In exoRBase 2.0, the number of exLRs has increased to 19 643 messenger RNAs (mRNAs), 15 645 long non-coding RNAs (lncRNAs) and 79 084 circular RNAs (circRNAs) obtained from ∼1000 human blood, urine, cerebrospinal fluid (CSF) and bile samples. Importantly, exoRBase 2.0 not only integrates and compares exLR expression profiles but also visualizes the pathway-level functional changes and the heterogeneity of origins of circulating EVs in the context of different physiological and pathological conditions. Our database provides an attractive platform for the identification of novel exLR signatures from human biofluids that will aid in the discovery of new circulating biomarkers to improve disease diagnosis and therapy.Extracellular vesicles (EVs) are small membranous vesicles that contain an abundant cargo of different RNA species with specialized functions and clinical implications. Here, we introduce an updated online database ( Extracellular vesicles (EVs) are nano- to micrometer-sized lipid membrane vesicles (mainly including exosomes and microvesicles) secreted by virtually all cell types ,2. Theseet al. monitored tumor-intrinsic and host immune status and predicted melanoma checkpoint blockade outcome (The application of advanced high-throughput RNA sequencing (RNA-seq) techniques has enabled researchers to comprehensively characterize the whole transcriptomic profiles of exLRs in large samples. We have developed an optimal strategy (termed exLR-seq) to extract and sequence exLRs, revealed abundant exLRs in human plasma and identified diverse specific markers potentially useful for cancer diagnosis ,14. By c outcome . ProgresIn 2017, we established exoRBase, a repository of exLRs in human blood exosomes from 92 normal controls and patients with five different cancer types . LncExpDHere, we introduce a completely expanded version, exoRBase 2.0, which contains 905 exLR-seq data of EVs from four types of human biofluids, including blood, urine, CSF and bile. These biofluid samples were collected from healthy individuals as well as patients with 13 types of cancer or other disease. All exLR-seq data were analyzed using an improved bioinformatics pipeline. The annotation information and expression profiles of 19 643 mRNAs, 15 645 lncRNAs and 79 084 circRNAs in EVs were obtained. We also covered the enrichment scores of 11 536 MSigDB (Molecular Signatures Database) pathways for each sample generated by ssGSEA  analyses on exLR expression profiles. In addition, exoRBase 2.0 provides the relative and absolute distribution of 16 types of tissue cells and 23 types of blood cells produced by the modified EV-origin approach. All data and plots in exoRBase 2.0 are freely available for download.http://www.exorbase.org/exoRBaseV2/statistics/index). The latest release  of gene expression TPM profiles (GENCODE version 26) across 30 tissues in the Genotype-Tissue Expression (GTEx) project was also downloaded for annotating possible original tissues of exLRs   , we emplan/ASJA) . Brieflyan/ASJA) . The reman/ASJA) . The reaan/ASJA) . The rea program . Additio program . The finThe basic gene information for all exLRs was annotated with GENCODE version 29. An exLR detected in at least three blood samples would be annotated as blood exLR, and an exLR detected in at least 1 urine  sample was annotated as urine  exLR. The minimal expression threshold of exLRs was 0.1 and circRNAs had at least two reads. Based on the GTEx expression atlas (V8 release), the tissue specificity scores of mRNA and lncRNA genes were calculated as the difference between the maximum possible entropy and the Shannon entropy of expression values for a gene in all tissues ,31. The https://www.gsea-msigdb.org/gsea/msigdb/)  , includimsigdb/) , 292 BIOmsigdb/) , 186 KEGmsigdb/)  and 1554msigdb/)  gene setmsigdb/) , and 189msigdb/) ,39.The estimation analysis of tissue-cellular source contributions of EVs for each sample was performed using EV-origin . This ap2FC (fold change)| > 1 and P-value <0.05 were considered significantly differential targets, and the enrichment results from pathway analyses and EV-origin strategy with p-value < 0.05 were also included in this study. For groups with larger sample sizes , the q-values (adjusted using the adjusted Benjamini–Hochberg method) of differential targets were <0.05. Box plots were employed to exhibit or compare the homogeneous or heterogeneous patterns of exLRs as well as information on pathways and EV origins across all cohorts. In addition, a cumulative percentage chart and rose polar diagram were used to indicate the relative fractions of 16 tissue or 23 blood cell types of EV origins in each exLR-seq sample. All statistical analyses and plots in exoRBase 2.0 were generated using R software (version 4.0.2) and ggplot2 (version 3.3.2) and ComplexHeatmap (version 2.4.3) packages.To evaluate the expression patterns of exLRs in various biofluids among different biological conditions, we calculated the expression frequencies (and corresponding sample numbers) and mean expression values of exLRs in urine, CSF, and bile samples as well as healthy, benign and tumor blood samples. The mean expression values of exLRs in different tumor cohorts were also calculated and further visualized using line and heat map charts. The mean enrichment scores of each pathway and mean absolute proportions of each EV source in all cohorts were also calculated and visualized. To characterize the differential expression or enrichment targets, the Mann–Whitney U test was used to separately perform differential analysis between healthy individuals and each disease/tumor cohort. To avoid the impact of sample imbalance on differential analysis, we randomly sampled 35 healthy samples to compare with groups with relatively fewer samples . ExLRs with |logThe exoRBase 2.0 database was developed based on Akka 2.6.5 (web server) and MySQL (database server). All data were organized and managed by MySQL (version 5.7.31), an open-source relational database management system. The interface of the website was designed and implemented using the Twirl template engine (version 1.5.0). The query system of this database was configured and handled by Play Slick (version 4.0.2). ExoRBase 2.0 can be successfully accessed by different web browsers, including Internet Explorer, Google Chrome, Firefox and Safari.Overall, exoRBase 2.0 focuses on integrating and characterizing the transcriptome data of EVs from human body fluids. A system-level overview of the workflow and kernel data for exoRBase 2.0 is presented in Figure This updated database contains high-quality exLR-seq data of EVs in blood, urine, CSF and bile samples from 905 individuals. Compared with the previous version, urine 16 samples), CSF (5 samples) and bile (17 samples) represent novel biofluid types. Moreover, exoRBase 2.0 houses many more blood samples obtained from fifteen groups, including healthy participants (118 samples), patients with benign disease (130 samples), BRCA (140 samples), CHD (12 samples), CRC (35 samples), ESCC (6 samples), GBM (13 samples), GC (9 samples), HCC (112 samples), KIRC (15 samples), ML (28 samples), MEL (21 samples), OV (30 samples), PAAD (164 samples) and SCLC (36 samples). Through RNA-seq analysis, the annotation information and expression profiles of 19 643 mRNAs, 15 645 lncRNAs and 79 084 circRNAs detected in EVs were obtained and compiled into exoRBase 2.0. In particular, this database has added the enrichment scores of 11 536 MSigDB pathways and the relative and absolute enrichment proportions of 39 tissue-cellular components of circulating EVs for each sample, which are generated based on the exLR expression profiles. Table 6 samplesThe web interfaces of exoRBase 2.0 have been redesigned to make it more accessible and user-friendly. The major user interfaces are shown in Figure In the browse section, there are four web pages for users to browse mRNA, lncRNA, circRNA, Pathway or Tissue/Cell origin Figure . The broIn the newly added two browse pages for Pathway and Tissue/Cell origin, the basic annotation and statistical information of pathways and origins are listed. The ‘Different group’ can be specified to choose significantly differential pathways or EV origins. We also provide the selective browsing of pathways that are recorded as specific ‘MSigDB source’. The 39 EV origins can also be selectively browsed according to the ‘Main category’ or ‘Tissue/Cell type’ annotation information. Clicking the pathway ID or tissue/cell name reveals the detail page for Pathway or Tissue/Cell origin. Users can click the pathway name to view the detailed information in the MSigDB database. We also offer a plot icon for every target to directly connect to the profile graph.In the browse section, entries can be sorted by each column of the table in ascending or descending order. Users can manually set the number of entries displayed per page (default 10) and directly locate entries of interest by a fast, easy search with key words. The whole or filtered browse tables can be downloaded freely.By clicking the ‘Search’ tab on the top navigation menu, users will jump to the search section from any pages Figure . Users c2 normalization with ‘Log2(TPM + 1)’ and ‘Log2(CPM + 1)’ boxes. On the ‘Comparison’ page, users can explore the difference between two groups with the Student's t-test (for groups with few samples) or Wilcoxon test (for groups with many samples). Similarly, the plots and data are also available for download.Clicking the gene symbol, circID, pathway ID or tissue/cell name in both browse and search result tables will link to the corresponding detail section that currently includes ‘Summary’, ‘Profile’ and ‘Comparison’ three pages Figure . For exLUsers can browse and search all exLRs in exoRBase 2.0 and can also mine candidate exLR biomarkers of specific biofluid types or cancer types. For example, the frequently overexpressed liver-specific mRNA genes in HCC blood samples can be screened out with the specified options ’) on the browse page Figure . The greBased on the exLR expression profiles, we further performed MSigDB pathway enrichment analysis and estimated the proportions of potential EV sources using EV-origin approach. These are two important modules of exoRBase 2.0. This improved database now enables users to study the homogeneity and heterogenicity of pathways and EV origins across different groups of biofluid samples. Users can explore desired pathways or EV origins on the browse and search pages in a manner similar to that described for exLRs.ExoRBase 2.0 supports the intuitive visualization not only of the absolute proportions of tissue/cell origins for comparison between different cohorts but also of the relative proportions of tissue/cell origins for comparison between different tissue/cell origins in each sample. By clicking the ‘Origin’ on the navigation bar or the ‘Tissue/Cell origin’ on the home page, the ‘Relative tissue/cell origin proportions’ page is displayed Figure  and B. BIn this work, we introduce the greatly improved exoRBase 2.0, which documents a large set of exLRs expressed in 905 urine, CSF, bile and blood samples from healthy individuals and patients with benign diseases and tumors. This database enables users to query and visualize the comprehensive annotation information and expression landscapes of exLRs, the enrichment scores of MSigDB pathways and the relative and absolute fractions of tissue and blood cell origins of EVs. ExoRBase 2.0 serves not only as a resource for exLRs but also as a platform for assessing the potential functional changes of exLRs and the heterogeneity of tissue-cellular origins of EVs.According to the professional practice recommended by The International Society for Extracellular Vesicles (ISEV), nanometer-sized membrane-bound particles (mainly including exosomes and microvesicles) are uniformly named extracellular vesicles (EVs). We followed this nomenclative principle by using the term EVs instead of exosomes in the updated database. ExoRBase 2.0 features several advantages compared with exoRBase 1.0. First, the new version provides a holistic view of EV transcriptomes in diverse human biofluids. Single exLR expression analysis can be performed among more than ten disease/tumor cohorts. Second, exoRBase 2.0 provides an additional opportunity to conduct concordant and differential pathway analysis for interpreting EV mRNA expression data and gaining insights into biological mechanisms. Third, the predicted abundances of EV origins will provide researchers with cellular-level indicators to evaluate the homogeneity and heterogenicity of different cohorts in physiological or pathological conditions. A few issues however should be noted: (i) The expression levels of most exLRs are comparatively low given the low abundance of EVs in human biofluids. Therefore, the expression frequencies are provided and act as additional parameters for expression evaluation. (ii) Compared with blood samples, the sizes of urine, CSF and bile samples are too small. In the future, more attention will be given to obtain exLR-seq data for such biofluids. (iii) The exLR-seq data of ESCC, GBM and MEL blood samples are produced by different teams with low mapped read counts. Hence, these data are only used to annotate exLRs detected in such types of samples, and the differences between the three groups and the healthy group were not taken into consideration. The sample selection criteria will be further refined to reduce the influence of sequencing quality on downstream analysis.To our knowledge, exoRBase 2.0 remains the only online resource available for exploring different long RNA biotypes of EVs in human normal and cancerous biofluids. Based on these new features, we hope exoRBase 2.0 will be one of the most popular tools to facilitate the identification of novel exLR signatures from human biofluids and to help discover new circulating biomarkers for the improvement of disease diagnosis and therapy. The platform will be updated regularly in the future. We are considering adding exLR data of other biofluids, such as ascites, gastric fluid and saliva. We also encourage researchers to share exLR-seq data of human biofluids in the data submission section to enrich this repository and highlight their discoveries.


Gentiana rigescens from 37 different regions in southwest China were analyzed by high performance liquid chromatography (HPLC). Furthermore, Fourier transform infrared (FT-IR) was adopted to trace the geographical origin  and predict the content of total secoiridoids . In the traditional FT-IR analysis, only one scatter correction technique could be selected from a series of preprocessing candidates to decrease the impact of the light correcting effect. Nevertheless, different scatter correction techniques may carry complementary information so that using the single scatter correction technique is sub-optimal. Hence, the emerging ensemble approach to preprocessing fusion, sequential preprocessing through orthogonalization (SPORT), was carried out to fuse the complementary information linked to different preprocessing methods. The results suggested that, compared with the best results obtained on the scatter correction modeling, SPORT increased the accuracy of the test set by 12.8% in qualitative analysis and decreased the RMSEP by 66.7% in quantitative analysis.Until now, the over-exploitation of wild resources has increased growing concern over the quality of wild medicinal plants. This led to the necessity of developing a rapid method for the evaluation of wild medicinal plants. In this study, the content of total secoiridoids  of  Gentiana rigescens Franch has become one of the 10 most important endangered medicinal plants in Yunnan province since 2002. Furthermore, wild G. rigescens has also been listed as a class III protected species of wild medicinal herbs in the “List of Species of Wild Medicinal Herbs under State Key Protection.”The medicine plant, commonplace throughout human history, is an indispensable part of modern human medicine and traditional medicine . It has G. rigescens, a representative medicine plant, is mainly grown in the Yunnan-Guizhou Plateau (southwest China)  . The rooiridoids . Note thiridoids . Neverthiridoids . FurtherBoletus edulis  is regularly used for both qualitative and quantitative analysis since it has the advantages of being the simplest sampling method, is non-destructive, and has a low analysis cost . New apps edulis , Radix Astragali , and Panoginseng . Neverthoginseng .Sequential preprocessing through orthogonalization (SPORT), a novel ensemble approach to the preprocessing fusion technique, takes inspiration from sequential and orthogonalized partial least-squares (SO-PLS). In fact, SPORT could lead to a boosting procedure by using SO-PLS to fuse several scatter correction techniques, since data processed with different scatter correction methods carry at least partially complementary information , 2021c. Gentiana rigescens. To this aim, the content of total secoiridoids of Gentiana rigescens (37 batches) was determined by high performance liquid chromatography (HPLC). Furthermore, SPORT was carried out to integrate the complementary information linked to FT-IR spectra processed by different preprocessing techniques for tracing the geographical origins  and predicting the content of secoiridoids . With respect to the quality assessment of wild medicinal plants, the approach of using multi-preprocessing integration in this paper not only provides an effective data fusion strategy without any extra instrument/sample information but also means that users do not need to select the best preprocessing methods.The goal of this study was to build a chemical method using multi-preprocessing integration for the qualitative and quantitative assessment of wild G. rigescens samples in the blooming period were collected from Yunnan, Guizhou, and Sichuan provinces (southwest China). Based on the commitment to ensure sustainable utilization, 331 individuals of 35 batch samples were utilized for the qualitative analysis . Among them, 273 individuals were utilized for the qualitative analysis . Note that the sampling locations covered the main distribution areas of G. rigescens . Water was purchased from Wahaha . Reference standards  were provided by the Control of Pharmaceutical and Biological Products . The purity of the reference compound analyzed by HPLC-MS was more than 98%, and the structure is described in In this experiment, the quantitative analysis was performed by an Agilent 1260 Infinity HPLC system equipped with a G13311C diode array detector . In an attempt to acquire the best chromatographic conditions, the detection wavelength, mobile phases, column temperature, and the type of chromatographic column were studied. Finally, the column temperature was maintained at 35? and the chromatographic separation was performed on an Agilent Intersil-C18 column . The mobile phase consisted of 0.1% aqueous formic acid in water (A) and acetonitrile (B), and the following gradient was used: 5% B, for the first 5 min; 5–10% A, for 5–10 min; 10–26% B, for 10–26 min; 26–30% B, for 26–30 min. Column temperature was maintained at 30°C and the detective wavelength was set at 246 nm. The flow rate was kept at 0.3 ml/min and the injection volume was 10 ul. The detection wavelength was set as 241 nm.–1), and resolution (4 cm–1). Each spectrum was scanned in three times, and the average spectra were calculated and used as the final result. Note that background interferences caused by H2O and CO2 should be eliminated before the scanning of the blank KBr.In this experiment, FT-IR spectra were obtained by a FT-IR Spectrometer  equipped with a deuterate triglycine sulfate detector. For each sample, 1.5 ± 0.2 mg of powder was mixed with spectrometry grade KBr (100 mg) in the agate mortar. The parameters of spectra acquirement were 32 co-added scans, scanning range  algorithm was carried out to generate synchronous 2DCOS images for identifying the overlapping peaks more effectively by imposing external disturbance on the samples. The flowchart of the whole conversion process is described in V1, V2) between V1 and V2 are expressed as:Hence, the synchronous two-dimensional correlation intensity Φ  , standar2p) and root mean square error of validation (RMSEP), were calculated to estimate the PLSR model’s performance. The PLS-DA and PLSR were performed by SIMCA-P+  software.In the present study, two multivariate analysis methods, including PLS-DA and PLSR, were carried out. PLS-DA, a widely applied discrimination algorithm, divided the multi-dimensional space into class-regions, hence, the under tested samples were assigned to one specific category. More detailed information can be found in Sequential preprocessing through orthogonalization, an emerging ensemble approach to preprocessing fusion, was developed from sequential and orthogonalized partial least-squares (SO-PLS) modeling . It taker2 > 0.9991) for three standard solutions . Note that before sample determination, the reasonability of the quantitative analysis approach was tested by calculating the revealed standard deviations (RSDs) of precision, repeatability, stability, and recovery. Information detailing this is shown in G. rigescens collected from different regions, the coefficient of variation was calculated. The coefficient of variation was identical to 25.2%, which indicated that the quality of G. rigescens was greatly varied. Hence, the further qualitative and quantitative assessment of the wild G. rigescens is of great significance for practical application in routine life.In this experiment, the external standard approach was performed to quantify the content of total secoiridoids . All standard solutions were diluted into different concentrations using methanol in an attempt to establish the regression equation. Finally, a good linear relationship was achieved r > 0.9991–1), which represented the different vibrational models of functional groups. In an attempt to gain insight into the spectral characteristics of G. rigescens, they were interpreted as follows: (i) the first overtone O-H stretching resulted in a peak at 3,400 cm–1, (ii) methylene asymmetry caused by esters can be seen at around 2,928 and 2,857 cm–1; (iii) a band at 1,615 cm–1 resulted in the asymmetric stretch of the C-C bond; (iv) a peak at 1,427 cm–1 was assigned to the asymmetric bending vibration of -CH3; (v) a peak at 1,375 cm–1 represented the bending vibration of -CH3 caused by esters, and (vi) peaks at 1,075 cm–1 were attributed to C-OH or C-O stretching  and 91 samples (test set) by the Kennard-Stone (KS) algorithm. After that, five PLS-DA models were established based on the raw FT-IR spectra and four processed FT-IR spectra, and 7-fold cross-validation was carried out to determine the optimal number of LVs. Subsequently, SPORT was carried out to integrate the complement information linked to different preprocessing techniques for the qualitative assessment of wild he model . Hence, The parameters, classification results, and confusion matrixes of the PLS-DA models are depicted in G. rigescens. The fusion order of the scatter correction methods and the selection of an optical number of LVs were consistent with the qualitative analysis SPORT model, and the selected result is shown in In an attempt to develop a reliable calibration model, the 273 samples were divided into 182  samples and 91 (test set) samples using the KS algorithm. The FT-IR spectra distribution of the calibration set and test set is displayed in 2p by 7.9% and decreased the RMSEP by 44.8%. On this basis, the results also indicated that SPORT, compared with the SD approach, significantly improved the predictive performance, especially in terms of the residual error -based ensemble, in which all the possible combinations of preprocessing are utilized and the equality PLS models are calculated to select the optimal preprocessing combination, as in a report involving corn . NeverthPolygonatum kingianum  and mid-infrared (MIR) spectroscopies, for the discrimination of the wild G. rigescens and its related species. The second one by Gentiana rigescens. Although higher precision results were obtained in the above analysis, the increase in the amount of the instrument increases, and the cost will inevitably increase, resulting in an unsuitable application in routine life. In the present study, SPORT was carried out to significantly improve the performance of both qualitative and quantitative analysis models through integrating the complementary information linked to different preprocessing techniques. The results of this study indicated that SPORT could be defined as a possible alternative solution to a multi-platform integration strategy without any extra instrument/sample information. It is our expectation that SPORT can provide a low-cost strategy for the quality assessment of wild G. rigescens, and provide a certain reference value for further protection, development, and utilization of wild medicinal plants.Data fusion, including low-level data fusion, mid-level data fusion, and high-level data fusion, was presented as a promising strategy for the quality assessment of medicinal plants, such as in reports involving ingianum , Amomum  tsao-ko , and Euculmoides . Indeed,G. rigescens using multi-preprocessing integration was demonstrated by the standard PLS technique and SPORT technique. What we used is a reliable method with wide applicability, which can’t only be used as a possible solution to effectively fuse data without any extra instrument/sample information but also allows the selection of the best preprocessing method without user input. Hence, with respect to the qualitative and quantitative analysis of medicinal plants, the application of SPORT in FT-IR modeling is recommended, furthermore, the SPORT approach was not limited to FT-IR data, but it could be utilized to integrate multiple preprocessing techniques with any spectroscopic data. We expect that SPORT can provide a low-cost strategy for the quality assessment of wild G. rigescens, and provide a certain reference value for the further protection, development, and utilization of wild medicinal plants.In this study, the feasibility of qualitative and quantitative assessment of wild The original contributions presented in the study are included in the article/ZLiu contributed to conceptualization, writing – original draft, and data curation. TS contributed to investigation, methodology, resources, and software. JiZ contributed to investigation, resources, and formal analysis. ZLi contributed to investigation and resources. YZ contributed to resources and supervision. ZZ contributed to validation and visualization. JinZ and YW contributed to funding acquisition and project administration. All authors have read and approved the final manuscript.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.


Artificial intelligence, or the discipline of developing computational algorithms able to perform tasks that requires human intelligence, offers the opportunity to improve our idea and delivery of precision medicine. Here, we provide an overview of artificial intelligence approaches for the analysis of large-scale RNA-sequencing datasets in cancer. We present the major solutions to disentangle inter- and intra-tumor heterogeneity of transcriptome profiles for an effective improvement of patient management. We outline the contributions of learning algorithms to the needs of cancer genomics, from identifying rare cancer subtypes to personalizing therapeutic treatments. Artificial intelligence (AI) is becoming a fundamental asset for healthcare and life science research. Despite being in its infancy, research activities employing AI are changing our understanding and vision of science. The European Commission has recently estimated that 13% of global venture capital investments  are for start-ups dedicated to AI application in medicine . ConsequThe need for AI models is even more evident in complex diseases such as cancer. The heterogeneity that characterizes Big Data is amplified in cancer, where diversity not only manifests itself across individuals  but also within each tumor  . So far,Transcriptomics generally refers to the high-throughput profiling of all RNA species produced by cells. Among genomic Big Data, transcriptomics has seen an explosive growth in recent years . RNA seqIn this paper, we provide an overview of AI approaches applied to high-volume bulk and single-cell RNA-seq in cancer genomics and precision oncology. We do not intend to provide a comprehensive characterization of all published AI methods and their technical details. By contrast, we illustrate the major AI solutions to disentangle the heterogeneity of cancer transcriptomes for an effective improvement of patient management. We explain distinct strategies to face the “heterogeneity challenge”. We then outline some of the major contributions of applying AI to the needs of cancer genomics, from identifying rare cancer subtypes to personalizing treatment for individuals.From the first drafts of the human genome , 20 yearSo far, genomic screenings have made available more than 106,585 RNA-seq samples  and thisThe availability of these data seizes the opportunity to boost the development of novel diagnostic tools and targeted treatments. Indeed, the implementation of AI models has increased in the last 10 years, as machine-learning  (ML) andIn cancer transcriptomics, ML and DL models have been applied to classify different cancer subtypes and cell populations ,18,19,20Disentangling Big Data heterogeneity is the major challenge that researchers have to face to gain novel scientific insights. When learning from real transcriptomic data, the heterogeneity increases due to the variable expression of genes across samples driven by genetic, environmental, demographic, and technical factors . In cancHowever, for an effective application of AI algorithms to cancer transcriptomics, the availability of highly-curated datasets is fundamental . DefininFinally, to boost the application of AI in precision oncology, it is of primary importance to provide the scientific community with the code and data behind the AI model. This practice is fundamental to ensure the reproducibility and transparency of results . NowadayHeterogeneity of cancer transcriptomes can arise from both technical and biological confounders . SeveralTechnical heterogeneity rises during the experimental generation of sequencing data. In particular, cancer transcriptomes can be profiled (i) from distinct sample types; (ii) using different protocols and platforms; and (iii) processed in unconnected laboratories by specific users in separate times. This “batch-specific” heterogeneity confounds the real biological signal of large-scale datasets . TherefoHeterogeneity emerges from the high dimensionality of transcriptomic datasets, which profile thousands of RNA isoforms. These profiles result in lists of fixed-length vectors of real values, namely features, which are highly variable in a specific range. The extremely high number of heterogeneous features prevents direct identification of biological similarities across samples under a phenotype of interest. In this view, dimensionality reduction is a useful pre-processing approach to remove confounders, speed up learning methods and improve their accuracy in detecting similarities . These tPrincipal Component Analysis (PCA) is conventionally used for dimensionality reduction and exploratory analyses of transcriptomic profiles . PCA aimBeing a linear approach, the accuracy of PCA is limited when dealing with large-scale RNA-seq datasets . To overFeature extraction can also be performed employing DL approaches. For instance, convolutional NNs can automatically reduce data dimensionality and perform classification tasks. Convolutional NN methods have been successfully implemented in digital pathology. Interestingly, these algorithms have been capable of inferring cancer transcriptomic profiles from histological images . AppliedFeature selection (FS) aims at identifying the most important attributes from heterogeneous high-dimensional data . The choDespite being pivotal for increasing the accuracy of AI predictions, the presence of extensive correlations between variables in large-scale datasets could reduce the stability of selected features . To mitiSince AI methods learn from inputs to predict outputs, the different scale and distribution of features in the training data can impact on model performance, particularly if the algorithm is based on distance measures . For insSimilarly, data discretization is a preprocessing step through which values are divided in a finite number of classes. AI methods, such as classification and clustering, can improve in learning speed and accuracy by discretizing the distribution of numerical input values ,103. In Features that have many zero values are commonly referred to as sparse, and their presence can lead to overfitting and reduced performances in AI models. The presence of sparse features characterizes single-cell RNA-seq datasets due to experimental limitations ,106. DatCancer manifests its genetic heterogeneity with the presence of distinct histological subtypes and tumor microenvironment (TME) compositions across tumors and within the same disease. Inter- and intra-tumor heterogeneity have different clinical implications, making their accurate identification pivotal for therapeutic decisions . As prevOne of the most used AI approaches to assess inter-tumor heterogeneity and improve the identification of distinct molecular subtypes using transcriptomic data is unsupervised learning clustering. This technique aims at identifying groups of samples with similar biological features by partitioning data according to similarity measures . DiffereThe use of prior information can be a useful solution to train AI models more effectively . For thiHowever, the heterogeneous composition of large-scale datasets can lead to AI models that are biased toward specific subtypes, thus impacting patient management . A NaïveDL approaches have shown advantages over supervised ML methods for their ability of automatically extracting features from input data . An autoTranscriptomic profiling of single cells has allowed direct access to intra-tumor heterogeneity through the identification of cell types and clones composing the tumor mass. Learning clustering represents the commonest approach to identify gene signatures representative of specific cell types ,22,68,69The heterogeneous composition of the tumor mass increases the complexity of cancer transcriptomes, making the systematic characterization of TME fundamental for the development of personalized therapies. The fine quantification of tumor-infiltrating immune cells can help to guide the selection and understand the effect of immunotherapeutic approaches. For these reasons, ML methods have been proposed to deconvolute cell-type abundance from bulk transcriptomic profiles of mixed populations. Among others, a ML approach based on non-negative matrix factorization has shown to accurately define cell-type-specific expression signatures exploiting tissue heterogeneity in more than 2300 cancer transcriptomes . In abseThe application of learning approaches to single-cell transcriptomic profiles of physically isolated cells has improved the characterization of TME composition and interactions. PCA dimensionality reduction followed by joint embedding and clustering approach elucidated the cellular composition of osteosarcoma, showing that TME-based chemotherapy may reduce osteoclast differentiation to osteosarcoma . UMAP-baFinally, the combination of bulk and single-cell transcriptome profiling of tumors can improve the characterization of TME and the selection of personalized therapeutic treatments. For instance, supervised clustering approach of 2269 bulk and 10,434 single-cell colorectal transcriptomes identified a TME-associated chemotherapy resistant gene signature enabling tumor subtyping with potential therapeutic response .Overall, deconvolution AI algorithms represent a powerful tool for improving our understanding of TME composition and the delivery of personalized medicine.To deliver an effective personalized medicine, the precise identification of patient-specific genetic markers that drive the disease is fundamental. To foster the discovery of novel cancer vulnerabilities, ML and DL have been applied to large-scale transcriptomic profiles and, often, integrated with pharmacogenomics  data. These approaches commonly employ protein-protein interaction network-based feature selection analyses to identify gene signatures associated with drug response, which are then used to train ML classifiers. Recently, an example of these ML frameworks based on ridge regressions has been shown to accurately identify gene signature associated with drug response of colorectal and bladder cancer patients . A similThe integration of multiple AI techniques can be a handy solution to improve the identification of cancer vulnerabilities. Recently, a combination of three learning algorithms resulted in identifying histone deacetylase inhibitors as potential therapeutic targets for multiple soft tissue sarcomas . In partThe analysis of single-cell transcriptomic data can enhance the detection of gene signatures that can discriminate between somatic cells and the other cell types composing the tumor mass. Recently, the combination of dimensionality reduction performed by projected matrix decomposition and clustering through non-negative matrix factorization identified gene signatures of healthy brain cells . AppliedStratification of patients into groups with different survival probabilities using prognostic biomarkers is pivotal to prioritize treatments and avoid unnecessary therapies . TraditiTo address the scarcity of training samples available for specific cancer types, Cox-PH NN generalizations can be exploited using transfer learning approaches . TransfeTogether, these results show that NN extensions of Cox-PH modeling improve the identification of prognostic gene signature responsible for cancer progression, and thus of putative novel biomarkers.Neoepitopes are tumor-specific peptides that are presented by antigen-presenting cells through the major histocompatibility complex and recognized by the immune system . SeveralNNs have been developed to improve peptide-prediction accuracy and MHC-ligand identification  from somDespite the results achieved so far, the application of AI to cancer transcriptome Big Data for valuable precision oncology is still limited. The complexity of cancer heterogeneity remains the major challenge to disentangle. On the one hand, AI represents the most powerful tool to extract the real biological information from large-scale transcriptomic datasets. As national and international sequencing consortia generate sequencing data, the ability of DL algorithms to capture the hidden relationships responsible for a phenotype without requiring a human supervision will become pivotal for our understanding of diseases and guide personalized therapeutic interventions. On the other hand, AI data mining poses several challenges. Harnessing Big Data carries with it the ‘curse of dimensionality’ phenomenon, or the need of more data when information increases ,126. WheThe demand of AI in precision oncology will go hand in hand with the need of doctors and experts that will be able to translate results into real precision therapeutic decisions and participate actively in the development of learning strategies. In this light, a precision AI-driven oncology will become effectively available on demand.


Macrohyporia cocos is an edible traditional Chinese medicine with high economic value. Due to the significant difference in quality between wild and cultivated M. cocos, this study aimed to trace the origin of the fungus from the perspectives of wild and cultivation. In addition, there were quite limited studies about data fusion, a potential strategy, employed and discussed in the geographical traceability of M. cocos. Therefore, we traced the origin of M. cocos from the perspectives of wild and cultivation using multiple data fusion approaches. Supervised pattern recognition techniques, like partial least squares discriminant analysis (PLS-DA) and random forest, were employed in this study using. Five types of data fusion involving low-, mid-, and high-level data fusion strategies were performed. Two feature extraction approaches including the selecting variables by a random forest-based method—Boruta algorithm and producing principal components by the dimension reduction technique of principal component analysis—were considered in data fusion. The results indicate the following: (1) The difference between wild and cultivated samples did exist in terms of the content analysis of vital chemical components and fingerprint analysis. (2) Wild samples need data fusion to realize the origin traceability, and the accuracy of the validation set was 95.24%. (3) Boruta outperformed principal component analysis (PCA) in feature extraction. (4) The mid-level Boruta PLS-DA model took full advantage of information synergy and showed the best performance. This study proved that both geographical traceability and optimal identification methods of cultivated and wild samples were different, and data fusion was a potential technique in the geographical identification.Poria originated from the dried sclerotium of  Macrohyporia cocos is a wood-decay fungus in the Polyporaceae family. It transforms the wood of pine trees into medicinal products, which could treat various edemas, invigorate the spleen function, and calm the mind. The sclerotium of M. cocos, called Poria, is one of the most widely used raw materials of Chinese herbal compound preparations. The Chinese Pharmacopoeia (version 2015) records over one hundred types of prescriptions including Poria. Moreover, the National Health Commission of the People's Republic of China has approved that this fungus could be used for food. Plenty of Poria-based skin cosmetics like facial masks have been produced and used. Present investigation shows that this fungus displays not only anticancer [ version 015 recorticancer , 4, antiticancer , and hepticancer  propertiGeographical traceability has always been a significant issue for quality assurance, not only for natural medicine but also for food. Many laws have been legislated for protecting part of geographical origins, such as China Protected Geographical Indication Products and European Protected Designation of Origin . It is tM. cocos by liquid chromatography and infrared spectroscopy combined with data fusion, and the results witnessed that the performance of low-level data fusion strategy preceded that of the single techniques. Li and Wang [M. cocos raw materials using ultraviolet (UV) and Fourier transform infrared (FTIR) spectroscopy data fusion, and it was found that the differences in growth patterns were larger than those in collection regions, whereas only a few types of data fusion methods were studied and discussed. Additionally, growth patterns have influences on the chemical composition of both inner part and epidermis of the species [M. cocos from the perspectives of wild and cultivation using multiple data fusion methods.By means of combining the outputs of multiple complementary information regarding objects to exploit the synergies of information, data fusion strategies have more opportunities to achieve an accurate characterization than single pieces of data , which hand Wang  performe species , and traFive types of data fusion methods involving low-, mid-, and high-level data fusion strategies were studied: one low-level, two mid-level, and two high-level. Each level fusion had its advantages. Low-level fusion was characterized by its easy implementation. Mid-level data fusion could save the computation time because its step of feature extraction significantly reduced the data dimensionality . The merAccording to our previous experiences regarding origin identification of cultivated Poria, the inner part with larger output was more efficient than the epidermis, and liquid chromatograms at 242 nm and FTIR spectra outperformed other techniques . TherefoIn this study, five data fusion methods combined with two multivariate classification approaches of partial least squares discriminant analysis (PLS-DA) and random forest (RF) have been applied to take advantage of the synergistic effect of the information obtained from FTIR and LC. In particular, two feature extraction approaches including the selecting variables by a RF-based method—Boruta algorithm and producing principal components (PCs) by the dimension reduction technique of principal component analysis (PCA)—were considered in data fusion. The quality, geographical traceability, and optimal identification methods of cultivated and wild samples were compared. The results of this study may improve the current knowledge and pave the way for further development and utilization of this fungus.−1): dehydrotumulosic acid: 5.00–999; poricoic acid A: 0.22–6730; 3-epidehydrotumulosic acid: 1–100; dehydropachymic acid: 2.4–480; pachymic acid: 10.3–1240; and dehydrotrametenolic acid: 0.49–2450.HPLC-grade acetonitrile was purchased from Thermo Fisher Scientific . Formic acid was purchased from Dikma Technologies . Purified water was purchased from Guangzhou Watsons Food & Beverage Co., Ltd. . Other chemicals and reagents were of analytical grade. The standard compounds  (purity ≥ 98%) were supplied by Beijing Keliang Technology Co., Ltd. . Dehydrotumulosic acid (purity ≥ 96%) was purchased from ANPEL Laboratory Technologies Inc. . The concentration ranges of standard solutions prepared for each analyte were the following (mg·LMacrohyporia cocos (Schwein.) I. Johans. & Ryvarden by Professor Yuanzhong Wang . For fresh sclerotium, the attached soil was brushed away and washed by tap water. Then, the samples were air-dried in the shade with good ventilation. The dark epidermis was removed, and white inner part was powdered afterwards for analysis. The powder was screened with 60-mesh sieve. All samples were preserved in polyethylene resealable bags for further analysis.Both wild and cultivated samples (123) were collected from Yuxi, Pu'er, Dali, Chuxiong, and Baoshan of Yunnan Province, China. The detailed information was showed in μm membrane filter. The filtrates collected in auto sampler were injected into the LC system for analysis. On the other hand, sample powder was used directly for attenuated total reflectance FTIR spectra acquisition.Then, on the one hand, accurately weighed powder (0.5000 ± 0.0001 g) was ultrasonically dissolved in 2.0 mL methanol for 40 min. The extract was filtered through a 0.22 μm) operated at 40°C. The mobile phase consisted of acetonitrile (A) and 0.05% formic acid (B). The flow rate was kept at 0.4 mL·min−1, and the injection volume was set at 7 μL. The signals were acquired at 242 and 210 nm. Before use, the mobile phase constituents were degassed by ultrasonication and filtered through a 0.2 μm filter. The samples were eluted with the following gradient: 40% A (0.00 min⟶25.00 min), 40%⟶69% A (25.00 min⟶52.00 min), 69%⟶72% A (52.00 min⟶56.00 min), 72%⟶78% A (56.00 min⟶58.00 min), 78%⟶90% A (58.00 min⟶58.01 min), and 90% A (58.01 min⟶60 min). Each run was followed by an equilibration period of three minutes with initial conditions (40% A and 60% B).LC analyses were performed with an ultra-fast liquid chromatography system  equipped with a UV detector, a thermostatic column compartment, an autosampler, a degasser, and binary gradient pumps. The separation was carried out on an Inertsil ODS-HL HP column  and humidity (30%) condition.A FTIR spectrometer  equipped with deuterated triglycine sulfate (DTGS) detector and attenuated total reflectance (ATR) sampling accessory was used to record FTIR spectra. The resolution and scan range were set as 4 cm−1 and 2670–1750 cm−1 had noise; therefore, the variables in both ranges were abandoned. Because chromatograms and spectra contained overlapped peaks and baseline shifts, Savitzky–Golay (SG) polynomial second-derivative filter  was carried out to highlight slight differences and eliminate the interference of baseline drift. Particularly, the deletion of spectral variable was performed after SG polynomial second-derivative preprocessing.The retention time of chromatograms would be affected by time and other factors. For this reason, the correlation-optimized warping algorithm  was usedm × n) was built to describe the change in variable numbers, where m represented the number of samples and n represented the retention time of chromatogram or the wavenumber of spectrum. Take wild samples for example; the initial chromatographic matrix (61 × 7201) was transformed as (61 × 2387) after pretreatment, and the raw spectral matrix (61 × 1737) was changed as (61 × 1097). The processed data matrixes were then used for PLS-DA, random forest or data fusion.The size of data matrix  was a wildly used linear classification method combining the properties of partial least squares regression with classification technique , 28. As tree) and mtry were chosen depending on OOB classification error. The operational steps were roughly divided into the following four steps. Firstly, a dataset processed by Kennard–Stone algorithm [tree depending on the lower OOB classification error values of total classes considering each class at the same time, and the initial value of ntree was tested with 2000. Thirdly, the optimal mtry was searched in the range of default value of mtry (square root of the number of variables) plus minus 10 with step by step [mtry's with lowest OOB classification error, the one that was closer to the default value came first. Finally, the RF model was built by using the selected ntree and mtry.Random forest (RF) was a method of ensemble learning based on decision of classification or regression trees , 31. Whelgorithm  was impo by step . If therFor assessing the performance of model, the calibration and validation sets were divided at the ratio of 2 : 1 employing Kennard–Stone algorithm. The calibration set was applied to build a model and the validation set was employed to obtain an estimate of the model practicability from an external perspective. In general, if the performance of calibration set is far higher than that of validation set, it shows the possibility of overfitting, that is, diminishing generalization ability of model, which should be avoided.Additionally, the efficiency and total accuracy rate were as synthetic parameters to evaluate the classification performance. The higher were the values of these parameters, the better was the model performance. The equation of efficiency was shown as follows , where T+  was used for PCA, PLS-DA, and SG polynomial second-derivative preprocessing. The random forest and Boruta were unfolded using R package (version 3.4.3). The correlation optimized warping and Kennard–Stone algorithms were performed by MATLAB software . Contents of five target compounds were statistically analyzed by one-way analysis of variance at P < 0.05 using SPSS software .SIMACA-PM. cocos . Accordingly, the cultivated and the wild samples should be performed for origin identification separately.The PLS-DA was performed using wild and cultivated samples as class ID. From the scores scatter plots of two dimensions regarding all of samples Figures , it coulM. cocos differs significantly in the same area. The content of dehydrotumulosic acid, poricoic acid A, and dehydropachymic acid of cultivated M. cocos was higher than that of wild samples, and the content of pachymic acid and dehydrotrametenolic acid of wild M. cocos was higher than that of cultivated samples in Pu'er area. In general, the quality of cultivated M. cocos samples from Chuxiong, Dali, and Baoshan is slightly higher than that of wild samples. The quality of cultivated M. cocos in Baoshan is the best, which is suitable for large-scale planting. Yuxi may be the geographic source for screening excellent wild M. cocos germplasm resources.In addition, the content of terpenoids in the wild and cultivated M. cocos. The contents of five compounds were presented as the box-plots with medians (lines in the boxes). For wild fungal samples (P < 0.05). Baoshan (BS) possessed higher content of dehydropachymic acid than the remaining collection locations and greater pachymic acid content than Chuxiong (CX). Compared with DL and BS, Yuxi (YX) had higher concentration of dehydrotrametenolic acid. Pu'er (PE) was significantly different from DL in the concentration of dehydrotrametenolic acid. The cultivated samples from BS were significantly different from those from the other geographical origins in terms of the contents of dehydrotumulosic acid, poricoic acid A, and dehydropachymic acid. Furthermore, for cultivated fungi, both CX and YX were significantly different from DL and PE in dehydrotumulosic acid, DL in dehydropachymic acid, BS, and DL and PE in pachymic acid. These quantitative results of five bioactive analytes gave a valuable reference for differentiating samples derived from different geographical regions and for evaluating the quality of M. cocos.These triterpenes showed plenty of bioactivities, and its presence and quantity had a vital influence on the health effect of  samples , Dali . As described in independent decision making, the optimal PLS-DA and RF models were set up using low-level merged data using suitable parameters . It coultree and mtry screening of the random forest models of mid-level-PCA and mid-level Boruta are displayed in In mid-level data fusion, the selected variables by Boruta from LC and FTIR data  intersected the vertical axis at or below zero; it suggested that the model was not overfitting. The results showed that there was no overfitting in the PLS-DA models.Because the accuracy rates of calibration set in PLS-DA models were usually much higher than those of validation set, all of PLS-DA models were validated by permutations test to assess the risk that the current PLS-DA model was spurious. A 30-iteration permutation test was carried out. As could be seen from M. cocos). What is more, it showed that it was of concern to trace the origin of M. cocos from the perspectives of wild and cultivation using multiple data fusion methods. It is worth going to try more data fusion approaches for approximating to an accurate characterization.Under the same condition, comparing the results of cultivated and wild samples geographical identification, it could be found that they showed difference in optimal identification method. For the cultivated ones, it was low-level fusion that preceded mid-level PCA and independent decision making. However, for the wild samples, the performance of individual data and mid-level PCA were better than that of low-level data fusion. And mid-level Boruta of this study was more suitable for the origin identification of wild samples than mid-level PCA. Accordingly, the reason why the mid-level model had better results than the low- and high-level fusion models and independent decision making might be the characters of samples, the feature extraction methods of data fusion, and the final decision methods of high-level data fusion (final decision methods maybe had an influence in the result of high-level data fusion of wild M. cocos samples was performed using multiple data fusion methods from the perspectives of wild and cultivation. Low-, mid-level Boruta, mid-level-PCA, high-level Boruta, and high-level PCA data fusion strategies were investigated. Two feature extraction approaches including the selecting variables by Boruta algorithm and producing PCs by the PCA dimension reduction technique were considered in data fusion. The results showed that the geographical traceability of the cultivated was superior to that of the wild. The Baoshan area is suitable for planting M. cocos on large noodles.The Yuxi can screen high-quality wild M. cocos germplasm resources. The cultivated samples from different collection regions could be easily identified only by FTIR or LC data, while the wild ones could not. In the origin identification of wild samples, Boruta did better than PCA in feature extraction. The PLS-DA and RF models of mid-level Boruta were able to well characterize M. cocos and provided a more efficient classification than those of mid-level PCA, low-, high-level PCA, and high-level Boruta data fusion strategies as well as independent decision making, in which the mid-level Boruta PLS-DA model was deemed as the most satisfactory. The mid-level Boruta PLS-DA model provides a reliable method for the identification of the geographical origin of M. cocos.In this study, the geographical traceability of M. cocos.In short, the quality, geographical traceability, and optimal identification methods of cultivated and wild samples were different, and this study also showed the potential of data fusion strategies in the geographical identification of


Transcriptionally informed predictions are increasingly important for sub-typing cancer patients, understanding underlying biology and to inform novel treatment strategies. For instance, colorectal cancers (CRCs) can be classified into four CRC consensus molecular subgroups (CMS) or five intrinsic (CRIS) sub-types that have prognostic and predictive value. Breast cancer (BRCA) has five PAM50 molecular subgroups with similar value, and the OncotypeDX test provides transcriptomic based clinically actionable treatment-risk stratification. However, assigning samples to these subtypes and other transcriptionally inferred predictions is time consuming and requires significant bioinformatics experience. There is no "universal" method of using data from diverse assay/sequencing platforms to provide subgroup classification using the established classifier sets of genes , nor one which in provides additional useful functional annotations such as cellular composition, single-sample Gene Set Enrichment Analysis, or prediction of transcription factor activity.To address this bottleneck, we developed classifieR, an easy-to-use R-Shiny based web application that supports flexible rapid single sample annotation of transcriptional profiles derived from cancer patient samples form diverse platforms. We demonstrate the utility of the " classifieR" framework to applications focused on the analysis of transcriptional profiles from colorectal (classifieRc) and breast (classifieRb). Samples are annotated with disease relevant transcriptional subgroups (CMS/CRIS sub-types in classifieRc and PAM50/inferred OncotypeDX in classifieRb), estimation of cellular composition using MCP-counter and xCell, single-sample Gene Set Enrichment Analysis (ssGSEA) and transcription factor activity predictions with Discriminant Regulon Expression Analysis (DoRothEA).https://generatr.qub.ac.uk/app/classifieRc and https://generatr.qub.ac.uk/app/classifieRb after signing up for an account on https://generatr.qub.ac.uk.classifieR provides a framework which enables labs without access to a dedicated bioinformation can get information on the molecular makeup of their samples, providing an insight into patient prognosis, druggability and also as a tool for analysis and discovery. Applications are hosted online at The online version contains supplementary material available at 10.1186/s12859-022-04641-x. Next generation sequencing (NGS) is leading the drive towards personalised precision cancer medicine. Single sample annotation of cancer patient samples, the process of annotating each sample individually with transcriptionally inferred sub-types and functional predictions are increasingly important to understand disease aetiology and underlying biological mechanisms. Within CRC the best-established sub-grouping methodology to date is the consensus molecular subgroups CMS1-4) [-4 [1], wC, which in its current version (1.0) enables intuitive click-of-a-button annotation of CRC transcriptomic datasets. This analysis includes transcriptomic subtyping through CRIS and CMS and its derivative CMScaller sub-types (CMS adapted for PDX models and cell lines) to help summarise patient biology. An assessment of cellular content using MCP-counter and xCell which can be used to detect which cells are present in the tumour microenvironment (TME). Prediction of transcription factor activity scores with DoRothEA, and providing Single Sample Geneset Enrichment (ssGSEA) analysis . This additionally enables interactive boxplot visualisation of the values generated by MCP-counter and DoRoTheA TF-activity and how these assessments segregate within the CRIS and CMS molecular subgroups as exemplified in Fig. http://generatr.qub.ac.uk/classifieRc and http://generatr.qub.ac.uk/classifieRb, which runs an example analyses on data from GSE103479 [The application has been configured, hosted and launched on a hosted Virtual Machine and can be easily accessed using any operating system  cut-off can be adjusted; a detailed explanation on how to choose the optimal cutoff values for this step is available in the ‘Introduction’ page of the application. If the user is using cell line data, the more cell line-specific CMScaller  package Each processing step within the application has been carefully optimized to reduce computational processing power and memory usage, cutting down the length of time required to run these analyses when compared to running it outside of classifieR with 46% less memory usage overall and 78% faster to run a complete analysis of 156 samples.http://generatr.qub.ac.uk/Currently, in order to perform the equivalent analyses, it requires an in-depth understanding of the R programming language and how to integrate multiple tools and outputs. classifieR enables labs without access to a dedicated bioinformation to understand what their data reveals functionally, how to interpret their results and to obtain information on the molecular makeup of their samples, providing an insight into patient prognosis and reponse to therapy. classifieR permits broader accessibility to tools that are currently only available through bioinformaticians and provides a faster and simultaneous analysis than through using the individual tools alone. Both applications are freely available to use at Project name: classifieR.https://generatr.qub.ac.uk/Project home page: Operating system(s): Platform Agnostic.Programming language: R version 4.0.3 (2020–10-10) / Java / HTML.Other requirements: Web browser.License: NA (Web server).Any restrictions to use by non-academics: Research use only.Additional file 1: Supplementary figures. Supplementary 1. Screenshots of the classifieR application. (A) Screenshot of main page, showing where the file is selected, what technology is used and a submission button. (B) Progress bar showing progress of the app’s stratification. (C) An example of the advanced settings that can be selected when running the analysis (D) This page of the app Includes a table with all classification data, and box plots which integrate the classifiers (CRIS/CMS) with immune population and transcription factor activity. (E) This page has a detailed table and interactive heatmap (not shown), an interactive bar chart showing transcription factor activity, a histogram of activity across all samples and a correlation plot across all samples in which the transcription factor of interest can be selected. Supplementary 2. Sample number can confound transcriptional subtyping of GSE103479. (A) Schematic of experimental plan to test number of samples for robust CRIS/CMS classification (B) Sample discordance between subsets of samples against all samples ran simultaneously in GSE103479.


Glioma is a classical type of primary brain tumors that is most common seen in adults, and its high heterogeneity used to be a reference standard for subgroup classification. Glioma has been diagnosed based on histopathology, grade, and molecular markers including IDH mutation, chromosome 1p/19q loss, and H3K27M mutation. This subgroup classification cannot fully meet the current needs of clinicians and researchers. We, therefore, present a new subgroup classification for glioma based on the expression levels of Gβ and Gγ genes to complement studies on glioma and Gβγ subunits, and to support clinicians to assess a patient’s tumor status.Glioma samples retrieved from the CGGA database and the TCGA database. We clustered the gliomas into different groups by using expression values of Gβ and Gγ genes extracted from RNA sequencing data. The Kaplan–Meier method with a two-sided log-rank test was adopted to compare the OS of the patients between GNB2 group and non-GNB2 group. Univariate Cox regression analysis was referred to in order to investigate the prognostic role of each Gβ and Gγ genes. KEGG and ssGSEA analysis were applied to identify highly activated pathways. The “estimate” package, “GSVA” package, and the online analytical tools CIBERSORTx were employed to evaluate immune cell infiltration in glioma samples.Three subgroups were identified. Each subgroup had its own specific pathway activation pattern and other biological characteristics. High M2 cell infiltration was observed in the GNB2 subgroup. Different subgroups displayed different sensitivities to chemotherapeutics. GNB2 subgroup predicted poor survival in patients with gliomas, especially in patients with LGG with mutation IDH and non-codeleted 1p19q.The subgroup classification we proposed has great application value. It can be used to select chemotherapeutic drugs and the prognosis of patients with target gliomas. The unique relationships between subgroups and tumor-related pathways are worthy of further investigation to identify therapeutic Gβγ heterodimer targets. Glioma is a classical type of primary brain tumors that is most common seen in adults, and its high heterogeneity used to be a primitive feature for subgroup classification . Histori+ Channels, Phosphoinositide 3 Kinases, Mitogen-Activated Protein Kinases, and is also involved in microtubule polymerization, recycling endosomes, and Golgi fragmentation  and 12 γ-subunits  in the human body. βγ pairs are specifically related to downstream signals . Gβ1γ2 hhttp://www.cgga.org.cn) and 672 glioma samples retrieved from TCGA database (http://cancergenome.nih.gov/) were utilized in this study for reference. Relevant data included relapse samples. For the same patient, we only used the first tumor RNA sequencing data. The FPKM-standardized mRNA sequencing data was log2 transformed for all analyses. The count format mRNA sequencing data retrieved from TCGA was standardized by voom function.Nine hundred fifty-one glioma samples retrieved from the CGGA database (https://www.r-project.org/). PCA was employed to study the gene expression patterns in different glioma groups. We applied the first three PC values representing RNA sequencing data of each sample to establish a distribution map of the sample. Drug sensitivity analysis was later on performed with “pRRophetic” package (https://www.kegg.jp/kegg/pathway.html). The “estimate” package, “GSVA” package, and the online analytical tools CIBERSORTx (https://cibersortx.stanford.edu/) were employed to evaluate immune cell infiltration in glioma samples  and CGGA (98.6%) datasets .Publicly available datasets were analyzed in this study. This data can be found here: .The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


F1-score, and AUC. The suggested strategy greatly decreases the number of genes required for classification, selects the most informative features, and increases classification accuracy, according to the results.Cancer can be considered as one of the leading causes of death widely. One of the most effective tools to be able to handle cancer diagnosis, prognosis, and treatment is by using expression profiling technique which is based on microarray gene. For each data point (sample), gene data expression usually receives tens of thousands of genes. As a result, this data is large-scale, high-dimensional, and highly redundant. The classification of gene expression profiles is considered to be a (NP)-Hard problem. Feature (gene) selection is one of the most effective methods to handle this problem. A hybrid cancer classification approach is presented in this paper, and several machine learning techniques were used in the hybrid model: Pearson's correlation coefficient as a correlation-based feature selector and reducer, a Decision Tree classifier that is easy to interpret and does not require a parameter, and Grid Search CV  to optimize the maximum depth hyperparameter. Seven standard microarray cancer datasets are used to evaluate our model. To identify which features are the most informative and relative using the proposed model, various performance measurements are employed, including classification accuracy, specificity, sensitivity,  Cancer can be considered as one of the leading death causes , and genMicroarrays are used to measure thousands of genes interactions at the same time and create a cellular function global picture , 4.The classification of microarray data is one of the most common and important applications of functional genomics microarray data which means classifying patients samples to many classes based on their gene expression profiles , 48. In The main phase in categorization systems is feature selection. To improve classification performance, feature selection-based classification approaches have been investigated. Any statistical technique's  success In order to be able to select an informative genes subset while eliminating/declining redundant or irrelevant genes and to be able to improve the performance of microarray high-dimension data classification, this research study introduces a hybrid feature selection approach, called PCC-DTCV, which combines different methods, Pearson's correlation coefficients (PCC) and Decision Tree (DT)  as classIn order to evaluate the suggested PCC-DTCV model, 7 popular datasets from the most well-known used microarray datasets for different cancer types are used. The proposed model evaluation was carried out using classification accuracy, k-fold cross-validation, sensitivity, and specificity values.The proposed method, according to the experimental methods, reduces dimensionality and selects the most important and informative features (genes) and improves the identification of cancer tissues from benign tissues. Furthermore, PCC-DTCV improves the accuracy classification performance.Recently, there have been several major research efforts to study the classification and diagnosis of cancer microarray data. As a framework for the study presented in the remainder of the article, we offer an outline of some of this analysis and the applied techniques in N (gene measurement) × M (a sample or condition involved in a specific microarray experiment).One of the main tools employed in molecular biology and genetics to track gene expression is the DNA microarray, which refers to the degree of development of genome-determined protein molecules. Although the protein structure varies from the mRNA measurement gene instead of protein structure, it is a popular method for calculating gene expression, so thousands of proteins would be difficult to analyse, and mRNA sequences  are hybrS{S1,…, Sm} and xij measured the expression strength of the i-th gene  in the j-th sample . The row of genes represents G . In this study, we concentrate on solving two problems: the analysis and understanding of microarray data which have faced several challenges:High dimensionality and noiseIncreasing the accuracy performance taking into consideration interpretations that are biologically significant in gene expression when analysing microarray dataThe data on gene expression given by microarray experiments are processed as the broad matrix, where the samples are defined by the columns Decision Tree learning can be considered as one of the most practical and commonly used methods for inductive inference.n is the number of classes and 0 log_20 = 0 in entropy calculations:p gives the probability distribution of the data sample and “bit” due to calculating statistics. Equation , the coefficient of linear correlation r is given by the following equation [The Correlation-based Feature Selection algorithm computes the correlation coefficient using a correlation-based heuristic evaluation function (CFS). It gets over the limitation of univariate filter approaches which do not take into account feature interaction , 24. Theequation :(6)r=∑Xir and is limited as follows:In our model, we used Pearson's correlation coefficient (PCC) as feature selection . This isn log n. According to the decreasing order, each selected feature is ranked. To remove the redundant feature, the feature-feature correlation is done. Threshold values  were used and their results were compared with the Decision Tree before and after the optimization of its maximum depth (max-depth).The proposed model used Pearson's correlation to calculate the correlation between the features. Also using symmetric uncertainty measures, the class feature is calculated. If the value is higher than the threshold value (0.5), then the feature will be chosen. The selection ends when the number of features equals A Grid Search is a parameter tuning method which builds and evaluates a model methodically for each algorithm parameters combination specified in a grid. The well-known terms that should be addressed while using Grid Search CV are discussed as follows .Estimator. This term is used in order to implement the estimator interface in scikit-learn. This parameter receives the classifier to be trained [ trained .Parameter Grid. It is a Python key-value dictionary with pairs of parameter names and parameter settings. All combinations of these parameters are tested to ensure the highest level of accuracy.Cross-Validation. This determines the strategy for cross-validation splitting. Cross-validation is a technique to resample available data to evaluate machine learning models. The main goal of this is to evaluate the performance of machine learning models on previously unseen data. The main advantage of using this is that it produces less biased or optimistic results compared to a simple train-test split. The way it works is that it first shuffles the dataset randomly. The entire dataset is then divided into k groups. Each group is used as test data, while the others are used as training data. The evaluation band related to each group is saved to be summarized at the end to be able to check the performance of the model. Importantly, each sample appears once in the testing data and is used to train k − 1 times. During the training step, hyperparameters that are optimizer variables are executed to get optimal average values after several trial-and-error processes. The tree maximum depth (max-depth) is the most critical hyperparameter that influences the difficulty of the Decision Tree model , whereasThe proposed hybrid model PCC-DTCV for classifying and diagnosing cancer microarray data , Intel(R) Core (TM) i5-7500CPU, as well as frameworks such as Pandas, NumPy, Keras, SciPy, and Matplotlib and the Python programming language. The datasets for human cancer research included the following: breast cancer, prostate, colon tumor, lung cancer, and leukemia were applied to 7 different microarray datasets with a limited sample and high-dimensional and binary class. Also, all datasets have two types, and k results from the folds can then be determined. To approximate the model, 10-fold cross-validation was used in our experiments, and the results obtained are illustrated in the form of mean ± standard deviation. Also, in both analyses, the number of iterations was 50. The value of the tree (max-depth) hyperparameter is [Accuracy (ACC): it is the most used evaluation standard for the proportion of correctly predicted pairs, but using it alone is usually insufficient. Accuracy (ACC)=((TP+TN)/(TP+TN+FP+FN)).S=(TP/(TP+TN)), where TN is the true negatives number and TP is the true positives number.Sensitivity (Sen.): a diseased person is likely to be recognized as diseased through the test. Specificity (Spec.): the likelihood is that a person without the illness is defined by the test as nondiseased . It is described as TRN=(TN/(TP+TN)), where FP implies the number of false positives and TN is the number of true negatives.The AUC shows the area under the receiver operating characteristics (ROC) curve, and it is calculated as AUC=(1+TPR − FPR)/2.In all studies, 10-fold cross-validation techniques were applied. The data were randomly divided into 10 different subsets , and the experiment was conducted 10 times. One subset was used as a test set for each run, and one was used as a validation set, and the other sets were used as a training set . To obtameter is , 30. OurTP is true positive, FP is false positive, TN is true negative, and FN is false negative. Based on the confusion matrix, we evaluated the performance of the proposed method and rival gene selection. To assess the output of our model, a statistical test method called Chi-Square test  is used F1-score [The experimental results are discussed in this section to validate the performance of the our model PCC-DTCV. The classification accuracy performance and the selected feature (genes) numbers of the PCC-DTCV method with PCC  for all microarray datasets, respectively, are summarized in Tables 36F1-score , 22. To F1-score compared to PPC ≥0.4 and 0.5, which means that the classification performance of PCC-DTCV is best with PPC ≥0.6 and the selected genes (features) are the most relevant, important, and informative genes.Tables Figures 6Figures 10f = 6 and the p value of 0.001 is found to be at 0.999 confidence interval and uncertainty interval 0.001. If the value of 22.46 is greater than 18.466, then it is assumed that there are no multiple-choice outliers. Sig = .0155, which is greater than 0.0001. It is a fulfilled condition and it means that the homogeneity condition variance exists and is fulfilled by a choice of Finney greater than 0.5. This indicates that the variance of the dependent variables is equal.These results clarify that the proposed models follow a normal distribution as the value in both Kolmogorov–Smirnov and Shapiro–Wilk Sig. 0.02 ≥ .05 shown in This paper presents a PCC-DTCV model for cancer diagnosis and classification. PCC-DTCV optimizes the tuning parameter (max-depth) of the DT classifier using Grid Search and Pearson's correlation used as a gene (feature) selection method. The proposed model was effective in identifying an optimal or near-optimal subset of informative and important genes and yielded high classification results. Several experiments were carried out to evaluate the proposed PCC-DTCV model for selecting the most informative genes to improve the performance classification of cancer microarray data. In addition to reducing the dimensionality of microarray data, the result demonstrates the model's effective performance in selecting the most informative genes with high importance. The results showed that PCC-DTCV provides the best fit for cancer type prediction in terms of specificity, AUC, sensitivity, and accuracy. In future work, for Decision Tree optimization, we anticipate using ski driver and other algorithms.