Gene network inference (GNI) methods have the potential to reveal functional relationships between different genes and their products. Most GNI algorithms have been developed for microarray gene expression datasets and their application to RNA-seq data is relatively recent. As the characteristics of RNA-seq data are different from microarray data, it is an unanswered question what preprocessing methods for RNA-seq data should be applied prior to GNI to attain optimal performance, or what the required sample size for RNA-seq data is to obtain reliable GNI estimates.We ran 9144 analysis of 7 different RNA-seq datasets to evaluate 300 different preprocessing combinations that include data transformations, normalizations and association estimators. We found that there was no single best performing preprocessing combination but that there were several good ones. The performance varied widely over various datasets, which emphasized the importance of choosing an appropriate preprocessing configuration before GNI. Two preprocessing combinations appeared promising in general: First, Log-2 TPM (transcript per million) with Variance-stabilizing transformation (VST) and Pearson Correlation Coefficient (PCC) association estimator. Second, raw RNA-seq count data with PCC. Along with these two, we also identified 18 other good preprocessing combinations. Any of these algorithms might perform best in different datasets. Therefore, the GNI performances of these approaches should be measured on any new dataset to select the best performing one for it. In terms of the required biological sample size of RNA-seq data, we found that between 30 to 85 samples were required to generate reliable GNI estimates.This study provides practical recommendations on default choices for data preprocessing prior to GNI analysis of RNA-seq data to obtain optimal performance results. Gene network inference (GNI) methods can identify putative interactions between different genes and the gene products they encode. Gene regulatory networks (GRN), which can be inferred by GNI methods, help in the basic biological understanding of genes and their functions as well as inferring potential drug targets of a disease . It is wGNI methods have mostly been developed for and applied to microarray gene expression datasets . The maiganet v2.2, in order to measure the performance of each GRN prediction. Using some of the identified good performing preprocessing combinations, we conducted further analysis and determined the required sample size of RNA-seq data for optimal GNI performances. This study addresses a need of our own lab to rationale decide which pre-processing steps to apply to GNI, and we believe it provides useful guidelines and practical tools for everyone interested in infer gene networks from RNA-seq datasets.To the best of our knowledge, this is the first study that performs a systematic comparison of preprocessing combinations, which include data transformation, normalization and association estimation for GNI. We compared the combination of 15 popular preprocessing methods, along with no preprocessing case, by using 3 different GNI algorithm and 7 RNA-seq datasets for the analysis. Optimal performance was measured by comparing the identified GRN connections to a compiled literature interaction database. For this, we updated the R  softwareStep 1 as shown in Step 2, we consider 6 different basic data transformations: First, no conversion at all, leaving the counts as they are. Second, converting the raw counts to CPM (counts per million), which corrects for different numbers of reads in different samples. Third, we also used TPM (transcript per million) which is similar to CPM but also take gene lengths into account. For each of these three approaches, we also implemented a second version in which the transformation was followed by a logarithm in base 2 (Log-2). We refer to this group as data types (or datatypes) in the analysis and figures in the paper.We assembled a set of RNA-Seq data pre-processing steps, to evaluate how they perform when applied prior to GNI analysis. We considered pre-processing steps commonly used prior to GNI analysis applied to microarray data, and combining them with pre-processing steps commonly applied to RNA-Seq data in the context of differential expression analysis. Step 3, we considered more advanced normalizations, namely 3 popular normalization techniques of RNA-seq and another one that is frequently used in microarray datasets. Specifically, Variance-stabilizing transformation (VST) [calcNormFactors function of edgeR [At on (VST)  from DeSon (VST) , the trion (VST)  and alsoon (VST)  from theof edgeR  R softwaof edgeR  that is At Step 4, we implemented the rank based data transformation technique, copula–transform (CT) as it was used in the popular GNI algorithms ARACNE [s ARACNE  and C3NEs ARACNE . They ars ARACNE . We alsoAt Step 5, we implemented association estimators to measure the similarity of the expression of gene pairs. Among the 27 available estimators [timators , we seletimators . They artimators , Chao-Shtimators , 28. TheAt Step 6, we used the preprocessed datasets to infer GRNs and input them to Step 8. At Step 7, we input the literature interaction information to Step 8, where overlap analysis was performed between the predicted networks and the literature to evaluate the performances of each preprocessing combinations.A pipeline was developed to implement this workflow, which is based on the workflow management system Snakemake .recount [We downloaded 7 different RNA-seq datasets using the recount  R packagFor the main analysis, we used 6 different RNA-seq datasets (Dataset 2 to 7) and obtained a total of 5400 different performance scores to select best performing preprocessing combinations with the representative GNI algorithms. The horizontal bar plots in The wide heterogeneity in the RNA-seq data makes it very difficult to choose a suitable preprocessing combination that works best for all the datasets. In In If we had looked at a result of a single dataset we would end up selecting a best preprocessing combination. But, we used 6 different datasets that allowed us to find out that there is no best preprocessing combination valid for all the datasets. Rather, there are good combinations that work well mostly but each dataset might need a different preprocessing combination for the best GNI performance. This is pretty different than we would expect to find in the beginning of this study. For example, the best performing GNI and preprocessing combination in The complete tables for each GNI algorithm and the table of the similar analysis of all the GNI and preprocessing combinations together can be seen as a spreadsheet file in The preprocessing combinations in Ranking approach, as in On the other hand, there are still best performing combinations available and that small difference in accurate predictions may still biologically have profound effect. Because even a single newly detected interaction may be the main regulator of that biological condition. Therefore, it is still interesting to use the best performing ones among the good ones when inferring GRN from RNA-seq data. In order to be able to find best ones, we use the ranking approach on them as explained above. The difference among rank values may look larger than the real performance scores. Therefore, we consider all the information as in datatypes) derived from the basic data transformations. CS and QN were removed from further analysis as mentioned previously. Results in the box plots in In NoNorm). Regarding the best performances, there is no one clearly differentiated from others considering all the 6 datasets. Nonetheless, regarding worst performances, QN clearly provides 3 worst median scores and it does not provide a best score over all the 6 datasets. It is a clear evidence of inconsistence performance of QN. This observation lead us to exclude QN from most of the analysis as we have no intention to use it in real applications regarding these outcomes. Considering the median values, VST has 4 highest and 2 lowest ranks. NoNorm has 2 best and 4 worst ranks. It is not safe to choose VST as the best performing normalization method in general as we see it also performs worst in two out of 6 datasets. One thing is worth mentioning that when VST has best median value 4 times, then NoNorm has always the worst values. Additionally, when NoNorm has the best median values twice then VST has the worst median values. This suggest that we need to consider preprocessing combinations with and without VST normalization. RLE and TMM seem to show moderate performances and similar to each other but RLE seem to be slightly better. This analysis was done regarding median performance results but we are normally interested in the best performance scores. As we see from In We have a closer look into the best performing normalization methods in In If we look at to the pie charts in CT is an important part of GNI over microarray datasets. Here, we investigated its effect on RNA-seq datasets. In Our analysis until here focused on the problem of which GNI preprocessing combinations should be chosen and what are the possible consequences if not properly done. Nevertheless, current RNA-seq datasets may be of any size from a few to several hundreds. It is also important to know if the sample size of the available count data is sufficient to attain to the best or optimal GNI performances. This important question was answered in  for micrDataset 1 was used as an additional different data set (with sample size 85). It is used for only sample size analysis along with the other datasets. In Dataset 1, using the preprocessing combination B-spline, RLE and Log-2 CPM (first row and column), we observe that there is constant and slow increase in performance until the precision score 0.025 around maximum. C3NET and ARACNE show similar trend until the sample size 85 but C3NET provided the precision score of 0.025 in 70 samples compared to the score for sample size 85 of ARACNE. In the Dataset 1, when we use PCC  along with the same other combination (RLE ad log-2 CPM), then we can obtain the same performance score of around 0.025 in 40 samples with C3NET. With this combination, the performance of C3NET showed somewhat convergence at sample size around 40 but it still produced a performance increase from 0.025 to 0.030 after sample size 70 to 85. ARACNE does not seem to converge but produce a slow performance increase until the sample size 85 and hits the score around 0.027. We do not mention about RELNET (RN) as it was already included the study as a base algorithm and used as a reference to somewhat justify the performance scores of the main representative GNI algorithms ARACNE and C3NET. Because RN is used as the first base step of those and many other algorithms [For Dataset 2, the scores of C3NET from the preprocessing combination B-spline, RLE and Log-2  start converging to precision 0.027 at sample size 60. The performances show an increase from around 0.027 to 0.03 between the sample sizes 70 to 80. A convergence is observed afterwards until the sample size 96. On the other hand, ARACNE seems to converge at precision around 0.027 after sample size 80. For Dataset 2, using the same preprocessing combination but replacing SCC instead of B-spline , we observe slower increase in performances than before until the sample size 96.For Dataset 3, the scores of C3NET with the combination B-spline, RLE and Log-2 TPM (second row and column), rapidly converge to precision around 0.022 from sample size 8 to 30. It then increases to the precision score of around 0.026 from sample size 60 to 80 and converges second time afterwards. Here, the scores of ARACNE seem to converge at sample size 60. For Dataset 3, when we replace PCC instead of B-spline in the previous combination , the performance scores of C3NET converge at sample size 50 to the precision of around 0.027. Whereas, ARACNE seem to converge at sample size 60 to the precision of around 0.026.nonnormalized in these figures) and Log-2 TPM , C3NET and ARACNE converged at sample size 50. On the other hand, using PCC instead of B-spline allowed them to converge at sample size 30 with the similar performance scores. PCC made significant difference with respect to sample size at similar converged performance scores.For Dataset 4, the combination B-spline, no normalization . Therefore, we selected some popular ones as they are expected to be promising. As illustrated in By using 6 different datasets, we found out that there is no best but there are good combinations that work well in mostly but each dataset might need a different preprocessing combination. That directs us to use the literature and perform a quick performance analysis with the good preprocessing combinations and select the best combination specifically for the dataset of interest. Alternatively, one may just choose one of the good ones that we present in this study; knowing the fact that it may not give the best performance but a good one.Comparing to the performance of randomly generated networks, we observed some close poor performance scores to random networks but we also observed dramatic gains in performance when right preprocessing combinations used. This shows the importance of this kind of studies that will help better selecting the preprocessing combinations to be able to apply GNI algorithms on RNA-seq datasets with some confident. From our extensive analysis, we selected some good preprocessing combinations as specified in the ganet, along with this study.We concluded that preprocessing is the most important part of any GNI application over RNA-seq datasets. Because, if not properly chosen, one might end up with very poor results. We also conclude that there is no best preprocessing method but several good combinations for GNI over RNA-seq datasets, as shown in We also concluded that the minimum required sample size may vary from around 30 to 85 for each RNA-seq data for sufficient performances of GNI. We also concluded that GNI algorithm and preprocessing combinations can make significant differences in the required sample size.For the practical applications in general, instead of selecting a single GNI preprocessing combination, we suggest to select several good ones and apply them for testing their performance with the literature as demonstrated in this study.Considering all the results, especially minet [c3net [We provided important practical information about the used methods in this section. However, further details are provided for all the methods in minet  and c3net [c3net . For ARAt [c3net . In ordet [c3net . One musAt Step 5 in copula in c3net software package [calcNormFactors function of edgeR [cpm function of the popular edgeR software package [At Step 4 in  package . At Step package  of DeSeq package , the tri package  and also package  from theof edgeR  R softwaof edgeR . Further package , 35. ForAn important practical note is that, before implementing any of these preprocessing steps, we add one to each value in the dataset matrices, which otherwise may cause erroneous results because of possible zeros in the matrix. Adding one to matrix do not change the results but may prevent from failing because of zeros. Also, if a result is obtained much better than the ones presented in this study, one must be very cautious and check the predicted network. We had come across cases where a few of the genes, who have extremely larger variations than most of the others, may dominate the network as big hubs. This biased case may cause to higher scores if the gene is one of the well-studied ones in the literature. Our default filtering and adding one before each preprocessing helped overcome these cases in the studied datasets. However, one should always check out the network once predicted for such dominated genes and also other unusual structures of the network.We first filtered out the NA named genes, meaning not annotated genes from the data. If there are multiple rows with the same gene symbol, we kept only one of the raw of that gene with the highest variance. After cleaning up the dataset, we filtered them as follows: We first removed those genes whose CPM expression values less than or equal to 0.1 for at least 20% of the samples. In In order to assess the performance of GNI results, we used the combination of all the suitable wet-lab validated interaction databases we found in the literature as in . The hypWe tested whether it makes sense to accept the hypothesis of using the literature as a relative reference network for performance evaluation using the randomly generated gene networks and its overlap with the literature. The result was not significant with respect to the overlap analysis. We gave more details on this below while describing random networks. Comparing it with the inferred networks by GNI algorithms, we observed very highly significant overlap results over the literature in general. These tests support the hypothesis to use the literature and we have some level of justification to use the literature as a reference network for globally assessing, or at least comparing, the inference performance of GNI algorithms on real datasets. Nonetheless, we are aware of the fact that when a gene interaction pair is labelled as TP according to the approach, it is not an absolute TP and we might still make mistake. But for the purpose of comparison of various algorithms on real and especially de novo datasets, this idea is very helpful and works well.The idea of using the literature for GNI performance evaluation is also not new to this study. It was initially used in  and sincSince one of the conclusions of this study is to evaluate the performance each de-novo dataset with the suggested good combinations, we developed a pipeline than can be run efficiently over high performance clusters or on a single computer for a smaller size evaluation. It is based on Snakemake , which iganet (version1.0) was including the following databases by 2011: BioGRID [ganet.hyperg, which runs hyper-geometric test, and for practical performance assessment, ganet.getperformance. The software ganet v.2.2 is available to be downloaded and used from a public repository. The details of how to install and use ganet with a practical example can be seen in The literature database in R software package  BioGRID , Human P BioGRID , Molecul BioGRID , IntAct  BioGRID  and B-Ce BioGRID . With th BioGRID , innateD BioGRID , CORUM [ BioGRID  and the  BioGRID  database BioGRID . We alsoWe generated 10 different random networks with 4748 interactions that is the same as the number of predicted interactions of the best scoring preprocessing combination in the best performing dataset, which is Dataset 6. We then performed the same overlap analysis for the random networks as done so far in this study. The literature database in ganet and the random networks overlap analysis resulted average precision of 0.003 and p value of 0.58 from hyper geometric tests. These results are very useful as we can employ them as base reference levels on performances. Any performance result close to or worse than the performance of the random network is considered as very poor and the associated combination is excluded with no doubt. For example, it appeared that the combinations that include CS estimator provided very poor results and excluded from further analysis. In fact, CS estimator was among the good ones in microarray analysis . This shrecount [RNA-seq datasets were downloaded conveniently using the recount  R packagDataset 1: This dataset has GEO accession number GSE79970 [GSE79970 . The titDataset 2: The GEO number of this dataset is GSE57148 [https://jhubiostatistics.shinyapps.io/recount/) is SRP041538. The title of this dataset in GEO database is ‘Characterizing gene expression in lung tissue of COPD subjects using RNA-seq’. We used 96 of COPD samples of this dataset as Dataset 2. After filtering, there were 17520 unique genes and 96 samples in the raw dataset.GSE57148  and its Dataset 3: This dataset has the 91 normal samples of GSE57148 from the same publication [lication  of DatasDataset 4: GEO number is GSE54456 [Dataset 5: This dataset has 83 normal samples of GSE54456 from the same publication [Dataset 6: GEO number is GSE69529 and SRA number is SRP059039. The title of this dataset in GEO database is ‘Elucidating the etiology and molecular pathogenicity of infectious diarrhea by high throughput RNA sequencing’. This study is based on whole blood samples. We used 55 of Rotavirus type samples of this dataset for Dataset 6. It is worth reminding that this dataset performed best overall despite its smaller number of samples. After filtering, there were 15040 unique genes in the raw dataset.Dataset 7: This dataset has 41 Salmonella samples of GSE69529 from the same study of Dataset 6. After filtering, there were 15112 unique genes in the raw dataset. Despite its low number of sample, this dataset has second best performance.Supplement 1Supplement 2


The confusion of acute inflammation infected by virus and bacteria or noninfectious inflammation will lead to missing the best therapy occasion resulting in poor prognoses. The diagnostic model based on host gene expression has been widely used to diagnose acute infections, but the clinical usage was hindered by the capability across different samples and cohorts due to the small sample size for signature training and discovery.N = 760). In the validation set (N = 147), bvnGPS also shows strong performance by attaining an AUC of 0.988 (0.978–0.998) on bacterial-versus-other and an AUC of 0.994 (0.984–1.000) on viral-versus-other. bvnGPS has the potential to be used in clinical practice and the proposed procedure provides insight into data integration, feature selection and multiclass classification for host transcriptomics data.Here, we construct a large-scale dataset integrating multiple host transcriptomic data and analyze it using a sophisticated strategy which removes batch effect and extracts the common information from different cohorts based on the relative expression alteration of gene pairs. We assemble 2680 samples across 16 cohorts and separately build gene pair signature (GPS) for bacterial, viral, and noninfected patients. The three GPSs are further assembled into an antibiotic decision model  using multiclass neural networks, which is able to determine whether a patient is bacterial infected, viral infected, or noninfected. bvnGPS can distinguish bacterial infection with area under the receiver operating characteristic curve (AUC) of 0.953  and viral infection with AUC of 0.956 (0.951–0.961) in the test set (https://github.com/Ritchiegit/bvnGPS. The construction of iPAGE algorithm and the training of neural network was conducted on Python 3.7 with Scikit-learn 0.24.1 and PyTorch 1.7. The visualization of the results was implemented on R 4.2, Python 3.7, and Matplotlib 3.3.4.The codes implementing bvnGPS are available at  Accurate diagnosis of bacterial and viral infections can assist clinicians in deciding treatment strategies and the confusion leads to missing the best therapy occasion and results in poor prognoses . ClinicaThe host’s immune response to infection provides a noninvasive diagnostic strategy by detecting blood gene expression . In recePreviously, we proposed an algorithm named individualized Pair Analysis of Gene Expression (iPAGE) to extract the common information from different cohorts, which utilizes the relative expression changes of gene pairs to retain the most reliable gene information and ensure robustness . iPAGE iIn this study, to build a diagnostic model with high accuracy in discriminating viral and bacterial infection, we used iPAGE to integrate data from different cohorts and boost the sample size for model training . Then, wEscherichia coli, methicillin-resistant Staphylococcus aureus, tuberculosis, influenza A virus subtype H1N1 etc. were included in the current study to identify bacterial infection and viral infection.We performed a systematic search for cohorts from the Gene Expression Omnibus (GEO)  databaseThe samples of 14 cohorts in the discovery set were divided into 70% (1876) for training and 30% (804) for testing . The traOur method extracted biomarkers from one-versus-other binary classification for each category in three tasks: bacterial-versus-other, viral-versus-other, and non-infected-versus-other.Individualized Pair Analysis of Gene Expression (iPAGE) was employed to screen out gene pairs as the biomarkers . DiffereGenerally, according to previous studies , the relFormally, the where With For shrinking the variance, Fisher’s exact test was used as a screen. The cutoff of Bonferroni corrected false discovery rate  pathway , owing tUsing LASSO, we selected the least number of pairs as GPSs for bacterial infection, viral infection, and noninfected, respectively. With the 54 unique gene pairs determined, the gene expression datasets were transferred to relative expression with In the feature generation module, binary labels are used to train a single class of features, such as the bacterial feature parameters (green), viral feature parameters (red), and noninfected feature parameters (blue) . The feaIn the bvn three classification module, bvnGPS concatenates the features of bacteria, virus, and noninfected, and classifies them into three categories. bvnGPS contains multiply hidden layers with ReLU activation function and batchnorm, the three neurons in the final layer corresponding to bacterial, viral, and noninfected categories with softmax activation function.We trained this model as follows: first, the parameters of the feature generation module were trained with the binary classification. Then, with the parameters of the feature generation module fixed, we train the bvn three classification module with infection labels in three categories. . BatchsiFor comparison, we employed the following models for three-categories classification: decision tree (DT), random forest (RF) with linear discriminant analysis (LDA), and support vector machines (SVM). For DT, the max depth of layers was set by default. For RF with LDA, the RF’s parameters were in the default setting and the component number in LDA remained as 1. For SVM, we fixed the decision function as one-versus-one to vote and observed the search of hyperparameter for SVM was performed with the penalty term from 0.1 to 2. Weights and parameters of models were fixed after being modified on the training set without any subsequent modification.To compare the performance of the multiclass model, area under the receiver operating characteristic curve (AUC) was employed to assess the classification ability of models to discriminate the positive samples from the negative ones. Since it has more excellent medical value to distinguish viral infections and bacterial infections from others in clinical rescue, the average AUC of these two categories directly guides the choice of classifier , Fig. 4.clusterProfiler in R environment  database  Supplem. We exclTo conduct a large-scale host transcriptome analysis, individualized Pairwise Analysis of Gene Expression (iPAGE) was used to integrate the samples across multiple cohorts, which is a sophisticated strategy to remove batch effect and extract the common information from different cohorts based on the relative expression changes of gene pairs. Although two genes have different orders of magnitude in expression, their relative expression in an identical individual is robust to the technical variation within a cohort and batch effect among different cohorts. Thus, we hypothesized that gene pairs with a consistent relative expression change between two groups would most likely demonstrate the expression alterations. Using gene pairs, we integrated all the samples of the training set and screened out gene pairs with pronounced reversal on relative expression between a positive and a negative group to construct GPSs  to distinguish patients from bacterial infection, viral infection, and noninfection. In bvnGPS, the structure of model consists of a feature generation module and a bvn three classification module. In the feature generation module, binary labels are used to train a single class of features. In the classification module, bvnGPS concatenates the features of bacteria, virus, and noninfected as well as classifies them into three categories. Specifically, the best-performed hyperparameter combination is that the feature generation module has 64 hidden units and the bvn three classification module contains no hidden layers . TrainedWe also implemented three other multiclass machine learning models for comparison , i.e. DTTo verify the performance of bvnGPS, we combined GSE21802 and GSE57065 containing either viral infection or bacterial infection as the external validation set . The bvnFurthermore, bvnGPS achieved high specificity of 0.953 and 0.990 for the noninfected samples in the test set and validation set, respectively, which indicated the infected samples could be identified with high confidence. With the ‘just in case’ strategy, antibiotic prescription on infected patients based on bvnGPS is able to lower missed diagnosis of bacterial infection. After micro averaging, bvnGPS achieved the highest accuracy and sensitivity on the test set and validation set , althougSTAT) and Janus kinase-signal transducer and activator of transcription (JAK-STAT) cascade and immune-related functions including regulation of innate immune response, regulation of cytokine production, regulation of lymphocyte activation, and regulation of cytokine mediated signaling pathway  in the validation set . The supAlthough discrimination of bacterial and viral infections helps a lot in guiding a clinician to use antibiotics appropriately, accurate identification of specific pathogens would be a crucial requisite for optimally targeted antimicrobial treatment. However, pathogen prediction is a multilabel classification problem, which is mainly challenged by the limited sample size. In this work, we have performed a systematic search for host transcriptome datasets and collected around 3000 samples, but the sample size for each is yet insufficient to build an accurate model for specific pathogens. In our future work, we will build a more precise model sensitive to specific pathogens once sufficient data is available.In conclusion, we constructed three GPSs for different infection types based on a large-scale integrative host transcriptomic data. Multiclass neural network was utilized to assemble these GPSs and developed the final model bvnGPS, which achieved a promising performance in the test set and external validation set. bvnGPS demonstrated its potential in reducing mortality and antibiotics overuse in clinical practice. Prospective clinical validation is warranted before this prediction model is used for patient care. This method also verified the reliability and robustness of relative expression and provided inspiration for further omics studies.btad109_Supplementary_DataClick here for additional data file.


With the development of machine learning, data-driven mechanical fault diagnosis methods have been widely used in the field of PHM. Due to the limitation of the amount of fault data, it is a difficult problem for fault diagnosis to solve the problem of unbalanced data sets. Under unbalanced data sets, faults with little historical data are always difficult to diagnose and lead to economic losses. In order to improve the prediction accuracy under unbalanced data sets, this paper proposes MeanRadius-SMOTE based on the traditional SMOTE oversampling algorithm, which effectively avoids the generation of useless samples and noise samples. This paper validates the effectiveness of the algorithm on three linear unbalanced data sets and four step unbalanced data sets. Experimental results show that MeanRadius-SMOTE outperforms SMOTE and LR-SMOTE in various evaluation indicators, as well as has better robustness against different imbalance rates. In addition, MeanRadius-SMOTE can take into account the prediction accuracy of the overall and minority class, which is of great significance for engineering applications. With the continuous innovation of technology, industrial equipment has developed rapidly in the direction of large-scale, automated, integrated, and intelligent, such as aircraft engines, steam turbines, wind turbines, centrifuges, etc. In order to meet the requirements of mechanical equipment reliability and precision in the industrial field, PHM  was initiated to ensure the stable operation of mechanical equipment and reduce maintenance costs ,2,3.With the development of big data in the industrial field, data-driven mechanical fault diagnosis research has received more and more attention ,5,6. MecAt present, the research on the problem of unbalanced data sets is relatively mature, but this research in the mechanical fault diagnosis field has just begun . Many faFor the data aspect, scholars mainly use resampling technology to copy, synthesize, delete, and perform other operations on original samples, to adjust the number of samples to reduce the impact of unbalanced data sets. Resampling techniques are divided into oversampling for minority class samples and undersampling for majority class samples. The main idea of oversampling is to increase the number of minority class samples to achieve class balance. The main methods are divided into replicating samples and generating new samples. ROS (Random Oversampling) is to randomly replicate original samples to expand the number of minority class samples, but it may cause the replication of noise samples to affect the quality of the data set . The metFor the algorithm aspect, with the rapid development of machine learning, many classifiers have responded to the problem of unbalanced data sets. On the premise that each sample is equal, the number of samples determines which class the classifier prefers, so setting the weight of the sample, the threshold of the decision boundary, or the objective function of the classifier can strengthen the ability of the classifier to combat unbalanced data sets ,25. AdjuThere is no universal solution to the problem of unbalanced data sets in mechanical fault diagnosis; although, scholars have tried in various directions. From the perspective of features, extracting more abundant features from vibration signals is beneficial to solving the problem, because the failure can be reflected in the energy of the vibration of the equipment . In addiAlthough new oversampling algorithms are emerging, there are still the following problems: (1) The solutions are generally only aimed at the prediction of bearing failures or gear failures, so the methods cannot comprehensively diagnose the running state of complete mechanical equipment. (2) Most of the solutions are aimed at the two-category problem, which is obviously not practical. For a simple secondary planetary gear, there are already as many as eight failure types. (3) The new samples are not effective enough that the existing oversampling methods generate. Although the number has reached a balance, it is far from enough in terms of the amount of fault-type information contained in the sample.In view of the existing problems, this paper improves SMOTE and proposes an oversampling algorithm called MeanRadius-SMOTE, which is specially used to solve the multi-classification problems in mechanical fault diagnosis. MeanRadius-SMOTE can reduce the production of noise samples and add more samples with the ability to affect the decision boundary, and it is easier to inherit the feature information from the original samples. The complexity of the MeanRadius-SMOTE algorithm is not high compared to SMOTE.The main contributions of this paper are as follows: To solve the problem of multi-classification unbalanced data sets in mechanical fault diagnosis, a new oversampling algorithm, MeanRadius-SMOTE, is proposed. The algorithm takes into account the performance of prediction of overall and minority class, and especially in the minority class, prediction accuracy is greatly improved. In this paper, a large number of comparative experiments are carried out on data sets with various specifications and imbalance rates, and the effectiveness, stability, and robustness of the algorithm are verified.The rest of this paper is divided into five parts. In Since the machine learning algorithm is greedy in the face of multi-classification problems, the classifier will give priority to ensuring the highest overall accuracy, resulting in an inaccurate prediction of some minority class samples. In the real industrial field, in the face of some faults with low probability but high maintenance cost, operators hope that the model can accurately predict these faults. Therefore, this section introduces the commonly used methods to deal with unbalanced data sets, namely, the traditional SMOTE method and the improved LR-SMOTE method.(1)x in the training set, calculate their Euclidean distance to each minority class sample ix, and obtain the k nearest neighbors of each minority class sample.For each sample (2)ix, randomly select N samples from its k nearest neighbors, denoted as xh.According to the sample imbalance rate, set the sampling ratio N. For (3)ix and xh until the classes are balanced, denoted as xnew.According to Equation (1), build new samples based on The SMOTE algorithm was proposed by Chaw La et al. in 2002 , and theAlthough the SMOTE algorithm overcomes the overfitting problem of the ROS algorithm, SMOTE still has some problems with noise samples and useless samples. Many scholars have improved SMOTE. For example, Han proposed the Borderline-SMOTE algorithm . The alg(1)Use SVM to classify the data set, and then for the wrongly classified minority samples use the K-means method to judge and remove the noise samples.(2)cx of the minority class sample, calculate the distance id from each minority class sample to the center cx, and calculate the average distance dm.Use K-means to find the center (3)ix, calculate the ratio iM of the average distance dm and the distance id.For each minority class sample (4)ix and build new samples newx according to Equation (2).According to the number of the same samples in the neighbor samples, set the weight of each minority class sample, and then randomly select a minority class sample (5)Repeat steps 3 and 4 until the number of samples of the majority class and minority class is balanced.Based on the SMOTE algorithm, Wang proposed the LR-SMOTE algorithm . The algIn the LR-SMOTE algorithm, the new samples are generated based on the functional relationship between the sample center and each sample, rather than any two minority samples. Therefore, the new samples will not deviate from the range of the minority samples, and the features are closer to the original sample. LR-SMOTE provides a good direction for generating rules so that the algorithm determines the distribution of samples according to the sample center. This paper also proposes a new algorithm along this way to solve the unbalanced data sets in the mechanical field. We use the MeanRadius-SMOTE algorithm to experiment on a variety of mechanical failure data sets, and the experimental results show that the MeanRadius-SMOTE algorithm is suitable for solving the problem of unbalanced data sets in the mechanical field.In an oversampling algorithm, new samples at different geometric locations have different improvements in classifier training. In general, the more new samples near the decision boundary, the greater the impact on the classifier. This paper proposes the MeanRadius-SMOTE (MR-SMOTE) algorithm considering the sample center and radius. When using machine learning to predict mechanical failures, we deal with noise samples in advance, so noise reduction is performed in feature preprocessing. Noise reduction is not involved in the MeanRadius-SMOTE, and the noise reduction algorithm will be introduced in the next section.(1)xc of the minority class sample.According to each minority class sample, calculate the geometric center, denoted as the sample center (2)dm of the minority class.Calculate the Euclidean distance from each minority class sample to the sample center, and then obtain the average distance, denoted as the sample radius (3)vi from the sample center cx to the samples. Compute the resultant vector of k vectors.Randomly select k minority class samples, and then obtain k vectors (4)dm and variance Use a normal distribution with mean (5)Repeat steps 3 and 4 until the number of samples of the majority class and minority class is balanced.The MeanRadius-SMOTE algorithm mainly changes the generation rules of the SMOTE algorithm, so that the new samples are more likely to be distributed near the average radius of the minority class samples, and the new samples have a stronger ability to affect the decision boundary of the classifier. In the MeanRadius-SMOTE algorithm, the new sample is determined by In order to show the flow of the algorithm more conveniently, we draw the flow chart of the MeanRadius-SMOTE algorithm, as shown in In the MeanRadius-SMOTE algorithm, For mechanical equipment, some concurrent faults and the original fault have similar vibration states, and the two types of samples often overlap in distribution. Whether the classifier can find an excellent decision boundary is the key to determining the accuracy of the model. In the MeanRadius-SMOTE algorithm, most of the new samples are concentrated around the sample radius to ensure the validity of the new samples. The new sample is determined by The new samples of SMOTE are more inclined to be generated in locations with a high density of the original samples. Since LR-SMOTE randomly chooses a sample to determine the orientation of the new sample, the new sample is more clustered and radial. In MeanRadius-SMOTE, the orientation of new samples is relatively random, and the new samples are generated around the sample radius.Our experimental data set is the 2009 PHM data challenge of gearbox . The datMechanical equipment frequently fails in the harsh environment of high temperature and high pressure due to concurrent failures composed of multiple single failures . In the For the vibration signal, we sampled the data set using a sliding window with a stride of 100 and a width of 1000. Then we extracted time–frequency domain features for each vibration signal sample and add labels . The forIn the experiment, we used the K-nearest neighbor algorithm to denoise the data set. If the five nearest samples around a sample are not of this class, we consider it to be a noise sample and delete it. After the above preprocessing, we obtained 2656 samples per label, of which 1000 samples per label were taken as the test set. Additional samples were used to construct unbalanced data sets.In order to comprehensively evaluate the oversampling algorithm, we chose different classifiers to build the experimental model, which excludes the influence of the classifier and verifies the generality of the oversampling algorithm. Through experiments in a large number of mechanical fault diagnoses, the SVM classifier generally has a good training effect, so we chose SVM to establish a classification model. With the continuous development of the decision tree algorithm, the ensemble learning model is also favored by scholars because of its excellent generalization ability. Therefore, we chose RF (Random Forest) representing bagging ensemble mode, and GBDT (Gradient Boosting Decision Tree) representing boosting ensemble mode for experiments.Traditional evaluation indicators can well evaluate the performance of the model in the two-category problem. However, in the multi-classification problem, due to the partiality of the classifier, these indicators cannot comprehensively evaluate the model on unbalanced data sets. The expectation of the oversampling algorithm in this paper is to improve the prediction performance of the minority class without losing the overall prediction accuracy of the classifier. Therefore, we will use the traditional evaluation indicators and the prediction indicator of the minority class to evaluate the prediction model. For class (1)Accuracy (Acc): The Acc value is the ratio of the number of correctly predicted samples to the total number of samples. The calculation method is as shown in Equation (4):(2)i samples is as shown in Equation (5):Macro-Precision (Mac-P): The calculation method of the Precision value for class (3)Macro-F1 (Mac-F1): It is contradictory to improve the Precision value and Recall value at the same time. The F1 value is a balance point with high Precision value and high Recall value, and its calculation method is as shown in Equation (7):(4)small): In order to pay more attention to the prediction effect of the model on the minority class samples after oversampling algorithms, we will calculate the Precision value of the minority class as an indicator, and its calculation method is as shown in Equation (9):Precision-Minority Since these seven unbalanced data sets are homologous, the better the oversampling algorithm, the closer the indicators should be. Comparing the nine charts, all indicators are relatively stable in the MeanRadius-SMOTE experiment, which is less affected by the imbalance rate and data set form, and this stabilization is more obvious in the SVM classifier. This shows that MeanRadius-SMOTE has good robustness.(2)small is relatively high, about 0.75.Analyzing the three charts—(3)small, and in most experiments, Presmall is greatly affected by the data sets, especially in the RF experiments. However, only in the model composed of MeanRadius-SMOTE and SVM do we obtain a very flat line, which shows that this model has good robustness and accuracy in predicting the minority class.Comparing the three charts—(4)Comparing the three charts—According to small. We can think that this is the result of sacrificing the prediction performance of the minority class. Therefore, it can still be considered that MeanRadius-SMOTE is better than SMOTE and LR-SMOTE. Furthermore, the model composed of MeanRadius-SMOTE and SVM can improve prediction accuracy and stability.In summary, MeanRadius-SMOTE shows excellent performance in all experiments, which can take into account the prediction performance of the overall and minority class. In individual experiments, SMOTE is slightly higher than MeanRadius-SMOTE in Acc, Mac-P, and Mac-F1, but lower than MeanRadius-SMOTE in PreMechanical fault diagnosis has always been a key issue in the PHM. Since the development of machine learning, although mechanical fault diagnosis has been solved by many effective methods, fault diagnosis under unbalanced data sets has always been a stubborn problem. The oversampling algorithm is currently recognized as an effective means to solve the problem of unbalanced data sets. The traditional oversampling algorithm is not only affected by the sample distribution, but also easily generates noise samples, which makes the decision boundary blurred. These drawbacks are not conducive to the classifier making predictions.Based on the SMOTE, this paper proposes the new algorithm, MeanRadius-SMOTE, combining the sample center and radius. MeanRadius-SMOTE effectively avoids useless samples and noise samples in the process of generating new samples. In this paper, we conduct comparative experiments for SMOTE, LR-SMOTE, and MeanRadius-SMOTE algorithms and use SVM, RF, and GBDT classifiers on three linear unbalanced data sets and four step unbalanced data sets. Experimental results show that the MeanRadius-SMOTE algorithm can effectively balance data classes and improve the prediction performance of machine learning classifiers. From the perspective of various indicators, the MeanRadius-SMOTE algorithm is better than SMOTE and LR-SMOTE, and has better robustness. In the problem of unbalanced data sets, MeanRadius-SMOTE can more accurately predict the minority class without sacrificing the prediction performance of other classes, which is of great significance for mechanical fault diagnosis, and the combined model of MeanRadius-SMOTE and SVM is proved to be much better than other models.(1)In this paper, in order to ensure that the experiment is carried out under a variety of unbalanced data sets, we use artificial unbalanced data sets in experiments. In future research, we will collect the failure unbalanced data sets of actual mechanical equipment to continue the verification experiment.(2)When constructing the data set in this paper, we only extracted the time–frequency domain features from the vibration signal. Currently, there are more methods to extract features from vibration signals, such as convolutional neural networks, wavelet packet decomposition, etc. Training sets composed of different types of features may have an impact on the performance of MeanRadius-SMOTE.Although this paper proves on PHM09 challenge data that MeanRadius-SMOTE has a good ability to deal with unbalanced data sets, considering the actual situation, future research can be carried out from the following aspects:


Drug-induced liver injury (DILI) is the most common adverse effect of numerous drugs and a leading cause of drug withdrawal from the market. In recent years, the incidence of DILI has increased. However, diagnosing DILI remains challenging because of the lack of specific biomarkers. Hence, we used machine learning (ML) to mine multiple microarrays and identify useful genes that could contribute to diagnosing DILI. In this prospective study, we screened six eligible microarrays from the Gene Expression Omnibus (GEO) database. First, 21 differentially expressed genes (DEGs) were identified in the training set. Subsequently, a functional enrichment analysis of the DEGs was performed. We then used six ML algorithms to identify potentially useful genes. Based on receiver operating characteristic (ROC), four genes, DDIT3, GADD45A, SLC3A2, and RBM24, were identified. The average values of the area under the curve (AUC) for these four genes were higher than 0.8 in both the training and testing sets. In addition, the results of immune cell correlation analysis showed that these four genes were highly significantly correlated with multiple immune cells. Our study revealed that DDIT3, GADD45A, SLC3A2, and RBM24 could be biomarkers contributing to the identification of patients with DILI. Drug-induced liver injury (DILI) refers to liver damage caused by a drug itself or its metabolites  . The cliIn general, clinical analysis is performed in combination with biochemical, histological, and imaging examinations. Commonly used biochemical indicators include alanine aminotransferase (ALT), aspartate aminotransferase (AST), alkaline phosphatase (ALP), and total bilirubin (TBIL) ,10; howeIncreasing evidence suggests that the occurrence and development of DILI may be a multi-gene, multi-cellular, and multi-path process; therefore, it is difficult to achieve a comprehensive understanding of the pathogenesis of individual genes . Given tLASSO, a typical ML technique, is commonly used for the prediction of various diseases, especially tumor diagnosis. Y. Zhou used Lasso to develop the radiologic features based on dynamic contrast-enhanced (DCE) MR images to predict the microvascular invasion (MVI) in mass-forming intrahepatic cholangiocarcinoma (IMCC) before surgery. The model showed good accuracy, sensitivity, and reproducibility . SVM, a There have been many studies on MLs, and different algorithms have different levels; therefore, combining multiple ML techniques will greatly improve the credibility of the analysis results.Several laboratory and clinical studies have revealed that there are differences in transcriptomes between patients with DILI and healthy individuals. In our study, we collected multiple DILI microarrays from the Gene Expression Omnibus (GEO) dataset to identify biomarkers for DILI diagnosis using six ML algorithms. The results were confirmed by differential and area under the curve (AUC) analysis in the testing groups. It is worth mentioning that, to our knowledge, this may be the first study to use ML to mine genes from the GEO database that are useful for DILI diagnosis, hoping to provide a scientific basis for future clinical diagnosis.Based on the inclusion and exclusion criteria, six microarrays , as detailed in GO analysis was conducted from three different aspects, namely, biological process (BP), cellular component (CC), and molecular function (MF). According to the GO enrichment results , 94 termp < 0.05)  .We adopted the ROC curve and AUC values to assess the diagnostic value of the four genes. When we set the four genes (as mentioned above) into the ROC curve, the results showed that their AUC values were >0.8 in both the training and testing sets . In concTo analyze the correlation between the diagnostic genes and 22 immune cells, we demonstrated the immune cells’ correlation among 132 samples of six microarrays . MoreoveTo our knowledge, this is the first study to identify biomarkers for DILI diagnosis based on information from the GEO dataset. Although DILI has become common in clinical practice in recent years, its diagnosis and differentiation from other diseases remain unclear. Previous studies have suggested that different kinds of drugs could cause liver injury, and DILI covers almost all known types of liver injury, simplified into hepatocellular, cholestatic, and mixed injuries . Howeverset.seed function to separate the training and testing sets, and the microarrays were randomly divided into the training and testing sets in a ratio of 8:2. In fact, we found that the segmentation of the training and testing data has different division standards in the different articles, such as 90:10 [The pathogenesis of DILI is complex and unclear, and its occurrence and development do not result from a single pathway. Currently, the “three-step mechanism” is the most widely recognized, includes initial injury, mitochondrial permeability transition, and apoptosis or necrosis . Currentas 90:10 , 85:15 [as 90:10 , 80:20, as 90:10 , 60:40 [as 90:10 , and so Subsequently, we developed and optimized six ML algorithms and successfully established a DILI diagnostic prediction model. It was worth noting that, in this work, we paid more attention to the values of the weights rather than the models, although we established six models of ML for classification. Therefore, we are not exaggerating to say that our method is the best but also that we can extract useful information about DILI diagnosis well in these six algorithms. For the selection of these six algorithms, the main considerations were the algorithm’s matching to the sample data, a wide range of applications in disease classification and diagnosis, and prediction performance. LASSO is one of the most commonly used algorithms, and its prediction performance has been demonstrated in previous studies . In thisAs the name implies, DDIT3, also known as C/EBP homologous protein (CHOP), is a key stress-responsive transcription factor that is activated in a variety of cellular stress responses, including DNA damage, endoplasmic reticulum stress, hypoxia, and amino acid starvation, thereby inducing cell cycle arrest and apoptosis. The KEGG analyses results showed that these genes were mainly enriched in apoptosis, ferroptosis, and various tumor-related pathways. Indeed, apoptosis and ferroptosis are also inseparable from tumor development; unlike apoptosis, ferroptosis is an iron-overload necrosis pathway that occurs due to the inhibition of cystine–glutamate exchangers on the cytoplasmic membrane and is characterized by the accumulation of lipid peroxides ,62. Prev4)-induced hepatic fibrosis in mice was accompanied by the downregulation of GADD45A and the upregulation of transforming growth factor (TGF-β)/Smad, ultimately confirming that GADD45A regulates the activation of hepatic stellate cells by inhibiting TGF-β/Smad signaling, thereby preventing liver fibrosis [GADD45A is a stress gene subject to conditioning by P53, which is involved in biological functions such as DNA damage repair, cell cycle arrest, apoptosis, and tumor inhibition. Recent reports have suggested that decreased GADD45A expression due to abnormal methylation may contribute to cancer cell resistance to radiotherapy through the PI3K/AKT signaling pathway ,73. Morefibrosis .RBM24 contains a conserved RNA recognition motif (RBM) composed of RNP1 and RNP2 subunits . Like GAIt is well known that enzymes play an important role in the metabolism of drugs after entering the body, but in fact, another class of molecules—transporter proteins, especially solute carriers (SLC)—also play an indispensable role. Of note, SLC3A2, an important member of the SLC family, plays a role in regulating the transmembrane transport of amino acids. In this study, we found that the AUC of the SLC3A2 gene in both the training and testing sets was above 0.80, implying that it could be used as a marker for the diagnosis of DILI. Interestingly, no study has reported its association with DILI. However, many studies have shown that SLC3A2 is highly expressed in most tumor types . Recent We are well aware that the present work, although innovative and interesting, has certain limitations. Firstly, this is a retrospective case-control study, which is more susceptible to selection bias than other epidemiological studies. Secondly, an inadequate sample number is a common limitation of bioinformatics research, although we have validated the prediction model on a test set based on AUC values. Thirdly, our current level of machine learning is rudimentary, and the detection performance metrics are relatively single. Lastly, it is well known that the “black box” characteristics of machine learning algorithms  may be difficult to explain. In the future, it is necessary to further verify these results through in vitro experiments. However, we still hope that our current work can be of some use to relevant research and clinical diagnosis.We searched the GEO database with “drug-induced liver injury” as the keyword. The dataset was screened according to the following criteria: (1) the biological species was human; (2) the item type was series ID; (3) the study type was expression profiling by array; (4) expression profiles included both healthy samples and DILI samples. The exclusion criteria were as follows: (1) presence of viral hepatitis or other liver diseases and (2) samples with gene knockout.sva R package  and preprocessCore  were applied to eliminate branch effects and quantile normalization among the multiple microarrays that met the inclusion criteria. Subsequently, we set the random seed at 234 with the set seed function to separate the training and testing sets in a ratio of 80:20. The training set was used to develop the prediction model, and, as its name implies, the testing set was used to validate the results of the model.First, the limma R package  to screen the DEGs in of both the DILI and control samples in the training set. After quantile normalization, the raw data were log2 transformed. According to the method of Benjamini and Hochberg, the p-value was adjusted to control for the FDR. We filtered DEGs based on the following criteria: |logFC | > 0.8 [t-test), and p-value < 0.05.We used the  | > 0.8 , FDR < 0clusterProfiler, DOSE, and enrichplot packages of R, versions 3.16.1, 3.14.0, and 1.8.1, respectively. GO analysis was composed of MF, BP, and CC.The DEGs obtained by the above methods were subjected to functional and pathway enrichment analysis, specifically including GO, KEGG pathway analysis, and GSEA based on the glmnet  R package. In this R package, the function cv.glmnet was adopted to tune the value of lambda. Then, the R package glmnet was adopted to the model. Furthermore, we set the min lambda as 2000, family as “binomial”, ten folds, and type of measure as “class”. With the tuned parameter, we built an optimal LASSO model for the validation of the testing set. The SVM algorithm was accomplished by the e1071 R package . In e1071, function tune.svm was added to tune the parameter. We set the kernel as “linear”; the cost was from 1 to 20, with a step size of one, and ten folds. With the tuned parameter, we built an optimal SVM model for the validation of the testing set. The DT algorithm was accomplished by the rpart R package  and rpart.plot R package . In this package, the rpart function was served to the model; the method was set as “class” and the cp value as 0.000001, with ten folds. With the DT model, we used the testing set for validation. The RF algorithm was accomplished by the randomForest R package . In this package, the tuneRF function was served to tune the parameter, with a step factor of 1, 500 trees, and ten folds. With the tuned parameter, we built an optimal RF model for the validation of the testing set. The NN algorithm was accomplished by the neuralnet R package . In this package, the neuralnet function was served to the model, with 3 hidden layers, with err.fct as “sse”, linear output, and ten folds. With the RF model, we used the testing set for validation. Different from the other algorithms, the GBM algorithm uses more processes and is error-prone. The GMB algorithm was accomplished by the h2o R package . Because the h2o needs the JAVA operating environment. We downloaded and installed jdk ; jdk is free and public . Firstly, the h2o.init function served to adjust the running memory (we set it with 8G). Different from the above other algorithms, both the training and the testing sets must be transformed into the h2o format with the as.h2o function. Then, the function h2o.gbm was served to the model and to tune the parameters, setting the distribution as “bernoulli”, with 100 trees, a learn rate of 0.01, a sample rate of 0.8, and ten folds. With the optimal GBM model, we took the testing set for validation. With the above process, each MLs harvests the variable weight of DEGs. Importantly, we normalized and summed the absolute values of the various weights of the DEGs using the formula: pROC package  was used to obtained the ROC. The AUC was calculated to determine the diagnostic value of the healthy and DILI samples. The higher the AUC value, the higher the diagnostic value.With the above DEGs, we built adopted six ML algorithms  to classify the DILI and healthy control. Furthermore, the error rate was adopted to verify the accuracy in both training and testing sets. The LASSO algorithm was adopted by the In order to analyze the correlation between useful genes and immune cells, Spearman’s rank correlation was used to analyze the correlation between 22 immune cells and four diagnostic genes, DDIT3, GADD45A, SLC3A2, and RBM24.https://www.r-project.org) and RStudio basement . For continuous variables, the independent Student’s t-test was used if the variables met the Gaussian distribution; if not, the Wilcoxon test was used. The Chi-squared test was used for categorical variables, and the Wilcoxon test was used for signed-rank variables. Correlation analysis was performed using Pearson’s or Spearman’s coefficient. A two-sided p value < 0.05 was considered to indicate statistical significance.Statistical analysis was performed with R software (version 4.1.4, written by Ross Ihaka and Robert Gentleman from the University of Auckland originally, N.z., and maintained by the Lucent Technologies, USA, In conclusion, we successfully established a DILI diagnostic prediction model based on the total weight method by identifying several DILI microarrays in the GEO database and using six ML algorithms. A series of analyses and confirmations revealed that DDIT3, GADD45A, and RBM24 may help identify patients with DILI and become useful biomarkers for the clinical diagnosis of DILI.


Identifying subgroups of T-cell acute lymphoblastic leukemia (T-ALL) with poor survival will significantly influence patient treatment options and improve patient survival expectations. Current efforts to predict T-ALL survival expectations in multiple patient cohorts are lacking. A deep learning (DL)-based model was developed to determine the prognostic staging of T-ALL patients. We used transcriptome sequencing data from TARGET to build a DL-based survival model using 265 T-ALL patients. We found that patients could be divided into two subgroups (K0 and K1) with significant difference (P< 0.0001) in survival rate. The more malignant subgroup was significantly associated with some tumor-related signaling pathways, such as PI3K-Akt, cGMP-PKG and TGF-beta signaling pathway. DL-based model showed good performance in a cohort of patients from our clinical center (P = 0.0248). T-ALL patients survival was successfully predicted using a DL-based model, and we hope to apply it to clinical practice in the future. It is well known that T-cell acute lymphoblastic leukemia (T-ALL) is a blood disease with high clinical incidence. The pathogenesis of T-ALL is complex. Genetic factors, viral infection and some toxic compounds can promote the occurrence of T-ALL . About a+, CD4+, or CD8+ immunophenotype presents a relatively favorable prognosis  framework on the T-ALL datasets. The deep learning framework we use is called autoencoder. Autoencoder has the function of representing learning algorithm in general sense. It has been shown that autoencoders can effectively generate prognostic features , 14. TheWe trained the model with 265 T-ALL samples from the Therapeutically Applicable Research to Generate Effective Treatments (TARGET) database. We found two subgroups with significant difference in survival time. These two subgroups have been identified as having independent predictive values for patient outcomes. Most importantly, the two subgroups derived from our DL framework have been successfully validated in self-built real-world patient cohorts. By analyzing these two subgroups, we found new genes and pathways that significantly affect the prognosis of T-ALL. As a result, this paper presents a significant DL-based model for predicting the prognosis of patients with T-ALL.https://ocg.cancer.gov/programs/target). After the removal of 21 samples with unknown survival status and 3 samples with unclear survival time, 241 T-ALL samples were finally included in the study. In order to minimize batch effects between data from different sources and to allow the model to be applied to a larger scale, the fragments per kilobase of transcript per million fragments mapped (FPKM) expression values were used in this study. To make the final model more explanatory, all gene IDs were converted to official gene symbols based on Gencode v22 (www.gencodegenes.org).In this study, 265 transcriptome sequencing samples from patients with T-ALL were collected from the TARGET database . The data was available at the GEO under accession numbers GSE214998.The lymphocytes separation medium  was used for the isolation of mononuclear cells from bone marrow aspirate. The Trizol reagents were added  to the sample and let stand for 10 minutes. Then chloroform was added, mixed well and let stand for 3 minutes. After centrifugation at 12,000 × g for 15 minutes at 4°C, the supernatant was taken and mixed with isopropyl alcohol of equal volume and let stand for 10 minutes. The supernatant was removed after centrifugation at 12,000 × g for 10 minutes at 4°C, and the precipitate was washed with ethanol. After centrifugation at 7,500 × g for 5 minutes at 4°C, the supernatant was removed and allowed to stand and dry for 15 minutes. Mix with DNase for 30 minutes and wash once with ethanol. Finally, RNase-free DDW was used to dissolve the RNA. Monitoring RNA contamination and degradation was carried out using AGAR gels. The NanoPhotometer spectrophotometer  was used to determine the purity of RNA, while the Bioanalyzer 2100 system  was used to determine the integrity of RNA.mRNA was purified using magnetic beads and Oligo(dT)-attached magnetic beads. Libraries were generated using the NEBNext UltraTM RNA Library Prep Kit for Illumina . In NEBNext First Strand Synthesis Reaction Buffer, divalent cations were used for fragmentation at high temperatures. In order to synthesize first strand cDNA, M-MuLV reverse transcription enzyme was used in conjunction with random hexamer primers. Using RNase H and DNA Polymerase I to synthesis the second strand cDNA. For hybridization, the NEBNext adaptor with hairpin loop structure were ligated. 250~300 bp cDNA fragments were selected by AMPure XP system . Before polymerase chain reaction (PCR), we used USER enzyme  to digest cDNA at 37°C for 15 min followed by 95°C for 5 min. The PCR is performed using Phusion High-Fidelity DNA polymerase, Universal PCR primers, and Index (X) Primer. The assay results were evaluated using Agilent Bioanalyzer 2100 system . The PCR products were purified using Beckman Coulter’s AMPure XP system.The TruSeq PE Cluster Kit (Illumia) was used to perform the clustering of the index-coded samples. Sequencing was performed on an Illumina Novaseq platform, with 150-bp paired-end reads. From raw fastq data, we removed reads containing ploy-N or low quality reads, as well as reads containing adapters. Clean Data was used for subsequent experiments.f of the two, and g minimizes the reconstruction error of the input feature:Autoencoder (AE) is a kind of Artificial Neural Networks (ANNs) used in semi-supervised learning and unsupervised learning, its function is to learn the representation of input information . The strhttps://github.com/tensorflow), including input layer, output layer and 3 hidden layers. The three hidden layers contain 500, 100 and 500 neurons respectively. The hidden layer in the middle becomes the bottleneck of the entire network, forcing the network to compress the input data and generate new features. The 50% dropout was used to make the entire network more robust. The whole network training process we carried out 10 epochs.After the solution is completed, h , the feature output by the encoder, can be regarded as a representation of the X . We build an autoencoder with 5 layers in Python (v3.8.12) based on TensorFlow 2.7.0  algorithm  was traiwhere S is the variance of the training samples, and U is the mean of the training samples. When training the XGB models, we used K-fold cross-validation (k=5) and grid search from the sklearn Python package to find the optimal hyperparameters. The sample grouping probability output from the XGB model was used as the survival risk score.In order to explore whether the Deep Learning framework could be replaced in this study, we tried two alternative approaches to carry out the experiment. The first alternative was to use principal components analysis (PCA) to reduce the dimension of the input data to replace the features generated by 100 neurons in the autoencoder bottleneck layer. We choose to retain at least 95% of the explained variance ratio. The second alternative was to use single-variant Cox-PH models to reduce the number of features in the input data. After the completion of the two alternative approaches, K-means clustering was required to observe whether patients with significant differences in survival time could be grouped.We performed some functional analysis to reveal the potential factors that could significantly affect the survival time of T-ALL patients.We collected and compared the clinical covariates of patients in K0 and K1. Including the age of patients at diagnosis, peripheral blood white blood cell (WBC) count, peripheral blood lactate dehydrogenase (LDH), bone marrow blasts percentage and WT1 expression in bone marrow cells measured by PCR. As well as whether the patient underwent hematopoietic stem cell transplantation (HSCT).To identify differentially expressed genes between survival subgroups, differential expression analysis was performed on mRNA expression. We used the limma (v3.32.7) R package . The selBy using clusterProfiler (v3.14.3) R package, we analyzed pathways enriched by up-regulated and down-regulated genes according to the Kyoto Encyclopedia of Genes and Genomes (KEGG). The inclusion criteria for pathways were P< 0.05, minimum count > 3, maximum count< 5000 and adj.P< 0.1.https://cibersortx.stanford.edu/) was used to analyze infiltrating immune cells in 22 tumors.The StromalScore, ImmuneScore and ESTIMATEScore were calculated using ESTIMATE . CIBERSOFrom TARGET database, we obtained the transcriptome sequencing data of 241 T-ALL patients, and each patient had mRNA expression levels of 24991 genes. All features were first input into the autoencoder . An autoUnivariate Cox-PH model was performed on 100 new features, and 42 features that were correlated with survival  were selected. K-means clustering determined 42 features subjectively, and the value of K was tried from 2 to 10. The Silhouette score was calculated after each clustering, and the score closest to 1 was selected. Finally, the best K value was 2. That is, the optimal cluster number was 2. The two groups obtained by clustering were called K0 and K1. There was also a significant difference in survival between the two clusters based on the KM survival curves . That left 9 principal components. However, there was no significant difference in survival time between K0 group and K1 group  could be very inconsistent. The second point is that there are differences in the way patients’ samples are stored. Most of the samples were dissolved in DDW in the form of RNA and refrigerated at minus 80 degrees Celsius. Some samples were directly refrigerated after total RNA was dissolved in Trizol. The third point is that patients’ clinical information is kept inconsistently. The loss of information may be due to frequent changes in the hospital’s record system in recent years, or it may be due to omissions in patient information collection years ago. Despite these problems, we obtained positive results in our external validation cohort, which demonstrated the robustness of this model. As clinicians and researchers, we look forward to conducting prospective studies in the future, not only to improve the model, but also to improve treatment for our patients.https://www.ncbi.nlm.nih.gov/, GSE214998.The datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found below: The studies involving human participants were reviewed and approved by ethics committee of Guangdong Provincial People’s Hospital. The patients/participants provided their written informed consent to participate in this study.LHZ, PLL, XD, and JYW conceived and designed this study. LHZ, LJZ, CL, YLW, PJL, LYZ, SXG, and PLL performed data analysis. LHZ, LJZ, XD, and JYW wrote the manuscript. All authors contributed to the article and approved the submitted version.This work was supported by the National Key R&D Program of China (No. 2017YFE0131600), the National Natural Science Foundation of China , the Natural Science Foundation of Guangdong Province, China  and High-level Hospital Construction Project of Guangdong Provincial People’s Hospital (DFJHBF202107).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.


Detailed understanding of pre-, early and late neoplastic states in gastric cancer helps develop better models of risk of progression to gastric cancers (GCs) and medical treatment to intercept such progression.We built a Boolean implication network of gastric cancer and deployed machine learning algorithms to develop predictive models of known pre-neoplastic states, e.g., atrophic gastritis, intestinal metaplasia (IM) and low- to high-grade intestinal neoplasia (L/HGIN), and GC. Our approach exploits the presence of asymmetric Boolean implication relationships that are likely to be invariant across almost all gastric cancer datasets. Invariant asymmetric Boolean implication relationships can decipher fundamental time-series underlying the biological data. Pursuing this method, we developed a healthy mucosa → GC continuum model based on this approach.Our model performed better against publicly available models for distinguishing healthy versus GC samples. Although not trained on IM and L/HGIN datasets, the model could identify the risk of progression to GC via the metaplasia → dysplasia → neoplasia cascade in patient samples. The model could rank all publicly available mouse models for their ability to best recapitulate the gene expression patterns during human GC initiation and progression.A Boolean implication network enabled the identification of hitherto undefined continuum states during GC initiation. The developed model could now serve as a starting point for rationalizing candidate therapeutic targets to intercept GC progression.The online version contains supplementary material available at 10.1007/s10120-022-01360-3. Helicobacter pylori, a known carcinogenic pathogen [Gastric cancer (GC) often presents as an advanced disease with patients either having inoperable conditions or surgery as the only potentially curative treatment . There ipathogen , 3. Riskpathogen . This onpathogen .Research into GCs has used impactful approaches to investigate the genome , therapeHere, we present a network-based approach for biomarker and target discovery that uses artificial intelligence (AI) to select genes and then perform rigorous validation in multiple independent GC datasets. Previously, we have successfully exploited this approach to identify biomarkers in IBD , COVID-1Detailed methods for computational modeling and AI-guided target identification are presented in Online Resource 1 and mentioned in brief here.GSE66229 [n = 400; 300 GC tumor and 100 patient-matched normal tissue). A Boolean Network Explorer  computational tool was introduced, which uses asymmetric properties of Boolean implication relationships  . We creaGSE66229  (n = 400lgorithm ) to modelgorithm . (2) Genlgorithm . Prior wA Boolean path contains one or more clusters. A composite score for each sample is calculated to provide a summary of the genes expressed in the Boolean path. The composite score is calculated using the following steps: (1) the genes in each cluster were normalized and averaged. Gene expression values were normalized according to a modified Z-score approach centered around StepMiner threshold (formula = (expr—SThr)/3/stddev). (2) A weighted linear combination of the averages from the clusters of a Boolean path was used to create a score for each sample. We either monotonically increased or decreased the weights along the path to make the sample order consistent with the logical order based on BIR. We then order the samples based on the final weighted and linearly combined score. If a cluster is highly expressed in a disease setting, it received a positive weight  and if a cluster is highly expressed in a healthy setting, it received a negative weight .n = 65, non-malignant = 36, GC tumor = 29; GSE122401, n = 160, patient-matched normal = 80, GC tumor = 80) to train a network model that should distinguish normal vs GC samples. Using Ordinary Least Squares (OLS) regression in Python statsmodels (version 0.12.2), we performed multivariate analysis to determine which models performed best in the two training datasets.We used two microarray datasets , t test , and unequal sample size). For all tests, a p value of 0.05 was used as the cutoff to determine significance. Violin and bar plots are created using Python seaborn package version 0.10.1.Statistical significance between experimental groups was determined using Python scipy.stats.ttest_ind package (version 0.19.0) with Welch’s two sample GSE66229) that is comprised of tumor (T) and adjacent normal (AN) samples, we built a Boolean implication network . Clusters #11-2-4–14 (C#11-2-4-14) performed the best with an ROC-AUC of 0.96 in training dataset #1 (GSE37023 AN versus T), while clusters #7-13-14 (C#7-13-14) performed best in training dataset #2 (GSE122401 AN vs T) with an ROC-AUC of 0.98  → metaplasia → dysplasia → neoplasia cascade Fig. a, b. In t test to determine the top ten models  is one in which the H. pylori infection → GC cascade is modeled in C57Bl6 mouse model of experimental infection with the closely related H. felis. The authors showed that while most infected mice develop premalignant lesions such as gastric atrophy, compensatory epithelial hyperplasia and IM, a minority is completely protected from pre-neoplasia. The models that ranked #2-6 (GSE103639 (NGE vs pCP_GC), GSE45956, GSE103639 (NGE vs pChePS_GC), GSE16902, GSE93774) were all genetically engineered mouse models (GEMMs) in which targeted deletions were performed on genes  that are associated with risk of GC, by virtue of being either the most common germline mutation in GC (CDH1 [SMAD4 [CLDN18). These results suggest that GC-BoNE can objectively assess the degree of similarity between mouse models  and human GC. In doing so, it can pinpoint which mouse models best recapitulate the patterns of gene expression that is observed during the transformation from healthy to GC in human samples.Next, we wanted to identify mouse models that recapitulated human normal versus GC. We analyzed 38 mouse models –42 from GC (CDH1 ), or for1 [SMAD4 ) or beint test) low risk-carrying CIM from HC. C#7-13-14 also could distinguish HC vs IIM-C (ROC-AUC = 0.80) and HC vs IIM-GC (ROC-AUC = 0.88), but not IIM-C vs IIM-GC (ROC-AUC = 0.71); however, C#11-2-4-14 performed better  to identify a gene signature (GC-BoNE) which could classify normal and gastric samples. Reactome pathway analysis of GC-BoNE revealed C#11-2-4-14 contains genes that control infection-inflammation: increase in cell cycle related genes in C#2 may lead to abnormal cell proliferation [Although the incidence rates of GC have been decreasing around the world , there hferation , 16, incferation –19 and iferation , 21. Altferation –23, muscfirst identifying a gene signature (GC-BoNE) that is better able to classify samples along the GC disease continuum compared to previous signatures. When looking at normal vs GC samples, many of the signatures performed well  that looked at whether precancerous gastric lesions progressed over time . Since the data was profiled on a DASL Human Cancer Panel microarray, many genes in GC-BoNE were not included in the generation of the violin plot . The resulting violin plot indicates we may not be able to predict which patients will progress using the available genes on a DASL cancer panel (progressors follow-up samples have lower scores than at baseline). The other datasets are small and did not show consistent patterns (Online Resource 5d-h). Due to the limited availability of datasets, we conclude that additional prospective studies at all stages of GC progression are necessary before we can fully evaluate the capability of GC-BoNE derived gene signatures to identify high-risk patients.Although this work provides a new set of genes that can be targeted for GC and precancerous conditions, we were not able to rigorously test whether GC-BoNE could identify patients with early lesions such as chronic atrophic gastritis who are at highest risk of progression to GC. We identified six additional datasets  from gastritis samples. One of them is a prospective study Supplementary Online Resource 2 List of GSE IDs used in the analysis along with sample type (human vs mouse), use  and figure panel file2 (XLSX 10 KB)Supplementary Online Resource 3 Complete list of genes used in all gene signatures (GC-BoNE and signatures from other sources) file3 (XLSX 29 KB)Supplementary Online Resource 4 Bubble plots of ROC-AUC values (radius of circles is based on the ROC-AUC) demonstrating the direction of gene regulation  for the classification of samples in 38 mouse models . P-values based on Welch’s T-test  are provided using the standard code  next to the ROC-AUC file4 (PDF 565 KB)Supplementary Online Resource 5 Analysis of atrophic gastritis datasets using GC-BoNE model. a. Schematic hypothetical disease continuum path from normal, gastritis, intestinal metaplasia to gastric carcinoma. b. Schematic describing study in GSE69144. c-h. Violin plots for gastritis datasets  using the GC-BoNE signature: 11-2-4-14 (left) and 7-13-14 (right) file5 (PDF 3082 KB)Below is the link to the electronic supplementary material.