A novel bioinformatics approach to identify the consistently well-performing normalization strategy for current metabolomic studies

MultiClassMetabo: A Superior Classification Model Constructed Using Metabolic Markers in Multiclass Metabolomics

Multiclass metabolomics has become a popular technique for revealing the mechanisms underlying certain physiological processes, different tumor types, or different therapeutic responses. In multiclass metabolomics, it is highly important to uncover the underlying biological information on biosamples by identifying the metabolic markers with the most associations and classifying the different sample classes. The classification problem of multiclass metabolomics is more difficult than that of the binary problem. To date, various methods exist for constructing classification models and identifying metabolic markers consisting of well-established techniques and newly emerging machine learning algorithms. However, how to construct a superior classification model using these methods remains unclear for a given multiclass metabolomic data set. Herein, MultiClassMetabo has been developed for constructing a superior classification model using metabolic markers identified in multiclass metabolomics. MultiClassMetabo can enable online services, including (a) identifying metabolic markers by marker identification methods, (b) constructing classification models by classification methods, and (c) performing a comprehensive assessment from multiple perspectives to construct a superior classification model for multiclass metabolomics. In summary, MultiClassMetabo is distinguished for its capability to construct a superior classification model using the most appropriate method through a comprehensive assessment, which makes it an important complement to other available tools in multiclass metabolomics. MultiClassMetabo can be accessed at http://idrblab.cn/multiclassmetabo/.

Evaluating Machine Learning Methods of Analyzing Multiclass Metabolomics

Multiclass metabolomic studies have become popular for revealing the differences in multiple stages of complex diseases, various lifestyles, or the effects of specific treatments. In multiclass metabolomics, there are multiple data manipulation steps for analyzing raw data, which consist of data filtering, the imputation of missing values, data normalization, marker identification, sample separation, classification, and so on. In each step, several to dozens of machine learning methods can be chosen for the given data set, with potentially hundreds or thousands of method combinations in the whole data processing chain. Therefore, a clear understanding of these machine learning methods is helpful for selecting an appropriate method combination for obtaining stable and reliable analytical results of specific data. However, there has rarely been an overall introduction or evaluation of these methods based on multiclass metabolomic data. Herein, detailed descriptions of these machine learning methods in multiple data manipulation steps are reviewed. Moreover, an assessment of these methods was performed using a benchmark data set for multiclass metabolomics. First, 12 imputation methods for imputing missing values were evaluated based on the PSS (Procrustes statistical shape analysis) and NRMSE (normalized root-mean-square error) values. Second, 17 normalization methods for processing multiclass metabolomic data were evaluated by applying the PMAD (pooled median absolute deviation) value. Third, different methods of identifying markers of multiclass metabolomics were evaluated based on the CWrel (relative weighted consistency) value. Fourth, nine classification methods for constructing multiclass models were assessed using the AUC (area under the curve) value. Performance evaluations of machine learning methods are highly recommended to select the most appropriate method combination before performing the final analysis of the given data. Overall, detailed descriptions and evaluation of various machine learning methods are expected to improve analyses of multiclass metabolomic data.

Robust microarray data feature selection using a correntropy based distance metric learning approach

Classification of high-dimensional microarray data is a challenge in bioinformatics and genetic data processing. One of the challenging issues of feature selection is the presence of outliers. The Euclidean distance metric is sensitive to outliers. In this study, a distance metric learning based feature selection approach that uses the correntropy function as the discrimination metric is proposed. For this purpose, the metric learning problem is formulated as an optimization problem and solved using the Lagrange method. The output of the approach signifies the most important and robust features. After feature selection, different classification methods such as SVM, decision trees, and NN classifiers are used to investigate the classification accuracy of the proposed method as well as precision, recall, and F-measure. Experiments are carried out on 13 high-dimensional datasets and show that the proposed method outperforms the previous models in terms of accuracy and robustness.

Clustering ensemble in scRNA-seq data analysis: Methods, applications and challenges

With the rapid development of single-cell RNA-sequencing techniques, various computational methods and tools were proposed to analyze these high-throughput data, which led to an accelerated reveal of potential biological information. As one of the core steps of single-cell transcriptome data analysis, clustering plays a crucial role in identifying cell types and interpreting cellular heterogeneity. However, the results generated by different clustering methods showed distinguishing, and those unstable partitions can affect the accuracy of the analysis to a certain extent. To overcome this challenge and obtain more accurate results, currently clustering ensemble is frequently applied to cluster analysis of single-cell transcriptome datasets, and the results generated by all clustering ensembles are nearly more reliable than those from most of the single clustering partitions. In this review, we summarize applications and challenges of the clustering ensemble method in single-cell transcriptome data analysis, and provide constructive thoughts and references for researchers in this field.

Critical Assessment of the Biomarker Discovery and Classification Methods for Multiclass Metabolomics

Multiclass metabolomics has been widely applied in clinical practice to understand pathophysiological processes involved in disease progression and diagnostic biomarkers of various disorders. In contrast to the binary problem, the multiclass classification problem is more difficult in terms of obtaining reliable and stable results due to the increase in the complexity of determining exact class decision boundaries. In particular, methods of biomarker discovery and classification have a significant effect on the multiclass model because different methods with significantly varied theories produce conflicting results even for the same dataset. However, a systematic assessment for selecting the most appropriate methods of biomarker discovery and classification for multiclass metabolomics is still lacking. Therefore, a comprehensive assessment is essential to measure the suitability of methods in multiclass classification models from multiple perspectives. In this study, five biomarker discovery methods and nine classification methods were assessed based on four benchmark datasets of multiclass metabolomics. The performance assessment of the biomarker discovery and classification methods was performed using three evaluation criteria: assessment a (cluster analysis of sample grouping), assessment b (biomarker consistency in multiple subgroups), and assessment c (accuracy in the classification model). As a result, 13 combining strategies with superior performance were selected under multiple criteria based on these benchmark datasets. In conclusion, superior strategies that performed consistently well are suggested for the discovery of biomarkers and the construction of a classification model for multiclass metabolomics.

Deep learning automates bidimensional and volumetric tumor burden measurement from MRI in pre- and post-operative glioblastoma patients

Tumor burden assessment by magnetic resonance imaging (MRI) is central to the evaluation of treatment response for glioblastoma. This assessment is, however, complex to perform and associated with high variability due to the high heterogeneity and complexity of the disease. In this work, we tackle this issue and propose a deep learning pipeline for the fully automated end-to-end analysis of glioblastoma patients. Our approach simultaneously identifies tumor sub-regions, including the enhancing tumor, peritumoral edema and surgical cavity in the first step, and then calculates the volumetric and bidimensional measurements that follow the current Response Assessment in Neuro-Oncology (RANO) criteria. Also, we introduce a rigorous manual annotation process which was followed to delineate the tumor sub-regions by the human experts, and to capture their segmentation confidences that are later used while training deep learning models. The results of our extensive experimental study performed over 760 pre-operative and 504 post-operative adult patients with glioma obtained from the public database (acquired at 19 sites in years 2021-2020) and from a clinical treatment trial (47 and 69 sites for pre-/post-operative patients, 2009-2011) and backed up with thorough quantitative, qualitative and statistical analysis revealed that our pipeline performs accurate segmentation of pre- and post-operative MRIs in a fraction of the manual delineation time (up to 20 times faster than humans). Volumetric measurements were in strong agreement with experts with the Intraclass Correlation Coefficient (ICC): 0.959, 0.703, 0.960 for ET, ED, and cavity. Similarly, automated RANO compared favorably with experienced readers (ICC: 0.681 and 0.866) producing consistent and accurate results. Additionally, we showed that RANO measurements are not always sufficient to quantify tumor burden. The high performance of the automated tumor burden measurement highlights the potential of the tool for considerably improving and simplifying radiological evaluation of glioblastoma in clinical trials and clinical practice.

Revealing EXPH5 as a potential diagnostic gene biomarker of the late stage of COPD based on machine learning analysis

Chronic obstructive pulmonary disease is a kind of chronic lung disease characterized by persistent air flow obstruction, which was the third leading cause of death in China. The incidence of COPD is steadily and increasing and has been a globally sever disease. Accordingly, it is urgently needed to explore how to diagnose and treat COPD timely. This study aims to find key genes to diagnose COPD as soon as possible to avoid COPD processing and analyze immune cell infiltration between COPD early stage and late stage. Two GEO datasets were merged as the merge data for analyses. 157 DEGs were used for GSEA analysis to find the pathway between COPD early stage and late stage. Above all, gene EXPH5 stood out from the screen as the most likely candidate diagnosis biomarker of COPD indicating the late-stage by least LASSO and SVM-RFE. ROC curves of EXPH5 were applied to represent the discriminatory ability through the area under the curve which is the gold standard to evaluate the accuracy of diagnosis and survival rate. The CIBERSORT algorithm was used to assess the distribution of tissue-infiltrating immune cells between two COPD stages. The diagnosis biomarker, gene EXPH5 had a positive correlation with NK cells resting; mast cell resting, eosinophils, and negative correlation with T cell gamma delta, macrophages M1, which underscore the role of gene and immune cell infiltration. To make results more reliable, we further analyzed the gene EXPH5 expression in single-cell transcriptome data and showed again that EXPH5 genes significantly downregulated in the late stage of COPD especially in the main lung cell types AT1 and AT2. In a word, our study identified genes EXPH5 as a marker gene, which adds to the knowledge for clinical diagnosis and pharmaceutical design of COPD.

Integrated analysis of multi-omics data for the discovery of biomarkers and therapeutic targets for colorectal cancer

The considerable burden of colorectal cancer and the rising trend in young adults emphasize the necessity of understanding its underlying mechanisms, providing new diagnostic and prognostic markers, and improving therapeutic approaches. Precision medicine is a new trend all over the world and identification of novel biomarkers and therapeutic targets is a step forward towards this trend. In this context, multi-omics data and integrated analysis are being investigated to develop personalized medicine in the management of colorectal cancer. Given the large amount of data from multi-omics approach, data integration and analysis is a great challenge. In this Review, we summarize how statistical and machine learning techniques are applied to analyze multi-omics data and how it contributes to the discovery of useful diagnostic and prognostic biomarkers and therapeutic targets. Moreover, we discuss the importance of these biomarkers and therapeutic targets in the clinical management of colorectal cancer in the future. Taken together, integrated analysis of multi-omics data has great potential for finding novel diagnostic and prognostic biomarkers and therapeutic targets, however, there are still challenges to overcome in future studies.

Automatic diagnosis and grading of Prostate Cancer with weakly supervised learning on whole slide images

BACKGROUND

The workflow of prostate cancer diagnosis and grading is cumbersome and the results suffer from substantial inter-observer variability. Recent trials have shown potential in using machine learning to develop automated systems to address this challenge. Most automated deep learning systems for prostate cancer Gleason grading focused on supervised learning requiring demanding fine-grained pixel-level annotations.

METHODS

A weakly-supervised deep learning model with slide-level labels is presented in this study for the diagnosis and grading of prostate cancer with whole slide image (WSI). WSIs are first cropped into small patches and then processed with a deep learning model to extract patch-level features. A graph convolution network (GCN) is used to aggregate the features for classifications. Throughout the training process, the noisy labels are progressively filtered out to reduce inter-observer variations in clinical reports. Finally, multi-center independent test cohorts with 6,174 slides are collected to evaluate the prostate cancer diagnosis and grading performance of our model.

RESULTS

The cancer diagnosis (2-level classification) results on two external test sets (n= 4,675, n= 844) show an area under the receiver operating characteristic curve (AUC) of 0.985 and 0.986. The Gleason grading (6-level classification) results reach 0.931 quadratic weighted kappa on the internal test set (n= 531). It generalizes well on the external test dataset (n= 844) with 0.801 quadratic weighted kappa with the reference standard set independently. The model enables pathological meaningful interpretability by visualizing the most attended lesions which are highly consistent with expert annotations.

CONCLUSION

The proposed model incorporates a graph network in weakly supervised learning with only slide-level reports. A robust learning strategy is also employed to correct the label noise. It is highly accurate (>0.985 AUC for diagnosis) and also interpretable with intuitive heatmap visualization. It can be unified with a digital pathology pipeline to deliver prostate cancer metrics for a pathology report.

Omics-based identification of an NRF2-related auranofin resistance signature in cancer: Insights into drug repurposing

Auranofin is a thioredoxin reductase-1 inhibitor originally approved for the treatment of rheumatoid arthritis. Recently, auranofin has been repurposed as an anticancer drug, with pharmacological activity reported in multiple cancer types. In this study, we characterized transcriptional and genetic alterations associated with auranofin response in cancer. By integrating data from an auranofin cytotoxicity screen with transcriptome profiling of lung cancer cell lines, we identified an auranofin resistance signature comprising 29 genes, most of which are classical targets of the transcription factor NRF2, such as genes involved in glutathione metabolism (GCLC, GSR, SLC7A11) and thioredoxin system (TXN, TXNRD1). Pan-cancer analysis revealed that mutations in NRF2 pathway genes, namely KEAP1 and NFE2L2, are strongly associated with overexpression of the auranofin resistance gene set. By clustering cancer types based on auranofin resistance signature expression, hepatocellular carcinoma, and a subset of non-small cell lung cancer, head-neck squamous cell carcinoma, and esophageal cancer carrying NFE2L2/KEAP1 mutations were predicted resistant, whereas leukemia, lymphoma, and multiple myeloma were predicted sensitive to auranofin. Cell viability assays in a panel of 20 cancer cell lines confirmed the augmented sensitivity of hematological cancers to auranofin; an effect associated with dependence upon glutathione and decreased expression of NRF2 target genes involved in GSH synthesis and recycling (GCLC, GCLM and GSR) in these cancer types. In summary, the omics-based identification of sensitive/resistant cancers and genetic alterations associated with these phenotypes may guide an appropriate repurposing of auranofin in cancer therapy.

Identification of crucial genes for predicting the risk of atherosclerosis with system lupus erythematosus based on comprehensive bioinformatics analysis and machine learning

BACKGROUND

Systemic lupus erythematosus (SLE) has become a major public health problem over the years, and atherosclerosis (AS) is one of the main complications of SLE associated with serious cardiovascular consequences in this patient population. The present study aimed to identify potential biomarkers for SLE patients with AS.

METHODS

Five microarray datasets (GSE50772, GSE81622, GSE100927, GSE28829, GSE37356) were downloaded from the NCBI Gene Expression Omnibus database. The Limma package was used to identify differentially expressed genes (DEGs) in AS. Weighted gene coexpression network analysis (WGCNA) was used to identify significant module genes associated with SLE. Functional enrichment analysis, protein-protein interaction (PPI) network construction, and machine learning algorithms (least absolute shrinkage and selection operator (Lasso, Support Vector Machine-Recursive Feature Elimination (SVM-RFE), and random forest) were applied to identify hub genes. Subsequently, we generated a nomogram and receiver operating characteristic curve (ROC) for predicting the risk of AS in SLE patients. Finally, immune cell infiltrations were analyzed, and Consensus Cluster Analysis was conducted based on Single Sample Gene Set Enrichment Analysis (ssGSEA) scores.

RESULTS

Five hub genes (SPI1, MMP9, C1QA, CX3CR1, and MNDA) were identified and used to establish a nomogram that yielded a high predictive performance (area under the curve 0.900-0.981). Dysregulated immune cell infiltrations were found in AS, with positive correlations with the five hub genes. Consensus clustering showed that the optimal number of subtypes was 3. Compared to subtypes A and B, subtype C presented higher expression of the five hub genes, immune cell infiltration levels and immune checkpoint expression.

CONCLUSION

Our study systematically identified five candidate hub genes (SPI1, MMP9, C1QA, CX3CR1, MNDA) and established a nomogram that could predict the risk of AS with SLE using various bioinformatic analyses and machine learning algorithms. Our findings provide the foothold for future studies on potential crucial genes for AS in SLE patients. Additionally, the dysregulated immune cell proportions and immune checkpoint expressions in AS with SLE were identified.

Transcriptomic discovery of a theranostic signature (SERPINE1/MMP3/COL1A1/SPP1) for head and neck squamous cell carcinomas and identification of antrocinol as a candidate drug

Head and neck squamous cell carcinomas (HNSCC) are prevalent malignancies with a disappointing prognosis, necessitating the search for theranostic biomarkers for better management. Based on a meta-analysis of transcriptomic data containing ten clinical datasets of HNSCC and matched nonmalignant samples, we identified SERPINE1/MMP3/COL1A1/SPP1 as essential hub genes as the potential theranostic biomarkers. Our analysis suggests these hub genes are associated with the extracellular matrix, peptidoglycans, cell migration, wound-healing processes, complement and coagulation cascades, and the AGE-RAGE signaling pathway within the tumor microenvironment. Also, these hub genes were associated with tumor-immune infiltrating cells and immunosuppressive phenotypes of HNSCC. Further investigation of The Cancer Genome Atlas (TCGA) cohorts revealed that these hub genes were associated with staging, metastasis, and poor survival in HNSCC patients. Molecular docking simulations were performed to evaluate binding activities between the hub genes and antrocinol, a novel small-molecule derivative of an anticancer phytochemical antrocin previously discovered by our group. Antrocinol showed high affinities to MMP3 and COL1A1. Notably, antrocinol presented satisfactory drug-like and ADMET properties for therapeutic applications. These results hinted at the potential of antrocinol as an anti-HNSCC candidate via targeting MMP3 and COL1A1. In conclusion, we identified hub genes: SERPINE1/MMP3/COL1A1/SPP1 as potential diagnostic biomarkers and antrocinol as a potential new drug for HNSCC.

DrugMAP: molecular atlas and pharma-information of all drugs

The efficacy and safety of drugs are widely known to be determined by their interactions with multiple molecules of pharmacological importance, and it is therefore essential to systematically depict the molecular atlas and pharma-information of studied drugs. However, our understanding of such information is neither comprehensive nor precise, which necessitates the construction of a new database providing a network containing a large number of drugs and their interacting molecules. Here, a new database describing the molecular atlas and pharma-information of drugs (DrugMAP) was therefore constructed. It provides a comprehensive list of interacting molecules for >30 000 drugs/drug candidates, gives the differential expression patterns for >5000 interacting molecules among different disease sites, ADME (absorption, distribution, metabolism and excretion)-relevant organs and physiological tissues, and weaves a comprehensive and precise network containing >200 000 interactions among drugs and molecules. With the great efforts made to clarify the complex mechanism underlying drug pharmacokinetics and pharmacodynamics and rapidly emerging interests in artificial intelligence (AI)-based network analyses, DrugMAP is expected to become an indispensable supplement to existing databases to facilitate drug discovery. It is now fully and freely accessible at: https://idrblab.org/drugmap/.

A novel multi-class classification model for schizophrenia, bipolar disorder and healthy controls using comprehensive transcriptomic data

Two common psychiatric disorders, schizophrenia (SCZ) and bipolar disorder (BP), confer lifelong disability and collectively affect 2% of the world population. Because the diagnosis of psychiatry is based only on symptoms, developing more effective methods for the diagnosis of psychiatric disorders is a major international public health priority. Furthermore, SCZ and BP overlap considerably in terms of symptoms and risk genes. Therefore, the clarity of the underlying etiology and pathology remains lacking for these two disorders. Although many studies have been conducted, a classification model with higher accuracy and consistency was found to still be necessary for accurate diagnoses of SCZ and BP. In this study, a comprehensive dataset was combined from five independent transcriptomic studies. This dataset comprised 120 patients with SCZ, 101 patients with BP, and 149 healthy subjects. The partial least squares discriminant analysis (PLS-DA) method was applied to identify the gene signature among multiple groups, and 341 differentially expressed genes (DEGs) were identified. Then, the disease relevance of these DEGs was systematically performed, including (α) the great disease relevance of the identified signature, (β) the hub genes of the protein-protein interaction network playing a key role in psychiatric disorders, and (γ) gene ontology terms and enriched pathways playing a key role in psychiatric disorders. Finally, a popular multi-class classifier, support vector machine (SVM), was applied to construct a novel multi-class classification model using the identified signature for SCZ and BP. Using the independent test sets, the classification capacity of this multi-class model was assessed, which showed this model had a strong classification ability.

In-silico screening and in-vitro assay show the antiviral effect of Indomethacin against SARS-CoV-2

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has caused the worldwide spread of coronavirus disease 19 (COVID-19), and till now, it has caused death to more than 6.2 million people. Although various vaccines and drug candidates are being tested globally with limited to moderate success, a comprehensive therapeutic cure is yet to be achieved. In this study, we applied computational drug repurposing methods complemented with the analyses of the already existing gene expression data to find better therapeutics in treatment and recovery. Primarily, we identified the most crucial proteins of SARS-CoV-2 and host human cells responsible for viral infection and host response. An in-silico screening of the existing drugs was performed against the crucial proteins for SARS-CoV-2 infection, and a few existing drugs were shortlisted. Further, we analyzed the gene expression data of SARS-CoV-2 in human lung epithelial cells and investigated the molecules that can reverse the cellular mRNA expression profiles in the diseased state. LINCS L1000 and Comparative Toxicogenomics Database (CTD) were utilized to obtain two sets of compounds that can be used to counter SARS-CoV-2 infection from the gene expression perspective. Indomethacin, a nonsteroidal anti-inflammatory drug (NSAID), and Vitamin-A were found in two sets of compounds, and in the in-silico screening of existing drugs to treat SARS-CoV-2. Our in-silico findings on Indomethacin were further successfully validated by in-vitro testing in Vero CCL-81 cells with an IC₅₀ of 12 μM. Along with these findings, we briefly discuss the possible roles of Indomethacin and Vitamin-A to counter the SARS-CoV-2 infection in humans.

REGLIV: Molecular regulation data of diverse living systems facilitating current multiomics research

Multiomics is a powerful technique in molecular biology that facilitates the identification of new associations among different molecules (genes, proteins & metabolites). It has attracted tremendous research interest from the scientists worldwide and has led to an explosive number of published studies. Most of these studies are based on the regulation data provided in available databases. Therefore, it is essential to have molecular regulation data that are strictly validated in the living systems of various cell lines and in vivo models. However, no database has been developed yet to provide comprehensive molecular regulation information validated by living systems. Herein, a new database, Molecular Regulation Data of Living System Facilitating Multiomics Study (REGLIV) is introduced to describe various types of molecular regulation tested by the living systems. (1) A total of 2996 regulations describe the changes in 1109 metabolites triggered by alterations in 284 genes or proteins, and (2) 1179 regulations describe the variations in 926 proteins induced by 125 endogenous metabolites. Overall, REGLIV is unique in (a) providing the molecular regulation of a clearly defined regulatory direction other than simple correlation, (b) focusing on molecular regulations that are validated in a living system not simply in an in vitro test, and (c) describing the disease/tissue/species specific property underlying each regulation. Therefore, REGLIV has important implications for the future practice of not only multiomics, but also other fields relevant to molecular regulation. REGLIV is freely accessible at: https://idrblab.org/regliv/.

Biological activities of drug inactive ingredients

In a drug formulation (DFM), the major components by mass are not Active Pharmaceutical Ingredient (API) but rather Drug Inactive Ingredients (DIGs). DIGs can reach much higher concentrations than that achieved by API, which raises great concerns about their clinical toxicities. Therefore, the biological activities of DIG on physiologically relevant target are widely demanded by both clinical investigation and pharmaceutical industry. However, such activity data are not available in any existing pharmaceutical knowledge base, and their potentials in predicting the DIG-target interaction have not been evaluated yet. In this study, the comprehensive assessment and analysis on the biological activities of DIGs were therefore conducted. First, the largest number of DIGs and DFMs were systematically curated and confirmed based on all drugs approved by US Food and Drug Administration. Second, comprehensive activities for both DIGs and DFMs were provided for the first time to pharmaceutical community. Third, the biological targets of each DIG and formulation were fully referenced to available databases that described their pharmaceutical/biological characteristics. Finally, a variety of popular artificial intelligence techniques were used to assess the predictive potential of DIGs' activity data, which was the first evaluation on the possibility to predict DIG's activity. As the activities of DIGs are critical for current pharmaceutical studies, this work is expected to have significant implications for the future practice of drug discovery and precision medicine.

RGCNCDA: Relational graph convolutional network improves circRNA-disease association prediction by incorporating microRNAs

Recently, a large number of studies have indicated that circRNAs with covalently closed loops play important roles in biological processes and have potential as diagnostic biomarkers. Therefore, research on the circRNA-disease relationship is helpful in disease diagnosis and treatment. However, traditional biological verification methods require considerable labor and time costs. In this paper, we propose a new computational method (RGCNCDA) to predict circRNA-disease associations based on relational graph convolutional networks (R-GCNs). The method first integrates the circRNA similarity network, miRNA similarity network, disease similarity network and association networks among them to construct a global heterogeneous network. Then, it employs the random walk with restart (RWR) and principal component analysis (PCA) models to learn low-dimensional and high-order information from the global heterogeneous network as the topological features. Finally, a prediction model based on an R-GCN encoder and a DistMult decoder is built to predict the potential disease-associated circRNA. The predicted results demonstrate that RGCNCDA performs significantly better than the other six state-of-the-art methods in a 5-fold cross validation. Furthermore, the case study illustrates that RGCNCDA can effectively discover potential circRNA-disease associations.

PFmulDL: a novel strategy enabling multi-class and multi-label protein function annotation by integrating diverse deep learning methods

Bioinformatic annotation of protein function is essential but extremely sophisticated, which asks for extensive efforts to develop effective prediction method. However, the existing methods tend to amplify the representativeness of the families with large number of proteins by misclassifying the proteins in the families with small number of proteins. That is to say, the ability of the existing methods to annotate proteins in the 'rare classes' remains limited. Herein, a new protein function annotation strategy, PFmulDL, integrating multiple deep learning methods, was thus constructed. First, the recurrent neural network was integrated, for the first time, with the convolutional neural network to facilitate the function annotation. Second, a transfer learning method was introduced to the model construction for further improving the prediction performances. Third, based on the latest data of Gene Ontology, the newly constructed model could annotate the largest number of protein families comparing with the existing methods. Finally, this newly constructed model was found capable of significantly elevating the prediction performance for the 'rare classes' without sacrificing that for the 'major classes'. All in all, due to the emerging requirements on improving the prediction performance for the proteins in 'rare classes', this new strategy would become an essential complement to the existing methods for protein function prediction. All the models and source codes are freely available and open to all users at: https://github.com/idrblab/PFmulDL.

Elucidating the metabolic characteristics of pancreatic β-cells from patients with type 2 diabetes (T2D) using a genome-scale metabolic modeling

Diabetes is a global health problem caused primarily by the inability of pancreatic β-cells to secrete adequate insulin. Despite extensive research, the identity of factors contributing to the dysregulated metabolism-secretion coupling in the β-cells remains elusive. The present study attempts to capture some of these factors responsible for the impaired β-cell metabolism-secretion coupling that contributes to diabetes pathogenesis. The metabolic-flux profiles of pancreatic β-cells were predicted using genome-scale metabolic modeling for ten diabetic patients and ten control subjects. Analysis of these flux states shows reduction in the mitochondrial fatty acid oxidation and mitochondrial oxidative phosphorylation pathways, that leads to decreased insulin secretion in diabetes. We also observed elevated reactive oxygen species (ROS) generation through peroxisomal fatty acid β-oxidation. In addition, cellular antioxidant defense systems were found to be attenuated in diabetes. Our analysis also uncovered the possible changes in the plasma metabolites in diabetes due to the β-cells failure. These efforts subsequently led to the identification of seven metabolites associated with cardiovascular disease (CVD) pathogenesis, thus establishing its link as a secondary complication of diabetes.

POSREG: proteomic signature discovered by simultaneously optimizing its reproducibility and generalizability

Mass spectrometry-based proteomic technique has become indispensable in current exploration of complex and dynamic biological processes. Instrument development has largely ensured the effective production of proteomic data, which necessitates commensurate advances in statistical framework to discover the optimal proteomic signature. Current framework mainly emphasizes the generalizability of the identified signature in predicting the independent data but neglects the reproducibility among signatures identified from independently repeated trials on different sub-dataset. These problems seriously restricted the wide application of the proteomic technique in molecular biology and other related directions. Thus, it is crucial to enable the generalizable and reproducible discovery of the proteomic signature with the subsequent indication of phenotype association. However, no such tool has been developed and available yet. Herein, an online tool, POSREG, was therefore constructed to identify the optimal signature for a set of proteomic data. It works by (i) identifying the proteomic signature of good reproducibility and aggregating them to ensemble feature ranking by ensemble learning, (ii) assessing the generalizability of ensemble feature ranking to acquire the optimal signature and (iii) indicating the phenotype association of discovered signature. POSREG is unique in its capacity of discovering the proteomic signature by simultaneously optimizing its reproducibility and generalizability. It is now accessible free of charge without any registration or login requirement at https://idrblab.org/posreg/.

Molecular Mechanism for the Allosteric Inhibition of the Human Serotonin Transporter by Antidepressant Escitalopram

Human serotine transporter (hSERT) is one of the most influential drug targets, and its allosteric modulators (e.g., escitalopram) have emerged to be the next-generation medication for psychiatric disorders. However, the molecular mechanism underlying the allosteric modulation of hSERT is still elusive. Here, the simulation strategies of conventional (cMD) and steered (SMD) molecular dynamics were applied to investigate this molecular mechanism from distinct perspectives. First, cMD simulations revealed that escitalopram's binding to hSERT's allosteric site simultaneously enhanced its binding to the orthosteric site. Then, SMD simulation identified that the occupation of hSERT's allosteric site by escitalopram could also block its dissociation from the orthosteric site. Finally, by comparing the simulated structures of two hSERT-escitalopram complexes with and without allosteric modulation, a new conformational coupling between an extracellular (Arg104-Glu494) and an intracellular (Lys490-Glu494) salt bridge was identified. In summary, this study explored the mechanism underlying the allosteric modulation of hSERT by collectively applying two MD simulation strategies, which could facilitate our understanding of the allosteric modulations of not only hSERT but also other clinically important therapeutic targets.

RNA-RNA interactions between SARS-CoV-2 and host benefit viral development and evolution during COVID-19 infection

Some studies reported that genomic RNA of SARS-CoV-2 can absorb a few host miRNAs that regulate immune-related genes and then deprive their function. In this perspective, we conjecture that the absorption of the SARS-CoV-2 genome to host miRNAs is not a coincidence, which may be an indispensable approach leading to viral survival and development in host. In our study, we collected five datasets of miRNAs that were predicted to interact with the genome of SARS-CoV-2. The targets of these miRNAs in the five groups were consistently enriched immune-related pathways and virus-infectious diseases. Interestingly, the five datasets shared no one miRNA but their targets shared 168 genes. The signaling pathway enrichment of 168 shared targets implied an unbalanced immune response that the most of interleukin signaling pathways and none of the interferon signaling pathways were significantly different. Protein-protein interaction (PPI) network using the shared targets showed that PPI pairs, including IL6-IL6R, were related to the process of SARS-CoV-2 infection and pathogenesis. In addition, we found that SARS-CoV-2 absorption to host miRNA could benefit two popular mutant strains for more infectivity and pathogenicity. Conclusively, our results suggest that genomic RNA absorption to host miRNAs may be a vital approach by which SARS-CoV-2 disturbs the host immune system and infects host cells.

Construction of the Classification Model Using Key Genes Identified Between Benign and Malignant Thyroid Nodules From Comprehensive Transcriptomic Data

Thyroid nodules are present in upto 50% of the population worldwide, and thyroid malignancy occurs in only 5–15% of nodules. Until now, fine-needle biopsy with cytologic evaluation remains the diagnostic choice to determine the risk of malignancy, yet it fails to discriminate as benign or malignant in one-third of cases. In order to improve the diagnostic accuracy and reliability, molecular testing based on transcriptomic data has developed rapidly. However, gene signatures of thyroid nodules identified in a plenty of transcriptomic studies are highly inconsistent and extremely difficult to be applied in clinical application. Therefore, it is highly necessary to identify consistent signatures to discriminate benign or malignant thyroid nodules. In this study, five independent transcriptomic studies were combined to discover the gene signature between benign and malignant thyroid nodules. This combined dataset comprises 150 malignant and 93 benign thyroid samples. Then, there were 279 differentially expressed genes (DEGs) discovered by the feature selection method (Student’s t test and fold change). And the weighted gene co-expression network analysis (WGCNA) was performed to identify the modules of highly co-expressed genes, and 454 genes in the gray module were discovered as the hub genes. The intersection between DEGs by the feature selection method and hub genes in the WGCNA model was identified as the key genes for thyroid nodules. Finally, four key genes (ST3GAL5, NRCAM, MT1F, and PROS1) participated in the pathogenesis of malignant thyroid nodules were validated using an independent dataset. Moreover, a high-performance classification model for discriminating thyroid nodules was constructed using these key genes. All in all, this study might provide a new insight into the key differentiation of benign and malignant thyroid nodules.

Optimization of metabolomic data processing using NOREVA

A typical output of a metabolomic experiment is a peak table corresponding to the intensity of measured signals. Peak table processing, an essential procedure in metabolomics, is characterized by its study dependency and combinatorial diversity. While various methods and tools have been developed to facilitate metabolomic data processing, it is challenging to determine which processing workflow will give good performance for a specific metabolomic study. NOREVA, an out-of-the-box protocol, was therefore developed to meet this challenge. First, the peak table is subjected to many processing workflows that consist of three to five defined calculations in combinatorially determined sequences. Second, the results of each workflow are judged against objective performance criteria. Third, various benchmarks are analyzed to highlight the uniqueness of this newly developed protocol in (1) evaluating the processing performance based on multiple criteria, (2) optimizing data processing by scanning thousands of workflows, and (3) allowing data processing for time-course and multiclass metabolomics. This protocol is implemented in an R package for convenient accessibility and to protect users' data privacy. Preliminary experience in R language would facilitate the usage of this protocol, and the execution time may vary from several minutes to a couple of hours depending on the size of the analyzed data.

Antioxidant Activity and the Potential Mechanism of the Fruit From Ailanthus altissima Swingle

The fruits of Ailanthus altissima Swingle (AS) possess a variety of pharmacological activities. Its antioxidant activity and the potential mode of action have not yet been investigated. In in vitro studies, AS revealed the strong reducing power and DPPH scavenging effect, but hydroxyl radical scavenging activity and ferrous ions-chelating ability were not strong. Meanwhile, the oxidative stress RAW264.7 cell injury model was established, the low and medium-doses of AS showed significant protective effects on the viability of H₂O₂-treated cells by CCK-8 method. Besides, three doses of AS all increased the activities of SOD, CAT, and GSH-Px and decreased the MDA level compared with the H₂O₂ group, suggesting it significantly relieved oxidative stress of cells. The active ingredients and related targets of AS were collected by HERB and Swiss Target Prediction database, the common targets of drugs and diseases database were conducted by GeneCards database platform and the Venny platform. We screened the core targets of AS like threonine kinase1 (AKT1), mitogen-activated protein kinase 1 (MAPK1), sirtuin-1 (SIRT1), mechanistic target of rapamycin kinase (MTOR) by STRING database, and the key pathways involved PI3K-AKT and FoxO signaling pathway by KEGG pathway enrichment analysis. Besides, qRT-PCR revealed AS preconditioning significantly up-regulated the expression level of AKT1, SIRT1, MAPK1, and MTOR in model cells, and the effect was related to the regulation of FoxO and PI3K/AKT signaling pathway. In summary, AS showed significant antioxidant activity and its potential mechanism was regulating FoxO and PI3K/AKT signaling pathway.

Genomic Variation Prediction: A Summary From Different Views

Structural variations in the genome are closely related to human health and the occurrence and development of various diseases. To understand the mechanisms of diseases, find pathogenic targets, and carry out personalized precision medicine, it is critical to detect such variations. The rapid development of high-throughput sequencing technologies has accelerated the accumulation of large amounts of genomic mutation data, including synonymous mutations. Identifying pathogenic synonymous mutations that play important roles in the occurrence and development of diseases from all the available mutation data is of great importance. In this paper, machine learning theories and methods are reviewed, efficient and accurate pathogenic synonymous mutation prediction methods are developed, and a standardized three-level variant analysis framework is constructed. In addition, multiple variation tolerance prediction models are studied and integrated, and new ideas for structural variation detection based on deep information mining are explored.

VARIDT 2.0: structural variability of drug transporter

The structural variability data of drug transporter (DT) are key for research on precision medicine and rational drug use. However, these valuable data are not sufficiently covered by the available databases. In this study, a major update of VARIDT (a database previously constructed to provide DTs' variability data) was thus described. First, the experimentally resolved structures of all DTs reported in the original VARIDT were discovered from PubMed and Protein Data Bank. Second, the structural variability data of each DT were collected by literature review, which included: (a) mutation-induced spatial variations in folded state, (b) difference among DT structures of human and model organisms, (c) outward/inward-facing DT conformations and (d) xenobiotics-driven alterations in the 3D complexes. Third, for those DTs without experimentally resolved structural variabilities, homology modeling was further applied as well-established protocol to enrich such valuable data. As a result, 145 mutation-induced spatial variations of 42 DTs, 1622 inter-species structures originating from 292 DTs, 118 outward/inward-facing conformations belonging to 59 DTs, and 822 xenobiotics-regulated structures in complex with 57 DTs were updated to VARIDT (https://idrblab.org/varidt/ and http://varidt.idrblab.net/). All in all, the newly collected structural variabilities will be indispensable for explaining drug sensitivity/selectivity, bridging preclinical research with clinical trial, revealing the mechanism underlying drug-drug interaction, and so on.

Prospects and challenges of cancer systems medicine: from genes to disease networks

It is becoming evident that holistic perspectives toward cancer are crucial in deciphering the overwhelming complexity of tumors. Single-layer analysis of genome-wide data has greatly contributed to our understanding of cellular systems and their perturbations. However, fundamental gaps in our knowledge persist and hamper the design of effective interventions. It is becoming more apparent than ever, that cancer should not only be viewed as a disease of the genome but as a disease of the cellular system. Integrative multilayer approaches are emerging as vigorous assets in our endeavors to achieve systemic views on cancer biology. Herein, we provide a comprehensive review of the approaches, methods and technologies that can serve to achieve systemic perspectives of cancer. We start with genome-wide single-layer approaches of omics analyses of cellular systems and move on to multilayer integrative approaches in which in-depth descriptions of proteogenomics and network-based data analysis are provided. Proteogenomics is a remarkable example of how the integration of multiple levels of information can reduce our blind spots and increase the accuracy and reliability of our interpretations and network-based data analysis is a major approach for data interpretation and a robust scaffold for data integration and modeling. Overall, this review aims to increase cross-field awareness of the approaches and challenges regarding the omics-based study of cancer and to facilitate the necessary shift toward holistic approaches.

Feature, Function, and Information of Drug Transporter Related Databases

With the rapid progress in pharmaceutical experiments and clinical investigations, extensive knowledge of drug transporters (DTs) has accumulated, which is valuable data for the understanding of drug metabolism and disposition. However, such data is largely dispersed in the literature, which hampers its utility and significantly limits its possibility for comprehensive analysis. A variety of databases have, therefore, been constructed to provide DT-related data, and they were reviewed in this study. First, several knowledge bases providing data regarding clinically important drugs and their corresponding transporters were discussed, which constituted the most important resources of DT-centered data. Second, some databases describing the general transporters and their functional families were reviewed. Third, various databases offering transporter information as part of their entire data collection were described. Finally, customized database functions that are available to facilitate DT-related research were discussed. This review provided an overview of the whole collection of DT-related databases, which might facilitate research on precision medicine and rational drug use. Significance Statement A collection of well-established databases related to DTs were comprehensively reviewed, which were organized according to their importance in drug ADME research. These databases could collectively contribute to the research on rational drug use.

Subtype-selective mechanisms of negative allosteric modulators binding to group I metabotropic glutamate receptors

Group I metabotropic glutamate receptors (mGlu₁ and mGlu₅) are promising targets for multiple psychiatric and neurodegenerative disorders. Understanding the subtype selectivity of mGlu₁ and mGlu₅ allosteric sites is essential for the rational design of novel modulators with single- or dual-target mechanism of action. In this study, starting from the deposited mGlu₁ and mGlu₅ crystal structures, we utilized computational modeling approaches integrating docking, molecular dynamics simulation, and efficient post-trajectory analysis to reveal the subtype-selective mechanism of mGlu₁ and mGlu₅ to 10 diverse drug scaffolds representing known negative allosteric modulators (NAMs) in the literature. The results of modeling identified six pairs of non-conserved residues and four pairs of conserved ones as critical features to distinguish the selective NAMs binding to the corresponding receptors. In addition, nine pairs of residues are beneficial to the development of novel dual-target NAMs of group I metabotropic glutamate receptors. Furthermore, the binding modes of a reported dual-target NAM (VU0467558) in mGlu₁ and mGlu₅ were predicted to verify the identified residues that play key roles in the receptor selectivity and the dual-target binding. The results of this study can guide rational structure-based design of novel NAMs, and the approach can be generally applicable to characterize the features of selectivity for other G-protein-coupled receptors.

CWLy-RF: A novel approach for identifying cell wall lyases based on random forest classifier

Drug resistance of pathogenic bacteria has become increasingly serious due to the abuse of antibiotics in recent years. Researchers have found that cell wall lyases are effective antibacterial agents that can specifically recognize target bacteria and degrade bacterial peptidoglycan. Traditional wet experiments are usually expensive, time-consuming and laborious for the identification of lyases. Therefore, there is an urgent need to develop prediction tools based on computer methods to identify lyases quickly and accurately. In this paper, a new predictor, CWLy-RF, is proposed based on the random forest (RF) algorithm to identify cell wall lyases. In this method, we combined three features, namely, 400D, 188D and the composition of k-spaced amino acid group pairs, using mixed-feature representation methods. Afterward, we improved the feature representation ability with the selected top 100 features by using the information gain method and trained a predictive model using RF. The constructed prediction model is evaluated by using 10-fold cross-validation. The accuracy obtained was 96.09%, the AUC was 0.993, the MCC was 0.922, the sensitivity was 94.92%, and the specificity was 97.32%. We have proved that the proposed predictor CWLy-RF is superior to other latest models, and it will hopefully become an effective and useful tool for identifying lyases.

Pharmacometabonomics: data processing and statistical analysis

Individual variations in drug efficacy, side effects and adverse drug reactions are still challenging that cannot be ignored in drug research and development. The aim of pharmacometabonomics is to better understand the pharmacokinetic properties of drugs and monitor the drug effects on specific metabolic pathways. Here, we systematically reviewed the recent technological advances in pharmacometabonomics for better understanding the pathophysiological mechanisms of diseases as well as the metabolic effects of drugs on bodies. First, the advantages and disadvantages of all mainstream analytical techniques were compared. Second, many data processing strategies including filtering, missing value imputation, quality control-based correction, transformation, normalization together with the methods implemented in each step were discussed. Third, various feature selection and feature extraction algorithms commonly applied in pharmacometabonomics were described. Finally, the databases that facilitate current pharmacometabonomics were collected and discussed. All in all, this review provided guidance for researchers engaged in pharmacometabonomics and metabolomics, and it would promote the wide application of metabolomics in drug research and personalized medicine.

Identification of long noncoding RNAs with machine learning methods: a review

Long noncoding RNAs (lncRNAs) are noncoding RNAs with a length greater than 200 nucleotides. Studies have shown that they play an important role in many life activities. Dozens of lncRNAs have been characterized to some extent, and they are reported to be related to the development of diseases in a variety of cells. However, the biological functions of most lncRNAs are currently still unclear. Therefore, accurately identifying and predicting lncRNAs would be helpful for research on their biological functions. Due to the disadvantages of high cost and high resource-intensiveness of experimental methods, scientists have developed numerous computational methods to identify and predict lncRNAs in recent years. In this paper, we systematically summarize the machine learning-based lncRNAs prediction tools from several perspectives, and discuss the challenges and prospects for the future work.

The miRNA: a small but powerful RNA for COVID-19

Coronavirus disease 2019 (COVID-19) caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is a severe and rapidly evolving epidemic. Now, although a few drugs and vaccines have been proved for its treatment and prevention, little systematic comments are made to explain its susceptibility to humans. A few scattered studies used bioinformatics methods to explore the role of microRNA (miRNA) in COVID-19 infection. Combining these timely reports and previous studies about virus and miRNA, we comb through the available clues and seemingly make the perspective reasonable that the COVID-19 cleverly exploits the interplay between the small miRNA and other biomolecules to avoid being effectively recognized and attacked from host immune protection as well to deactivate functional genes that are crucial for immune system. In detail, SARS-CoV-2 can be regarded as a sponge to adsorb host immune-related miRNA, which forces host fall into dysfunction status of immune system. Besides, SARS-CoV-2 encodes its own miRNAs, which can enter host cell and are not perceived by the host's immune system, subsequently targeting host function genes to cause illnesses. Therefore, this article presents a reasonable viewpoint that the miRNA-based interplays between the host and SARS-CoV-2 may be the primary cause that SARS-CoV-2 accesses and attacks the host cells.

Recent Advances in Predicting Protein S-Nitrosylation Sites

Protein S-nitrosylation (SNO) is a process of covalent modification of nitric oxide (NO) and its derivatives and cysteine residues. SNO plays an essential role in reversible posttranslational modifications of proteins. The accurate prediction of SNO sites is crucial in revealing a certain biological mechanism of NO regulation and related drug development. Identification of the sites of SNO in proteins is currently a very hot topic. In this review, we briefly summarize recent advances in computationally identifying SNO sites. The challenges and future perspectives for identifying SNO sites are also discussed. We anticipate that this review will provide insights into research on SNO site prediction.

Genome-Wide Analysis of LysM-Containing Gene Family in Wheat: Structural and Phylogenetic Analysis during Development and Defense

The lysin motif (LysM) family comprise a number of defense proteins that play important roles in plant immunity. The LysM family includes LysM-containing receptor-like proteins (LYP) and LysM-containing receptor-like kinase (LYK). LysM generally recognizes the chitin and peptidoglycan derived from bacteria and fungi. Approximately 4000 proteins with the lysin motif (Pfam PF01476) are found in prokaryotes and eukaryotes. Our study identified 57 LysM genes and 60 LysM proteins in wheat and renamed these genes and proteins based on chromosome distribution. According to the phylogenetic and gene structure of intron-exon distribution analysis, the 60 LysM proteins were classified into seven groups. Gene duplication events had occurred among the LysM family members during the evolution process, resulting in an increase in the LysM gene family. Synteny analysis suggested the characteristics of evolution of the LysM family in wheat and other species. Systematic analysis of these species provided a foundation of LysM genes in crop defense. A comprehensive analysis of the expression and cis-elements of LysM gene family members suggested that they play an essential role in defending against plant pathogens. The present study provides an overview of the LysM family in the wheat genome as well as information on systematic, phylogenetic, gene duplication, and intron-exon distribution analyses that will be helpful for future functional analysis of this important protein family, especially in Gramineae species.

Computational advances of tumor marker selection and sample classification in cancer proteomics

Cancer proteomics has become a powerful technique for characterizing the protein markers driving transformation of malignancy, tracing proteome variation triggered by therapeutics, and discovering the novel targets and drugs for the treatment of oncologic diseases. To facilitate cancer diagnosis/prognosis and accelerate drug target discovery, a variety of methods for tumor marker identification and sample classification have been developed and successfully applied to cancer proteomic studies. This review article describes the most recent advances in those various approaches together with their current applications in cancer-related studies. Firstly, a number of popular feature selection methods are overviewed with objective evaluation on their advantages and disadvantages. Secondly, these methods are grouped into three major classes based on their underlying algorithms. Finally, a variety of sample separation algorithms are discussed. This review provides a comprehensive overview of the advances on tumor maker identification and patients/samples/tissues separations, which could be guidance to the researches in cancer proteomics.

NOREVA: enhanced normalization and evaluation of time-course and multi-class metabolomic data

Biological processes (like microbial growth & physiological response) are usually dynamic and require the monitoring of metabolic variation at different time-points. Moreover, there is clear shift from case-control (N=2) study to multi-class (N>2) problem in current metabolomics, which is crucial for revealing the mechanisms underlying certain physiological process, disease metastasis, etc. These time-course and multi-class metabolomics have attracted great attention, and data normalization is essential for removing unwanted biological/experimental variations in these studies. However, no tool (including NOREVA 1.0 focusing only on case-control studies) is available for effectively assessing the performance of normalization method on time-course/multi-class metabolomic data. Thus, NOREVA was updated to version 2.0 by (i) realizing normalization and evaluation of both time-course and multi-class metabolomic data, (ii) integrating 144 normalization methods of a recently proposed combination strategy and (iii) identifying the well-performing methods by comprehensively assessing the largest set of normalizations (168 in total, significantly larger than those 24 in NOREVA 1.0). The significance of this update was extensively validated by case studies on benchmark datasets. All in all, NOREVA 2.0 is distinguished for its capability in identifying well-performing normalization method(s) for time-course and multi-class metabolomics, which makes it an indispensable complement to other available tools. NOREVA can be accessed at https://idrblab.org/noreva/.

A Cancer Gene Module Mining Method Based on Bio-Network of Multi-Omics Gene Groups

The initiation, promotion and progression of cancer are highly associated to the environment a human lives in as well as individual genetic factors. In view of the dangers to life and health caused by this abnormally complex systemic disease, many top scientific research institutions around the world have been actively carrying out research in order to discover the pathogenic mechanisms driving cancer occurrence and development. The emergence of high-throughput sequencing technology has greatly advanced oncology research and given rise to the revelation of important oncogenes and the interrelationship among them. Here, we have studied heterogeneous multi-level data within a context of integrated data, and scientifically introduced lncRNA omics data to construct multi-omics bio-network models, allowing the screening of key cancer-related gene groups. We propose a compactness clustering algorithm based on corrected cumulative rank scores, which uses the functional similarity between groups of genes as a distance measure to excavate key gene modules for abnormal regulation contained in gene groups through clustering. We also conducted a survival analysis using our results and found that our model could divide groups of different levels very well. The results also demonstrate that the integration of multi-omics biological data, key gene modules and their dysregulated gene groups can be discovered, which is crucial for cancer research.

The mechanistic, diagnostic and therapeutic novel nucleic acids for hepatocellular carcinoma emerging in past score years

Despite The Central Dogma states the destiny of gene as 'DNA makes RNA and RNA makes protein', the nucleic acids not only store and transmit genetic information but also, surprisingly, join in intracellular vital movement as a regulator of gene expression. Bioinformatics has contributed to knowledge for a series of emerging novel nucleic acids molecules. For typical cases, microRNA (miRNA), long noncoding RNA (lncRNA) and circular RNA (circRNA) exert crucial role in regulating vital biological processes, especially in malignant diseases. Due to extraordinarily heterogeneity among all malignancies, hepatocellular carcinoma (HCC) has emerged enormous limitation in diagnosis and therapy. Mechanistic, diagnostic and therapeutic nucleic acids for HCC emerging in past score years have been systematically reviewed. Particularly, we have organized recent advances on nucleic acids of HCC into three facets: (i) summarizing diverse nucleic acids and their modification (miRNA, lncRNA, circRNA, circulating tumor DNA and DNA methylation) acting as potential biomarkers in HCC diagnosis; (ii) concluding different patterns of three key noncoding RNAs (miRNA, lncRNA and circRNA) in gene regulation and (iii) outlining the progress of these novel nucleic acids for HCC diagnosis and therapy in clinical trials, and discuss their possibility for clinical applications. All in all, this review takes a detailed look at the advances of novel nucleic acids from potential of biomarkers and elaboration of mechanism to early clinical application in past 20 years.

PSBP-SVM: A Machine Learning-Based Computational Identifier for Predicting Polystyrene Binding Peptides

Polystyrene binding peptides (PSBPs) play a key role in the immobilization process. The correct identification of PSBPs is the first step of all related works. In this paper, we proposed a novel support vector machine-based bioinformatic identification model. This model contains four machine learning steps, including feature extraction, feature selection, model training and optimization. In a five-fold cross validation test, this model achieves 90.38, 84.62, 87.50, and 0.90% SN, SP, ACC, and AUC, respectively. The performance of this model outperforms the state-of-the-art identifier in terms of the SN and ACC with a smaller feature set. Furthermore, we constructed a web server that includes the proposed model, which is freely accessible at http://server.malab.cn/PSBP-SVM/index.jsp.