional data file.S2 Fig(PDF)Click here for additional data file.S3 Fig(PDF)Click here for additional data file.


The quantity of data required to give a valid analysis grows exponentially as machine learning dimensionality increases. In a single experiment, microarrays or gene expression profiling assesses and determines gene expression levels and patterns in various cell types or tissues. The advent of DNA microarray technology has enabled simultaneous intensive care of hundreds of gene expressions on a single chip, advancing cancer categorization. The most challenging aspect of categorization is working out many information points from many sources. The proposed approach uses microarray data to train deep learning algorithms on extracted features and then uses the Latent Feature Selection Technique to reduce classification time and increase accuracy. The feature-selection-based techniques will pick the important genes before classifying microarray data for cancer prediction and diagnosis. These methods improve classification accuracy by removing duplicate and superfluous information. The Artificial Bee Colony (ABC) technique of feature selection was proposed in this research using bone marrow PC gene expression data. The ABC algorithm, based on swarm intelligence, has been proposed for gene identification. The ABC has been used here for feature selection that generates a subset of features and every feature produced by the spectators, making this a wrapper-based feature selection system. This method's main goal is to choose the fewest genes that are critical to PC performance while also increasing prediction accuracy. Convolutional Neural Networks were used to classify tumors without labelling them. Lung, kidney, and brain cancer datasets were used in the procedure's training and testing stages. Using the cross-validation technique of k-fold methodology, the Convolutional Neural Network has an accuracy rate of 96.43%. The suggested research includes techniques for preprocessing and modifying gene expression data to enhance future cancer detection accuracy. With increasing data dimensionality in machine learning, the quantity of data required for analysis grows exponentially.p, small n” issue (where p is the number of features and n is the number of samples) may lead to overfitting. The difficulty may arise due to loud characteristics.Bellman alluded to the “curse of dimensionality” while acknowledging the challenges of dynamic optimization. The issue is solved by projecting a high-dimensional dataset onto a smaller variable number (or feature) that preserves the information. Microarray data usually pertain to a small sample issue. Each data point (or sample) may include up to 450,000 variables (gene probes), resulting in a significant computational cost. Due to the sparsity of such relevant data for every dataset, it is very difficult to show that this finding is statistically significant. Large datasets with a “large Gene expression data have been found to be both data-rich and data-poor in terms of information, and the public databases of the microarray are the National Center for Biotechnology Information, GenBank, the Array Express, the Gene Expression Omnibus, and the Stanford Microarray .Microarrays provide genome-wide patterns in gene expression, increasing the clinical uses of this technology for illness diagnosis, therapy, and prognosis.WPact features . On the Latent  characteThere may be hundreds of these places on the microarray, each containing about a million copies of the undistinguishable DNA molecules linked with the gene. In the location, any gene will identify genomic DNA, or a short oligonucleotide strand modified to match any gene as DNA. Microarrays have been used for the appearance of the gene in various ways and continued in a particular condition (state A) for a comparable collection of genes from reference cells (state B). Allow cDNA from state A cells to be examined with one red dye and cDNA from state B cells to be analyzed with a green dye. They will hybridize to the exact locations on a glass slide that has a similar arrangement of the same kind if they are marked differently. When cDNA is attached to a spot, the actual quantity of cDNA is proportional to the original number of RNA molecules and the number of molecules of the gene present in the samples.These areas in a hybridized microarray will be stimulated using a laser and scanning at their proper wavelengths for detecting the red and green dyes, respectively, after completing the processes.X in a specific gene is in profusion from condition Y, the spot will be reflected as red in the cDNA. Otherwise, the color will be green. If the gene has been expressed in a similar manner, the spot may be yellow, and if the gene has not been expressed, the area may be completely black. Consequently, for each place on the microarray belonging to a gene, there will be an associated value of fluorescence, which will be displayed as the gene's expression level in the picture of the array.The quantity of fluorescence produced upon excitation will be proportional to the amount of nucleic acid that has been bound. As a result, if a cDNA with condition Using the principles of nucleic acid hybridization and pairing, the Microarray technology has been developed to identify seven such complementary sequences in compound combinations. Single-stranded labelled “probes” of all of the recognized sequences have been developed to detect the presence of unknown samples and their compliments on the microarray. The early techniques of macroscopic hybridization are employed as flexible and porous filters to provide a solid support for halting the detection probes, which are used as solid supports for stopping the probes. This starting use of solid supports such as glass and the decrease of all of the immobilized nucleic acid characteristics that were apparent as soon as the first microarrays appeared were all significant developments.The “probe” refers to the immobilized strand in the nucleic acid, and the “target” refers to a complimentary phase component that is used in conjunction with the “probe.” For the most part, the probes on the microarrays were of three kinds. The first was cDNA, the second was resynthesized oligonucleotides, and the third was in situ synthesized oligonucleotides . The novelty and the major contribution of the proposed work in the identification of informative genes are accomplished by statistical metrics or optimization strategies. The marker genes utilized in this study were selected using a two-step procedure. The initial stage involves retrieving feature subsets from the database. The first phase involves analyzing the original microarray data with statistical measurements, and the second step involves analyzing the original microarray data with statistical measures. Look for the optimum feature subsets that will enhance the classification performance. Heuristic optimization is used to improve performance. Heuristic optimization is used to improve performance. The organization's fundamental goal is to The purpose of the study is to find the marker genes that increase categorization accuracy. The goal of the study was to find the marker genes that increase categorization accuracy.The following is the structure of the paper. There are several surveys relevant to the planned work that is being conducted by different research organizations. As a result of the feature selection method, Ang et al.  found thAccording to the American Cancer Society, the subtypes of colorectal cancer have been characterized by their latest molecular classifications in terms of global gene expression. Summary: They are displaying opposition to therapy, in addition to other signs of opposition. In order to avoid an erroneous assessment of the situation, several categories have been examined, and a decision has been reached. Erroneous assessment of the situation Several categories have been examined, and a decision has been reached by Calon et al. (2015)  found thA promising and novel method for identifying cancer and metastasis has been developed. The most recent advances in construction and architecture have given opportunities for metastasis. In addition, dynamic bimolecular settings for different cancers are being investigated. Types: as a result, we may assume that the connections between gene expressions are significant. By studying different types of cancer, we may have a better understanding of how cancer develops early on in bimolecular networks that have been linked to both normal and pathological processes. Cancerous states are a kind of cancer. This theory has been tested by Ling et al. (2014), and they investigated if there is a relationship between the mRNA terminologies of three different genes.When the cancer-related genes PIK3C3, PIM3, and PTEN were randomly selected, “The cancer had advanced,” and these coefficients have been put through their paces in the field of cancer research. Diagnosis: during the course of the illness, the following observations were made: it was discovered that there were robust correlations (0.68 r 1.0) between the variables: PIM3 and PIK3C3 in breast cancer; PIM3 and PTEN in breast, renal, and ovarian cancer; and malignancies of the liver and thyroid, and cancers of the breast and ovary involving PIK3C3 and PTEN cancers. This ascribes that the connections for early cancer diagnosis are important for the gene expression profiles of cancer networks and may be added to the current clinical data. Biomarkers such as cancer antigens are examples of biomarkers. Cancer is responsible for about ten to fifteen percent of all human deaths. Viruses: to discover novel viruses and the relationships that exist between them, massive parallel sequencing has shown to be effective in both malignant and normal tissues.In certain patient populations, this approach has not yet been implemented. An example from Tang et al. (2013)  has utilThe real-time quantitative reverse transcription-polymerase chain reaction was used to monitor and regulate the process. Furthermore, in previous research, 58 GC patients were included. Prior to the GC diagnosis, at least one blood sample must be collected, as a progressive, to believe in a brighter future. The discovery of new advancements in known serum miRNA expression has continued. Based on the results of a long-term follow-up with a group of individuals, the serum samples that were obtained were collected over a period of five years before the clinical diagnosis of GC was determined. According to the panel, the total accuracy rate was 79.3 percent. As a result, it was shown that these three serums showed their promise as novel diagnostic tools for the early identification of GC noninvasive biomarkers.Allory et al.  found thThe following may be investigated further:Aspects of Urothelial Bladder Cancer's (UBC) spectrum, its relationship to expression and clinical prognosis, and the prevalence of TERT mutations to assess the TERT To study promoter alterations, a cohort of 111 UBCs at various phases of development was used. Sequencing using the Sanger method and messenger RNA (mRNA) communication via TERT, the reverse transcription-quantitative polymerase chain reaction was used to accomplish this Using the Snapshot test, the two most frequently occurring mutations have been identified. UBC researchers examined a separate group of 184 non–muscle-invasive and 173 muscle-invasive UBC patients. Bennet et al. 2014)  propose   proposet-tests have been used to compare two groups of data. The CUP genes in the top fifty-nine positions have the. It was decided to choose the maximum fold with a probability of less than 0.001. Six points out of ten in the set of the forty-four genes that were found to be upregulated in the CUP set were classified, and ribosomal proteins [It is more successful in improving our knowledge of cancer biology. It can predict the development and reaction of the disease and the capacity to monitor the cure of cancer. The probability of finding a shared diagnostic biomarker for two diseases Cancer of Unknown Primary (CUP) was investigated using a microarray gene expression analysis in the Kobayashi et al. (2013)  researchproteins  are protFollowing the feature selection of molecular data, the suggested method has been related to the triple-negative breast cancer subtype. However, this method is not accurate because of the unsupervised job given to the trainees throughout the training phase. During the feature selection process, the proposed study explains how to overcome the limitations of unsupervised learning. The method of extracting multi-selection features from microarray data is characterized as an unsupervised training procedure based on the data. This method is used based on the weights assigned to each component acquired during the data partitioning process and the subset of features generated. However, one of the disadvantages is that weight loss occurs throughout the L1 normalization process. This paper proposes classification methods based on unsupervised learning and feature selection strategies based on the normalization of gene expression for use during classification . The inpThe proposed work consists of two main stages, namely, training and testing stages. During the training stage of gene expression data, feature selection is carried out to categorize the input gene expression dataset.Machine learning methods provide a solution to the issue of having a large number of datasets to work with. The data may be split into two categories: the training dataset and the validation dataset, respectively. This data collection is used to calibrate the parameters of certain models that have been developed using it. The training dataset is used to evaluate the effectiveness of the learned features. The Kaggle gene expression dataset is used to collect data and it is an open dataset that is available for the researchers to access freely and identify the cancer using machine learning algorithm.This paper  suggestsThe feature selection method identifies the suitable features. It discards the inappropriate elements, resulting in a reduction in the input dimensionality and an improvement in the classification performance of the classifier. Furthermore, many studies have shown that most genes discovered via DNA microarray study are insignificant for making an accurate difference between different classes of the issue. To avoid being plagued by the curse of dimensionality, feature selection is an essential component of DNA microarray research. The second important rationale for reducing dimensionality is that it will aid scientists in discovering the underlying mechanism that links gene expression to disease progression.In data mining, feature selection is used in a number of different fields such as classification, association rules, clustering, and regression. Feature selection algorithms developed using a variety of evaluation concepts may be divided into three categories: filter algorithms, wrapper algorithms, and hybrid models. Without taking into account any mining method, the filter model is based primarily on generic characteristics of the data analyzed, and feature subsets are chosen without taking into account any mining process. Searching for more suitable features for mining algorithms is more time-consuming than searching for features that are better suited for other algorithms. The hybrid model makes use of the advantages of both models by using both of their changed assessment criteria at different search stages, thus maximizing their combined benefits. To divide each point, machine learning algorithms choose the most relevant characteristics to the situation at hand; ideally, one should never pick unnecessary or redundant traits. It is widely accepted that the addition of irrelevant characteristics to a dataset “confuses” the machine learning system in situations when there is overfitting and makes the system less understandable .In a document, information gain (IG) is defined as the quantity of information gained for the purpose of making a group prediction. The existence vs lack of a feature is used to evaluate the reduction in entropy in a system. This application may be thought of as a generic method that assesses informational entropy to determine a specific characteristic's significance. When combined with Shannon entropy, informational entropy may be used to calculate the amount of data needed to encode a particular piece of information. The entropy of a system is directly proportional to the amount of space taken up by the information it is trying to encode.When it comes to categorization, the information at stake is the distribution of instances inside different classes of objects. If the instances are allocated haphazardly among classes, the number of bits required to encode this class distribution is large since each individual example must be calculated. When all of the instances are members of a single class, the entropy is reduced. Consequently, when the class distribution is spread in a large number of subgroups, the entropy of subsets of the data should rise, according to the entropy-measurement function.The murmur is a feature selection technique in which features are chosen based on their high correlation with the class (output) and their low correlation with one another . In the case of continuous features, the F-statistic is used in the calculation of the correlation between features (relevance) and the computation of the correlation between features and other variables (redundancy). It is necessary to apply the Pearson correlation coefficient. The features are selected one by one using a greedy search to maximize the objective function, which may be thought of as a function of relevance and redundancy. The selection of features is made in this manner: one feature is selected after another.In this study, the objective functions that were utilized were the Mutual Information Difference (MID) criteria and the Mutual Information Quotient (MIQ).As represented in When the relief algorithm assigns relevance weights to each characteristic, it takes advantage of instance-based learning to do so. Weights are allocated to each feature among the many class values. A final subset of characteristics is produced by ranking the features according to their weight and selecting the higher weight features than the user requested. Threshold: in this approach, training data examples are selected at random from a pool of instances. Upon sampling a feature, the closest feature of the same and opposite class is identified for that feature. The weight of an attribute is updated depending on how its values are differentiated from those of the sample features and those of its closest hit and miss neighbors, respectively. The importance of a feature increases if it differentiates between characteristics from distinct classes while maintaining a consistent value for features from the same class.According to the importance of the feature relief algorithm to the classification job, an algorithm is given to each feature relief feature. Initially, all weights are given a value of zero, and then 50 iterative updates are carried out. During this procedure, two groups of instances are selected: those with the closest characteristics that belong to the same class, and those with a distinct set of features that belong to a separate class. Relief-F is used to deal with multi-class issues since it expands Relief's capabilities to allow for dealing with incomplete and noisy information.Relief-D is a deterministic version of the algorithm that uses all occurrences and the near-miss and near-hit of each feature. This results in the equivalent running relief being available for an indefinite period of time after that.The disadvantage of univariate filters, such as IG, is that they do not account for correlations between features, which may be reduced by multivariate filters, such as Correlation-based Feature Selection, which is described below (CFS). The value of a subset is determined via the use of CFS of attributes, which considers the individual predictive power of each feature and the degree of redundancy among the characteristics. It is possible to estimate the correlation between subsets of features and class and the correlation between features themselves using the correlation estimate. When there is a connection between features and classes, the significance of a set of features increases and declines as the intercorrelation between features and classes increases . CFS aidn, and the number of protein-coding genes in each dataset is given by dm. The data matrix of each dataset is denoted by the notation Un, dm (uniform data matrix). P must be less than dm to choose a subgroup; p denotes the gene normalization process.Each patient's data point was collected via the use of microarrays, and this dataset is a collection of those data points. The data collection includes high-dimensional gene expression information obtained from a range of tumor profiles, and the information is organized into categories. Cancers such as lung cancer, kidney cancer, and brain cancer are among those that may be discovered. To get information about cancer genome quotes, you must first visit the global cancer genome quotation site. 840 and 345 cancer samples from the squamous cell subtype and adenocarcinoma subtype, respectively, are mixed up with cells from the adenocarcinoma subtype in the lung cancer dataset . With 14For the experimental methods, it is possible to split the data into two groups. The first group contains the feature X1; the second group has the sir classes X2. The matrix value is structured as main underclass nx1, where x represents the number of samples and n indicates the number of genetic factors in each class in the feature matrix of the feature. The data collection, which contains a variety of samples, is split into two parts: training data and authentication data. The training samples consist of 139 models, whereas the authentication samples consist of 36 models each. The training dataset is used to perform the first calibration, which is done using a deep learning algorithm with the training dataset. The hyper parameter was applied to the validation set and is used to determine the correctness of the algorithms in question. The correctness of each procedure is determined by adjusting the hyper parameter using the fold cross-validation method to prevent overfitting the values , 25. TheThe read count value of gene expression is included inside the dataset. The range of gene expression fluctuates and is less than one in size. This means that the logarithmic equations must be used to perform the transformation:x represents the gene characteristics acquired from the microarray dataset in calculation 1, which signifies the gene characteristics received from the microarray dataset. With the usage of filtering techniques, the threshold value will be the same as 1.19 in the gene expression throughout the whole sample when the filtering methods are used. Depending on the threshold setting, the accuracy and consistency of classification are both compromised. Because the lower threshold value preserved a greater number of genes, the value of a specific biomarker will not be reduced. It is possible to convert normalized values into gene expression levels by using input genitival microarray representation, as the third phase begins with the transformation that was used to estimate the performance of the intended job. This is the beginning point for the fourth step. Asymmetrical transformations are applied to the data, which are determined by the properties of the individual datasets. When it comes to passing the suggested job, the Latent Feature Assortment Technique is employed, and the feature assortment procedure is essential for accomplishing the given task (LFST).If the values are less than one,  will decm growth examples with the typical d1 gene appearance topographies in the matrix range of Amd1 and the latent feature selection method, which are used in conjunction with the d1 gene appearance features. In the case of regularized topographies with a lower range, the form of p is represented in the case of d1, while the form of p is represented in the case of d2. Initially, the trained feature is produced via the use of an autoencoder model in combination with the PCA feature extraction technique. This trained feature is then used as the starting point for the feature selection procedure. It is necessary to train the Convolutional Neural Network on the features retrieved via PCA before using it to perform the feature selection process on the features. When the latent space is stated in the form of the z dimension, the latent space meets the criteria ld. A Latent space with a Gaussian kernel was used to project the Amd1 data in order to get the final outcome in this case. In the Gaussian kernel emulator, the Gaussian kernel is represented by the symbol KZ and utilized as a target kernel. A kernel for each feature is created by constructing Amd1, the matrix holding the D1 set of features. This results in the creation of a kernel for each feature in D1. Finally, the most recent model is used to reduce the subset of p centers that are alternately designated and merged to create K with an upsurge in the arrangement of the kernels in the matrix, with an increase in the arrangement of the kernels in the matrix.When using the recommended feature selection approach, the latent feature selection methodology is used to pick the most relevant features. It is composed of The Latent Feature Selection Method consists of an optimization procedure that inputs the normalized score features collected and utilizes them to choose the most suitable components. The feature selection process is organized using an unsupervised approach, and the best features are selected using the procedure results. It is necessary to utilize an m-dimensional interplanetary purpose value that is symmetrical and semi-positive to meet the criterion.Here, apply the Hilbert space transform, where the purpose signifies the dimensionality of the high-dimensional information and H is the Hilbert interplanetary convert coefficient shown in equation . The HilXi, the kernel matrix obtained, which may be regarded as information for the training phase.n is the number of characteristics that were present in the genetic factor sample collection; the kernel may be constructed using a function derived from the core matrix, which produces the desired output.It is represented by  and defiEquation  providesM by using the alignment method.Various kernels are available. K1 and K2 both have different numbers of samples, as well as different arrangements of the board. The sample set has a variety of topographies that may be used for preparation, and it is composed of three regulated input appearances. Equation  signifieElement selection is made out using equation  to separIt is necessary to incorporate the Convolution Neural Network Classifier in both the training and testing stages of the process since it is based on a deep learning method. For the prediction of specific cancer types, it has been proposed that a CNN model be used. Each CNN model is intended to perform this function in order to create gene expression, data models. Class predictions are produced based on the qualities that have been selected or arranged and have been given as input. It is necessary to employ a one-layer Convolutional matrix with restricted samples for the development of the CNN architecture. The number of parameters used in the network determines how many samples are utilized. The CNN model is built on a one-dimensional kernel with two input vectors as its foundation. Reference .The Kera's visualization package  was usedThe gene has a copy number of 2.6 and is classified as a cancer gene. The epoch and batch size are set at 60 and 128, respectively. Initially, all CNN models were trained on the different tumor samples that were available. In t-test, and ANOVA are often employed to identify DEGs. The use of heuristics to solve the DEG issue is possible. It is planned to include more meta-heuristic methods and improve the computational complexity. These natural optimization methods may be used with classifiers to improve classification accuracy [One of the main flaws of microarray technology is the production of noise. Some advanced noise reduction and data normalization approaches may be investigated in addition to the suggested feature selection methods. Differentially expressed genes (DEGs) may be investigated. Statistical methods like fold-change, accuracy .The subject features and superfluous features are suggested to be reduced from a large dimensional feature of gene expression  data usi


The identification of cancer types is of great significance for early diagnosis and clinical treatment of cancer. Clustering cancer samples is an important means to identify cancer types, which has been paid much attention in the field of bioinformatics. The purpose of cancer clustering is to find expression patterns of different cancer types, so that the samples with similar expression patterns can be gathered into the same type. In order to improve the accuracy and reliability of cancer clustering, many clustering methods begin to focus on the integration analysis of cancer multi-omics data. Obviously, the methods based on multi-omics data have more advantages than those using single omics data. However, the high heterogeneity and noise of cancer multi-omics data pose a great challenge to the multi-omics analysis method.In this study, in order to extract more complementary information from cancer multi-omics data for cancer clustering, we propose a low-rank subspace clustering method called multi-view manifold regularized compact low-rank representation (MmCLRR). In MmCLRR, each omics data are regarded as a view, and it learns a consistent subspace representation by imposing a consistence constraint on the low-rank affinity matrix of each view to balance the agreement between different views. Moreover, the manifold regularization and concept factorization are introduced into our method. Relying on the concept factorization, the dictionary can be updated in the learning, which greatly improves the subspace learning ability of low-rank representation. We adopt linearized alternating direction method with adaptive penalty to solve the optimization problem of MmCLRR method.Finally, we apply MmCLRR into the clustering of cancer samples based on multi-omics data, and the clustering results show that our method outperforms the existing multi-view methods. Cancer is a series of complex diseases with high heterogeneity. Nowadays, cancer has gradually become one of the most common and fatal diseases worldwide. Medical studies show that gene variation and mutation are the main factors leading to the formation and development of cancer diseases , 2. MoreSequencing technology has opened the omics era of life science and is leading and changing the development of the whole field of cancer research , 4. WithIn the field of bioinformatics, machine learning algorithms play an important role in disease diagnosis, pathogenic factors discovery and treatment outcome prediction, etc. , 9. As aAll of the above studies indicate that a variety of mutation mechanisms contribute to the occurrence and development of cancer . In ordeRecently, the low-rank representation method, namely LRR, was proposed to solve the problem of subspace clustering . In LRR,In light to the shortcomings described above, we present Multi-view Manifold Regularized Compact Low-Rank Representation method, which is called MmCLRR for short. Unlike most LRR based approaches, in MmCLRR, the concept factorization  idea is A multi-view based clustering analysis method named MmCLRR is proposed. Against specified fixed dictionary matrix used in most LRR methods, in MmCLRR, we adopt concept factorization to model the dictionary matrix. Concept factorization makes the dictionary update continuously during optimization, which enhances the completeness of dictionary and breaks through the bottleneck of using fixed dictionary matrix to describe subspace in LRR. In addition, we apply manifold regularization to further preserve the local topology of the data in the projecting. Benefiting by concept factorization and manifold regularization, the proposed method can capture the inherent subspace structure located in each view, and identify the latent subspace hidden in multi-view.We apply MmCLRR to model cancer multi-omics data, and further propose a new cancer clustering framework based on multi omics data. This will make the clustering study of cancer get rid of the limitation of single omics data, and greatly promote the development of multi-omics data in cancer clustering research.The clustering framework of MmCLRR is used to study cancer clustering, and many experiments of cancer samples clustering based on multi-omics data are provided. The experimental results indicate that it is feasible to cluster cancer using multi-omics data. These results also demonstrate the effectiveness of MmCLRR in cancer clustering.The key contributions of this study are summarized as below.The remainder of this article is schemed as follows. In Sect. 2 a brief overview of the related work including LRR, manifold regularization as well as concept factorization is given. In Sect. 3, the developed MmCLRR method and its model on cancer multi-omics data are elaborated. The experiment results and the performance analysis based on MmCLRR and several comparison methods are demonstrated in Sect. 4. The conclusion of this work is given in Sect. 5.LRR is an important method of subspace clustering firstly developed by Liu et al. . The maiHere, In practice, the original high-dimensional data are directly regarded as mentclass2pt{minimHere, Generally, the observations from the real world are noisy. In order to reduce the influence of noise on subspace learning, an error item is usually added to the object of LRR. For random noise, we often employ entclass1pt{minima removal , 36 or fustering  or classustering –40, and nal data .MLRSSC is a multi-view clustering framework. It jointly learns a subspace representation by constructing a consistent similarity matrix shared by multi-view data. Given a dataset Here, Usually, the naturally generated data are approximately regarded as to be located in a certain manifold. Many studies have shown that the manifold structure of data is very important to the low-dimensional space learning or low-dimensional representation , 43. Howse refer . In thisHere, According to the basic assumption of manifold theory, namely, if two data points in the data manifold are close to each other, their mappings of the two data points in a new coordinates are still close , we can Here, n matrix . \documegorithms –50.The basic idea of concept factorization is that each prominent concept in the observation data set can be represented by associating data points with similar concepts . Namely,Here, On the other hand, the data point in the observation data can also be approximated by linear union of these concepts, in mathematics, which can be expressed in the following formula.In Eq. , \documeresearch , 52.In this part, the proposed Multi-view Manifold Regularized Compact Low-Rank Representation (MmCLRR) method and its solution are elaborated. And then the model of MmCLRR based on cancer multi-omics data is given.Most LRR-based methods select observed data as dictionary to learn the low-rank representation of high-dimensional data. The noise contained in the data and the insufficient sample size will lead to the incompleteness of the dictionary, which will directly affect the mapping expression of the original data in the low-dimensional space. To this end, we introduce concept decomposition into MLRSSC method to reconstruct dictionary matrix using the linear combination of original sample points. Meanwhile, in view of the advantages of manifold regularization in exploring the local structure of manifold, we further introduce manifold regularization into our method. In MmCLRR, we combine the sparse LRR model with the data dictionary modeling and manifold regularization constraints to obtain the subspace structure information comprehensively. Given a dataset with Here,  item in  can helpmentclass2pt{minimWe use Linearized Alternating Direction Method with Adaptive Penalty (LADMAP)  to solve problem  is conveThen, we draw into augmented Lagrangian method. The function  is recasHere, The formula  is separThen, the final iterative algorithm is obtained by solving the above sub problems in turn. It is assumed that all variables after the (1) Updating  problem , we take(2) Updating  problem  with regAccording to LADMAP, the solution of  problem .22\docum problem  is as foHere,(3) Updating  problem  as belowLet formula  be 0, anAccording to literature , the lit(4) Updating  problem  is as be(5) Updating eference , the iteHere, (6) Updating (7) Updating rem 1 in , we solvHere, (8) Updating Finally, based on the low-rank representation matrix of each view, we calculate the fused affinity matrix  formula .36\documThe detailed optimization process of MmCLRR method is shown in Algorithm 1.As mentioned earlier, besides gene expression data, DNA methylation and copy number variation also play important roles in the formation and development of cancer. And these omics data of cancer have been used alone or in combination with other data for cancer type research. This fully shows that these data contain the characteristic information needed in cancer type recognition. Thinking different omics data as the expression of cancer features at different levels, it is reasonable for us to regard that the feature information in these omics data can complement each other. Therefore, we intend to fuse the characteristic information of gene expression data, DNA methylation data and copy number variation data to cluster cancer samples. Here, we think of each omics data as a feature view of cancer, and use MmCLRR method to model these omics data. The schematic diagram of MmCLRR model on multi-omics data is shown in Fig. We use Accuracy (Acc) , Rand InAcc evaluates the clustering performance at the global level by calculating the matching degree between the experimental labels and the actual labels. It is defined as follows.Here, –Munkres  is usualRI assesses the performance of clustering algorithm by comparing the relationship between the actual classification and the experimental classification. The following is the definition of RI.Here, F1 is the average of precision rate and recall rate, which is defined as below.Here, The data sets used in our study, including Head and Neck cancer (HNSC), Esophagus Cancer (ESCA) and Colon Adenocarcinoma (COAD), are downloaded from TCGA. Each data set contains three types of omics data, namely gene expression, DNA methylation data and copy number variation. And these omics data in each dataset come from the same batch of samples. Each of the three data sets includes cancer samples and normal samples. Specifically, HNSC consists of 398 cancer samples and 20 normal samples, ESCA includes 183 cancer samples and 9 normal samples, and COAD has got 262 cancer samples and 19 normal samples. The number of genes in gene expression, DNA methylation data and copy number variation data is 20502, 23,627 and 21,031, respectively. The samples and genes distribution of each omics data is shown in Table In order to test and verify the performance of our method in cancer samples clustering, we compare MmCLRR with the existing multi-views analysis methods, including ioNMF , SNF 2323, BlockAmong the methods, BLLRR, MLRSSC and MmCLRR are low-rank subspace clustering (LRSC) methods. These LRSC methods mainly use the nuclear norm constraint to obtain the low-rank representation of multi-omics data, so as to explore the subspace structure of data. And, they construct the affinity matrix based on low-rank representation for cancer samples clustering. SNF is a network-based approach. It constructs similarity network for each omics data, and then integrates these networks generated by different omics data to realize samples clustering. The ioNMF approach is a NMF-based method. In ioNMF, different omics data are decomposed into a common fusion matrix and multiple independent sub matrixes at the same time, and then the common matrix is used to cluster samples. So, we firstly compare the three subspace clustering methods with ioNMF and SNF. From Table Among the three LRSC methods, MLRSSC method does not take the local topology of data into account in subspace learning. Different from MLRSSC method, both BLLRR and MmCLRR methods are all committed to obtaining the global and local structures of manifold in multi-omics data by introducing manifold regularization constraint into LRR. Therefore, next, we compare MLRSSC with BLLRR and MmCLRR. For the convenience of comparison, as shown in Fig. Thirdly, we compare MmCLRR with BLLRR. As be seen in Fig. Finally, the MmCLRR approach is compared with MLRSSC. These two methods are basically consistent in the framework and main ideas for multi-view processing. There are two differences between them. On the one hand, compared with MLRSSC, manifold constraint is introduced into MmCLRR. On the other hand, the construction methods of dictionary are different. As mentioned above, MmCLRR takes the linear combination of original data as dictionary to update the dictionary matrix with the algorithm optimization, while MLRSSC uses original data as the fixed dictionary. From Fig. In MmCLRR method, there are four regularization parameters MmCLRR is a novel multi-view integration analysis framework based low-rank decomposition. Our main contribution is to model dictionary matrix by concept factorization, which enables the dictionary matrix to update with subspace learning, thus enhancing the ability of dictionary to describe subspace. The comparative experiment of MmCLRR with other four multi-view methods is given on real multi-omics data. And the experiment results indicate that MmCLRR has a good performance in subspace clustering. In our experiment MmCLRR treats all omics data equally, so the parameter In this study, we develop a multi-view low-rank subspace clustering method, named as MmCLRR, to analyze caner multi-omics data. MmCLRR aims to achieve the consistent low-rank representation from multi-view data by balancing the consistency of different views. In our method, concept factorization is adopted to model dictionary. That is, the dictionary is constructed as the combination of the original data. Furthermore, the manifold regularization is introduced into our method to grasp the local structural relationship within the data. So, MmCLRR can capture the global and local structure of submanifold shared by multi-view data more efficiently. Finally, we adopt the proposed method to cluster cancer samples based on multi-omics data from TCGA. The experimental results demonstrated that our method can outperform the state-of-the-art multi-view approaches. In the future, we will promote the application of MmCLRR in other fields of cancer research.


Liquid biopsies offer a minimally invasive sample collection, outperforming traditional biopsies employed for cancer evaluation. The widely used material is blood, which is the source of tumor‐educated platelets. Here, we developed the imPlatelet classifier, which converts RNA‐sequenced platelet data into images in which each pixel corresponds to the expression level of a certain gene. Biological knowledge from the Kyoto Encyclopedia of Genes and Genomes was also implemented to improve accuracy. Images obtained from samples can then be compared against standard images for specific cancers to determine a diagnosis. We tested imPlatelet on a cohort of 401 non‐small cell lung cancer patients, 62 sarcoma patients, and 28 ovarian cancer patients. imPlatelet provided excellent discrimination between lung cancer cases and healthy controls, with accuracy equal to 1 in the independent dataset. When discriminating between noncancer cases and sarcoma or ovarian cancer patients, accuracy equaled 0.91 or 0.95, respectively, in the independent datasets. According to our knowledge, this is the first study implementing an image‐based deep‐learning approach combined with biological knowledge to classify human samples. The performance of imPlatelet considerably exceeds previously published methods and our own alternative attempts of sample discrimination. We show that the deep‐learning image‐based classifier accurately identifies cancer, even when a limited number of samples are available. To our knowledge, this is the first report that uses a deep neural network to analyze RNA‐sequencing data in liquid biopsies. imPlatelet method shows superior performance, detecting cases even in the early stage ovarian cancer. It shows remarkable potential in healthy individuals' indication. We believe that similar approach could be applied to sequencing data of tissues or single cells. AUCarea under curveCTCcirculating tumor cellsctDNAcirculating tumor DNADNNdeep neural networkKEGGKyoto Encyclopedia of Genes and GenomesMUGMedical University of GdańskNSCLCnon‐small cell lung cancerOCovarian cancerPSOparticle swarm optimizationROCreceiver operating characteristicSVMsupport vector machineTEPstumor-educated platelets1Sequencing technologies have enabled in‐depth analysis of liquid biopsies in cancer, offering a minimally invasive sample collection. This new approach to patient management has lately revolutionized cancer diagnostics, allowing for accurate detection of cancer , monitorMultiple nucleic acid‐based assays are applied to study liquid biopsy material , 11, 12.As artificial intelligence includes multiple types of machine‐learning and deep‐learning approaches, we hypothesized that medical diagnostics could also benefit from this ongoing machine‐learning revolution. We observed that thromboSeq method, which relies on PSO‐enhanced SVM , might bFirst, we tested imPlatelet method on a published cohort of NSCLC patients, sarcoma patients, former sarcoma patients, and healthy donors , 25. Nex22.1The detailed list of all cases, along with their characteristics, is presented in Table Blood collected in Gdańsk was processed within 48 h upon collection, strictly according to thromboSeq protocol . Briefly2.2trimmomatic (v. 0.22) https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4103590/, mapped to the human reference genome (hg19) using star (v. 2.3.0), and summarized using htseq (v. 0.6.1), guided by the Ensembl gene annotation version 75. The expression data for each sample were normalized using DESeq2 package . Th. Th22]. 3.2RNA‐based diagnostics can benefit from gene panels, similarly to DNA‐focused studies. We present here a cancer panel based on KEGG Pathway database , applied3.3r [Panel approach transforms numeric RNA‐sequencing data into a set of figures. Each input sample is turned into a separate image. Therefore, widely used classification methods like SVM or random forests cannot be utilized for this type of input. To classify the generated image data, we employed a DNN, based on TensorFlow and Keras libraries, implemented in r .In our approach, the network resembles visual cortex of the brain, characterized by a high level of connectivity. The DNN Fig.  consistsOnce the network is trained using the input dataset, a test set of independent samples (not used in the training procedure) is classified. The final sample class decision is conducted using the output unit, activated by a sigmoid function. Because of the sigmoid function application, our method currently supports only two‐class decision making.3.4GSE158508 [To evaluate imPlatelet, we tested the method in three TEP datasets. Sample selection was preceded by quality control filtering. The first dataset consisted of 401 NSCLC and 204 healthy donor samples , 36, theSE158508 .et al. [et al., validation set was called evaluation set, and independent test set was called validation set.The NSCLC dataset included mostly stage IV cases 343/401). As the advanced disease strongly affects the platelet expression profile, the performance of the classifier in this dataset was excellent. Specificity, sensitivity, and balanced accuracy were all equal to 100% Fig. , Table 1. As the et al. . Note, tet al. [Sarcoma dataset was more challenging in classification than NCSLC dataset. First, the number of samples was lower and the dataset was imbalanced . Second, patients had less advanced disease than NSCLC cohort . Both of these factors made DNN training and testing more complicated Fig. . We focuet al. . We alsoet al. .Finally, imPlatelet method was tested in our own, previously unpublished OC dataset Fig. . In an iNext, in order to provide better insight in the classification dynamics of patients with OC, benign controls and healthy donors, we generated the distribution of the TEP scores in samples belonging to the independent set, classified with imPlatelet and thromboSeq, respectively Figs  and 7. T4Our imPlatelet classifier reached higher balanced accuracy and sensitivity in all three tested independent datasets: lung cancer, sarcoma, and OC. So far, no data have been published on TEPs in OC. Liquid biopsy alternatives, namely CTCs and ctDNA, proved limited utility in the early diagnosis of the disease. Detection of CTCs in OC is difficult even in the advanced stages of the disease , and theet al., Aliper et al., and Antonio et al. rely on deep learning, the authors focus on data other than RNA sequencing and the architecture of their network differs considerably [According to our knowledge, this is the first study implementing an image‐based approach to classify biological samples. While works of Matsubara iderably , 42, 43.Important features of imPlatelet classifier are the calculation of balanced accuracy and execution time. Some methods tend to classify all samples as cases or controls in an unbalanced dataset, leading to performance estimation bias. Balanced accuracy is therefore suitable also for datasets with a small number of cases. In terms of execution time, the training process in the DNN‐based approach takes minutes, up to hours, as opposed to thromboSeq which requires days, up to weeks. five‐fold cross‐validation of the model with OC data takes approximately 6 min, compared to over 3 days using thromboSeq. Once the model is trained, the testing procedure in imPlatelet requires less than one second per sample, using a desktop computer . Consequently, for diagnostic use, imPlatelet could be executed on a laptop with an attached tensor processing unit accelerator.Several limitations of the study need to be acknowledged. First, we had a limited number of OC cases when compared with nonmalignant and healthy controls. The recently published studies suggest that even 70–80 cases are a reasonable number , 44, butet al. [Summing up, our developed algorithm shows superior performance, detecting cancer cases even in the early I and II stage OC. It shows remarkable potential in healthy individuals' indication, paving the way to correct classification in other diseases. We believe that similar approach could be applied to sequencing data in tissues. For example, analysis of splice variants in tumor tissue has been considered promising in cancer immunotherapy . In the et al. . Further5To our knowledge, this is the first report that uses a DNN to analyze RNA‐sequencing data. We believe we have created a classifier which significantly outperforms other available tools used in cancer diagnostics: GRAIL , CancerShttps://github.com/KrzysztofPastuszak/ImplateletCode available at KP conceptualized the study, performed methodology, involved in software programming, validated the study, performed formal analysis, involved in investigation, provided resources, curated the data, wrote, reviewed, and edited the draft, and visualized the data. AS conceptualized the study, performed methodology, involved in investigation, provided resources, curated the data, wrote, reviewed, and edited the draft, visualized the data, supervised the study, involved in project administration, and acquired the funding. MGB conceptualized the study, involved in investigation, provided resources, curated the data, and wrote, reviewed, and edited the draft. SGJGIV curated the data and wrote and reviewed the draft. SŁS conceptualized the study, involved in investigation, provided resources, curated the data, and wrote, reviewed, and edited the draft. AŁ provided resources, curated the data, and wrote and reviewed the draft. RR conceptualized the study, performed methodology, involved in investigation, and wrote, reviewed, and edited the draft. AJŻ conceptualized the study, provided resources, wrote, reviewed, and edited the draft, and acquired the funding. JJ conceptualized the study, involved in investigation, provided resources, wrote, reviewed, and edited the draft, supervised the study, and acquired the funding. TW conceptualized the study, involved in investigation, provided resources, curated the data, wrote, reviewed, and edited the draft, and acquired the funding. TS conceptualized the study, involved in investigation, wrote and reviewed the draft, and supervised the study.TW and MGB are inventors on relevant patents. TW is shareholder of GRAIL Inc. KP, AS, TS, AJZ, and JJ have filed a patent application regarding the classifier.https://publons.com/publon/10.1002/1878‐0261.13014The peer review history for this article is available at Table S1. The table summarizes the panel of 243 pathways extracted from KEGG database: 22 pathways directly implicated in cancer, 131 signaling‐related pathways and 90 pathways involved in metabolism.Table S2. The table cases used for training, validation and independent test set. For sarcoma dataset, controls include healthy donors and former sarcoma samples. For OC dataset, controls include benign controls and healthy donor samples.Table S3. The table specifies which cases were used for which experiment. OC used 5‐fold cross‐validation with independent test set. Both sarcoma and NSCLC experiments were conducted using sample splits as close as possible to those used in the original articles. Several samples from the NSCLC and sarcoma datasets were not included as they did not meet the QC requirements.Table S4. Performance of thromboSeq on the same dataset that was used for OC classification by imPlatelet.Click here for additional data file.Movie S1. Manuscript overview. The link to the promo video can be found here: https://youtu.be/FrPFehYQHLU.Click here for additional data file.


Network intrusion detection remains one of the major challenges in cybersecurity. In recent years, many machine-learning-based methods have been designed to capture the dynamic and complex intrusion patterns to improve the performance of intrusion detection systems. However, two issues, including imbalanced training data and new unknown attacks, still hinder the development of a reliable network intrusion detection system. In this paper, we propose a novel few-shot learning-based Siamese capsule network to tackle the scarcity of abnormal network traffic training data and enhance the detection of unknown attacks. In specific, the well-designed deep learning network excels at capturing dynamic relationships across traffic features. In addition, an unsupervised subtype sampling scheme is seamlessly integrated with the Siamese network to improve the detection of network intrusion attacks under the circumstance of imbalanced training data. Experimental results have demonstrated that the metric learning framework is more suitable to extract subtle and distinctive features to identify both known and unknown attacks after the sampling scheme compared to other supervised learning methods. Compared to the state-of-the-art methods, our proposed method achieves superior performance to effectively detect both types of attacks. Network intrusion detection systems (NIDS) play important roles in network security in the past several decades –3. NIDS Despite substantial advances being made, there exist two major challenges in designing a reliable and effective NIDS, namely the imbalanced training data sets and the frequent occurrences of unknown attacks. In information systems, normal samples in network traffic are sufficient, easy to obtain, and diverse in subtypes. However, it is very difficult to obtain network attack samples because abnormal flow accounts for a small proportion of total flow, and traffic samples of newly emerging forms of attacks such as “zero-day” attacks are difficult to obtain.To address the imbalanced data problem, either over- or undersampling strategy has been proposed to balance the training data –14. HoweTo address the above-mentioned challenges, in this paper, we propose a novel NIDS algorithm that integrates an unsupervised subtype sampling scheme with a few-shot learning-based Siamese capsule network to achieve reliable detection of different types of network attacks as well as identify new unknown attacks effectively. Specifically, we design a new sampling method based on unsupervised machine learning techniques, for example, clustering to group training samples of each network attack type into subtypes of data. With this method, more representative samples can be preserved when balancing the training data. These samples are then used to train the few-shot learning-based Siamese capsule network so that subtle patterns and distinctive features can be extracted by a metric learning framework. These two components are complementary to build a reliable and effective NIDS.K value from an unbalanced data set. This scheme can obtain large representative samples by clustering the training data of each attack type into subtypes, thus taking data distribution into consideration. It further improves the reliability of the few-shot learning network performance.We propose a new unsupervised subtype sampling mechanism to construct a few-shot learning training data set with an indefinite We develop an innovative Siamese capsule network by adapting the capsule network architecture into the Siamese network for intrusion detection. As a result, the location information across features can provide extra cues to help detect distinctive patterns of intrusion attacks.C-way K-shot E-extra problem in the context of a few-shot learning framework in the field of intrusion detection so that our approach can detect unknown attack types without samples. When facing unknown attacks, this is more like a special zero-shot learning method based on few-shot learning [We redefine a so-called learning . In the Recently, there are several few-shot learning-based intrusion detection methods proposed in –18. ThesThe remainder of this paper is organized as follows. In this section, several issues in NIDS that are relevant to this paper are discussed separately, including network intrusion detection techniques, method of unbalanced data processing, and few-shot learning. A compilation of related work is shown in Network intrusion detection systems are usually used to detect various malicious traffic in information systems. Thus, they can be defined as binary classification systems to distinguish between normal and malicious network traffic. Wang et al.  proposedRecently, deep learning-based algorithms are widely used in intrusion detection due to their excellent performance in classification tasks. Wu et al.  proposedTo address the imbalanced training data problem, extensive research has been undertaken in preprocessing training data –29 as thk-means clustering and posterior probability SVM (PPSVM) for unbalanced data based on robot vision and achieved a relatively significant improvement in detection performance. While significant progress has been made, challenges remain for these existing preprocessing methods. For example, synthesizing samples using oversampling techniques can reduce the sample quantity gap between classes but increase the likelihood of overlapping samples within classes, thus creating samples that do not provide valid information. Undersampling balances the number of samples between types by reducing the number of sufficient classes but is prone to overfitting. In a nutshell, data augmentation alleviates overfitting in low data regimes but does not solve it.Yilmaz et al.  proposedTo address the detection of unknown attacks, few-shot learning models have been proposed to solve tasks with a limited number of training samples . The modRecently, two few-shot learning methods for intrusion detection have been proposed by Yu and Bian  and Xu eThe architecture of our proposed approach is illustrated in Specifically, the approach will work as follows. At the training stage of our intrusion detection algorithm, data samples from different types of attacks and normal network traffic are clustered and sampled based on the proposed unsupervised subtype sampling scheme, which is explained in the next subsection. After resampling the raw data set, the balanced data set and data samples collected from scarce attack types are used to form the training set for the Siamese capsule network training so that the few-shot learning algorithm could learn more distinctive features to identify the network attacks with such imbalanced data set. In addition, the balanced few-shot training set is used as the support set at the test stage to identify the abnormal network behaviours. At the test stage, we use the most similar samples in the support set to classify the tested samples after extracting features from the Siamese capsule network. It is to be noted that two-dimensional grayscale images converted from the traffic vectors are built as the input feature representations of the proposed framework. The detail of the representation is explained in the experiment in Learning tasks in scenarios with unbalanced sample numbers have received extensive research attention. Although a large volume of normal traffic data could be easily collected, training samples of intrusion attacks are usually much scarcer. When dealing with unbalanced data sets, traditional methods usually use data enhancement and enrich supervision information to construct new balanced data sets . The speC-way K-shot” in few-shot learning is a learning method, which constructs C categories, and each category has K samples. In this method, the value of K for each category is usually fixed and identical. However, the intra- and interclass variations of traffic data in network intrusion detection vary when the K value is changed. If the value of K is much smaller than the type of its subtype, the learning ability of the algorithm for normal samples will be insufficient, which will affect the detection performance. On the contrary, if a high K value is set, the subtypes may have too few samples as the sample number of newly emerging attacks is sparse. Therefore, it is still difficult to build a suitable few-shot training set using a fixed K value. In our method, instead of pursuing the balance between samples and categories, we set the K value as an adaptive value, that is, the value of K is different in different types. In this way, we are able to fully learn the features in normal types, while avoiding the restriction on K values of sparse classes.The “To illustrate the variations of traffic samples of different types of attacks, we randomly sample six types of attacks, including Benign, Bot, DDoS, PortScan, DoS GoldenEye, and Web Attack SQL Injection in the CICIDS-2017 data set  and randIn an information system, the normal traffic of the HTTP protocol and the SNMP protocol behave differently in connection characteristics, traffic characteristics, and header content; even the normal traffic within the HTTP protocol is different. As the goal of traffic attacks is to disguise normal samples from all levels, many samples of the attack data have significant variations in characteristics, while samples of different attack types share similarity in some characteristics. Therefore, when constructing a few-shot data set, it is required to design a sampling scheme to obtain sufficient samples to cover each subtype of these attacks.k-means method [K number is determined adaptively based on the silhouette coefficient [As shown in s method  to clustfficient , which ba(i) represents the average of the distances from the samples i in the cluster to all other samples in the cluster and b(i) represents the minimum value of the average distance from the sample i in the cluster to all samples in the cluster closest to the sample. The calculation result of the silhouette coefficient is between −1 and 1. After setting a set of candidate K values and run the k-mean method to cluster the data in each attack type, the final K value for each type is selected based on the following equation, which is the smallest number of clusters from the top n largest silhouette coefficient.where n is usually set to 10 and argmaxn represents the number of clusters corresponding to the largest first n silhouette coefficient. After obtaining the most appropriate number of clusters, we take one sample from each subtype after clustering to build a few-shot training set of sufficient classes. In contrast, this new sampling method is able to select representative samples from sufficient classes for training and can alleviate the problem of information loss in random undersampling. As shown in where the parameter K-means algorithm was used for clustering. According to the above unsupervised subtype sampling method, the optimal number of clusters is 20. After completing the clustering, a sample is randomly selected from each subtype to observe the distribution of unsupervised sampling samples in all samples. As shown in We illustrate the sampling results in C and the sampling value K of each type. Construct a few-shot learning data set, including training set, query set, support set, and test set.  Step 1: determine the number of types   Step 2: choose suitable feature extraction neural network algorithms to construct a backbone network with weight sharing and choose a suitable similarity measurement method to construct a comparison network.  Step 3: randomly sample the same type and different types of sample pairs as the input of the Siamese network. If the two samples in the input sample pair are of the same type, the similarity label is 1, and if the types are different, the similarity label is 0.  Step 4: compare the output label with the real label to obtain the loss and establish the network model step by step iteratively.  Step 5: bring the sample pair composed of the tested sample and the sample in the support set into the model to measure the similarity. Take the sample type in the support set with the highest similarity to the tested sample as the tested sample type.Siamese network is an application form of few-shot learning in the field of supervised learning framework. Its main goal is to learn a reliable classification model based on a very small number of samples. It is also considered as one type of metric learning method, which classifies samples by comparing the similarity between the tested samples and the labelled samples in its support set. The classification task establishment process is as follows:The main function of the Siamese backbone network is to extract features from samples. CNN can effectively extract features, but it also has certain limitations. First, the data is transferred between neurons in a scalar way. Scalar has only content but no direction, so CNN is not strong in recognizing the spatial position relationship between features. Second, the pooling layer of CNN will lose a lot of valuable information. The characteristic location of the network traffic sample is very sensitive , and theAlthough the capsule network guarantees the directionality of the feature extraction process, the initial process of extracting features from the original data still needs to rely on the convolution operation. As shown in In our work, we propose the Siamese capsule network for the NID system. As the metric model is a crucial part of the few-shot learning method, the Siamese network is used in our work. As illustrated in As shown in x1i, x2i are two random samples input at one time. If the samples are of the same type, y  = 1, otherwise, it is y  = 0. In addition, we also use the Adam optimizer with better convergence performance [where formance . To solvTo evaluate the detection effect of the proposed methods, we conduct experiments using the CICIDS-2017 data set  and UNSWN = 9 and establish each sample as a 9 ∗ 9 grayscale image to extract geometric features.Among the selected 7 attack types on the CICIDS-2017 data set, we define 5 of them as known attack types. The other 2 attack forms (iG and iH) simulate unknown attacks, and there are no samples of these two types to be used in the training set. Among the known attack types, the iB and iC attack types are set to have sufficient traffic samples, and the iD, iE, and iF attack types have limited traffic samples. Each sample in the data set has 78 features and 1 sample label. We set N = 7 and establish each sample as a 7 ∗ 7 grayscale image to extract geometric features.Among the selected 6 attack types on the UNSW_NB15 data set, we define 4 of them as known attack types. The other 2 attack forms (rF and rG) simulate unknown attacks, and there are no samples of these two types to be used in the training set. Among the known attack types, the rB and rC attack types are set to have sufficient traffic samples, and the iD and rE types have limited traffic samples. Each sample in the data set has 49 features and 1 sample label. We set We conduct experiments under two different settings to simulate the imbalance of data in practical applications. CICIDS-2017 data set is taken as an example, in the first setting; we set the maximum number of training samples for the Benign, DDoS, and Bot types in abnormal traffic to 1,500, 1,000, and 500, respectively, and the maximum number of training samples for scarce attack types PortScan, DoS GoldenEye, and Web Attack SQL Injection to 5, 5, and 5, respectively. In the second setting, we set the maximum number of training samples for the Benign, DDoS, and Bot types in abnormal traffic to 3,000, 2,000, and 1,000, respectively, and the maximum number of training samples for scarce attack types PortScan, DoS GoldenEye, and Web Attack SQL Injection to 20, 20, and 10, respectively. After obtaining different types of available training data sets, value samples are selected to form the training data set through unsupervised subtype sampling and establish multiple training sets with different sample sizes to verify the usability of the method. The UNSW_NB15 data set is the same in the selection strategy of the training set. As shown in The experiment was carried out under the environment of CPU Intel Xeon E5-2620, GPU NVIDA GTX1080ti, RAM 64G, video memory 11G, CuDNN 7.6.5, CUDA 11.0, TensorFlow 1.13.1, and Keras 2.2.4.TP: normal samples are correctly detected as normal samplesFN: normal samples are incorrectly classified as abnormal samplesTN: attack samples are correctly detected as abnormal samplesFP: attack samples are incorrectly classified as normal samplesIn addition, to test the classification on the known attacks, the detection task is also tested on unknown attacks. The classification of unknown attack samples relies on the comparison of their similarity with normal samples and abnormal samples. Therefore, the model's detection of traffic samples is a process of binary classification of normal samples and abnormal samples. The test results of the samples are divided into the following four types:We use three evaluation indicators including accuracy rate, precision rate, and recall rate to evaluate the method. The accuracy rate is the ratio of the number of samples correctly classified to the total number of samples, which can reflect the accuracy of the model classification. The precision rate is the proportion of real positive samples in the samples that are judged to be positive. The recall rate refers to the proportion of samples that are judged to be positive in all samples that are truly positive. The latter two items can reflect the classification performance of the method from two aspects: false positives and underreports. The formulas of each evaluation standard are as follows:The above three evaluation criteria can effectively judge the detection accuracy of the method, but in order to better show the model's ability to detect attack traffic, we introduce the detection rate to further evaluate the method. The detection rate refers to the proportion of samples that are correctly judged as negative classes in the entire negative class samples, that is, the proportion of detected attack samples occupying all attack samples. The expression formula is as follows:The Adam optimizer can maintain fast convergence but has insufficient generalization ability. After being supported by the decay strategy, its loss function converges more smoothly. As shown in To verify the superiority of our proposed method, we also compare our method with different backbone networks that are integrated into the Siamese network. The DCNN network proposed by Yu and Bian , the VGGOn training A of CICIDS-2017, the few-shot capsule network demonstrated an overall advantage with accuracy and recall of 98.37% and 96.29%, respectively. While ensuring a high accuracy rate for all samples, the Siamese capsule network algorithm can achieve an 86.55% abnormal detection rate, which is relatively stable performance. On training B, compared with the other two Siamese network algorithms, the Siamese capsule network still maintains a leading advantage as a whole. Although its detection rate of anomalies is slightly lower than that of the capsule network algorithm, it maintains a leading position in comprehensive evaluation criteria such as accuracy and recall. The experiments on the UNSW_NB15 data set further demonstrate the superiority of the method in the paper, maintaining the lead in the correct classification of both abnormal samples and normal samples. According to Figures To test the sampling effect of the unsupervised subtype sample method, resampling method , random As shown in K value. The support set is established using a small amount of random sampling method. Its Siamese network architecture using FC-NET is constructed by a deep neural network (DNN). When testing, the tested sample is compared with the samples in different types of support set, and the type of the tested sample is judged by the size of the average value of each type in the tested sample support set. The difference between the above method and the method proposed in this paper is shown in To pursue higher detection accuracy, the method mentioned in  considerTo show the application effect of the method in the intrusion detection field, the few-shot learning method mentioned in the literature  is compaIn K = 5, the FC-NET method has a better detection effect on unknown anomalies. However, as shown in When facing unknown attack types such as iG, iH, rF, and rG, on the data set of In addition to comparing the above few-shot learning method recently proposed and applied in the field of intrusion detection with the method in the article under the same conditions, we also included other methods for comparison on different data sets. As shown in K values and builds a Siamese capsule network that can perform directed feature extraction. The experimental results show that we have achieved high accurate classification rate using only a very small number of samples, on the detection of both known attacks and unknown attacks. The detection of unknown attacks in our work is particularly outstanding due to the advantage of the metric learning framework.In this paper, we designed a novel few-shot learning-based intrusion detection method with imbalanced training data. This method uses unsupervised subtype sampling to establish a few-shot data set with adaptive In future research, we will further explore the temporal information to embed it into the meta-learning algorithms for NIDS. We will investigate new few-shot-based learning frameworks, such as triplet network and contrastive learning methods. Additionally, we will incorporate parallelization mechanisms to further improve the detection efficiency of the method and make it more relevant to practical applications of intrusion detection.