Briefings in Bioinformatics

Article	Citations
oncoPredict: an R package for predicting in vivo or cancer patient drug response and biomarkers from cell line screening data	725
BioGPT: generative pre-trained transformer for biomedical text generation and mining	280
NetCoMi: network construction and comparison for microbiome data in R	262
Multimodal deep learning for biomedical data fusion: a review	194
CellTalkDB: a manually curated database of ligand–receptor interactions in humans and mice	178
Next generation sequencing of SARS-CoV-2 genomes: challenges, applications and opportunities	159
AlgPred 2.0: an improved method for predicting allergenic proteins and mapping of IgE epitopes	157
A deep learning method for predicting metabolite–disease associations via graph neural network	157
Predicting the potential human lncRNA–miRNA interactions based on graph convolution network with conditional random field	150
GSCA: an integrated platform for gene set cancer analysis at genomic, pharmacogenomic and immunogenomic levels	139
A roadmap for multi-omics data integration using deep learning	138
Biological network analysis with deep learning	136
Exploration of natural compounds with anti-SARS-CoV-2 activityviainhibition of SARS-CoV-2 Mpro	130
Detection algorithms and attentive points of safety signal using spontaneous reporting systems as a clinical data source	128
DSTG: deconvoluting spatial transcriptomics data through graph-based artificial intelligence	127
A review on drug repurposing applicable to COVID-19	123
Hiplot: a comprehensive and easy-to-use web service for boosting publication-ready biomedical data visualization	121
Utilizing graph machine learning within drug discovery and development	117
Circular RNAs and complex diseases: from experimental results to computational models	115
SSI–DDI: substructure–substructure interactions for drug–drug interaction prediction	113
The miRNA: a small but powerful RNA for COVID-19	111
Venn diagrams in bioinformatics	110
Tumor immune microenvironment lncRNAs	109
Artificial intelligence in the prediction of protein–ligand interactions: recent advances and future directions	108
A transformer architecture based on BERT and 2D convolutional neural network to identify DNA enhancers from sequence information	107

Computational strategies to combat COVID-19: useful tools to accelerate SARS-CoV-2 and coronavirus research	106
An end-to-end heterogeneous graph representation learning-based framework for drug–target interaction prediction	104
A survey on computational models for predicting protein–protein interactions	102
Network Pharmacology and bioinformatics analyses identify intersection genes of niacin and COVID-19 as potential therapeutic targets	100
ggmsa: a visual exploration tool for multiple sequence alignment and associated data	98
Current challenges for unseen-epitope TCR interaction prediction and a new perspective derived from image classification	97
Graph representation learning in bioinformatics: trends, methods and applications	97
Drug repositioning based on the heterogeneous information fusion graph convolutional network	97
ToxinPred2: an improved method for predicting toxicity of proteins	95
StackIL6: a stacking ensemble model for improving the prediction of IL-6 inducing peptides	94
Anticancer peptides prediction with deep representation learning features	92
Do we need different machine learning algorithms for QSAR modeling? A comprehensive assessment of 16 machine learning algorithms on 14 QSAR data sets	91
Multi-view Multichannel Attention Graph Convolutional Network for miRNA–disease association prediction	89
DeepDDS: deep graph neural network with attention mechanism to predict synergistic drug combinations	87
Pan-cancer analysis of NLRP3 inflammasome with potential implications in prognosis and immunotherapy in human cancer	87
Machine learning revealed stemness features and a novel stemness-based classification with appealing implications in discriminating the prognosis, immunotherapy and temozolomide responses of 906 gliob	85
Inferring microenvironmental regulation of gene expression from single-cell RNA sequencing data using scMLnet with an application to COVID-19	85
POSREG: proteomic signature discovered by simultaneously optimizing its reproducibility and generalizability	84
A weighted bilinear neural collaborative filtering approach for drug repositioning	83
MG-BERT: leveraging unsupervised atomic representation learning for molecular property prediction	81
Attention is all you need: utilizing attention in AI-enabled drug discovery	81
Computational prediction and interpretation of cell-specific replication origin sites from multiple eukaryotes by exploiting stacking framework	80
PharmKG: a dedicated knowledge graph benchmark for bomedical data mining	80
Health informatics and EHR to support clinical research in the COVID-19 pandemic: an overview	78
Predicting protein stability changes upon single-point mutation: a thorough comparison of the available tools on a new dataset	77
Virtual screening and molecular dynamics simulation study of plant-derived compounds to identify potential inhibitors of main protease from SARS-CoV-2	77
Molecular design in drug discovery: a comprehensive review of deep generative models	77
Comprehensive assessment of machine learning-based methods for predicting antimicrobial peptides	77
DeepDTAF: a deep learning method to predict protein–ligand binding affinity	77
Text mining approaches for dealing with the rapidly expanding literature on COVID-19	76
Bioinformatics and machine learning approach identifies potential drug targets and pathways in COVID-19	76
Predicting metabolite–disease associations based on auto-encoder and non-negative matrix factorization	74
Artificial intelligence in drug discovery: applications and techniques	74
HINGRL: predicting drug–disease associations with graph representation learning on heterogeneous information networks	73
Network-based modeling of herb combinations in traditional Chinese medicine	73
MDF-SA-DDI: predicting drug–drug interaction events based on multi-source drug fusion, multi-source feature fusion and transformer self-attention mechanism	73
Benchmark of filter methods for feature selection in high-dimensional gene expression survival data	73
An effective self-supervised framework for learning expressive molecular global representations to drug discovery	73
Identification of miRNA–disease associations via deep forest ensemble learning based on autoencoder	72
A review on longitudinal data analysis with random forest	72
FitDock: protein–ligand docking by template fitting	69
Opportunities and challenges for ChatGPT and large language models in biomedicine and health	68
Clinical significance and immunogenomic landscape analyses of the immune cell signature based prognostic model for patients with breast cancer	68
Pharmacoinformatics and molecular dynamics simulation-based phytochemical screening of neem plant (Azadiractha indica) against human cancer by targeting MCM7 protein	67
Machine learning meets omics: applications and perspectives	67
Deep-joint-learning analysis model of single cell transcriptome and open chromatin accessibility data	67
Identifying the natural polyphenol catechin as a multi-targeted agent against SARS-CoV-2 for the plausible therapy of COVID-19: an integrated computational approach	67
Computational drug repositioning based on multi-similarities bilinear matrix factorization	66
A simple guide to de novo transcriptome assembly and annotation	66
FP-GNN: a versatile deep learning architecture for enhanced molecular property prediction	65

Comprehensive investigation of pathway enrichment methods for functional interpretation of LC–MS global metabolomics data	65
Deep learning methods for biomedical named entity recognition: a survey and qualitative comparison	64
NeuroPred-FRL: an interpretable prediction model for identifying neuropeptide using feature representation learning	63
DeepYY1: a deep learning approach to identify YY1-mediated chromatin loops	63
A protocol for dynamic model calibration	62
Deep-ABPpred: identifying antibacterial peptides in protein sequences using bidirectional LSTM with word2vec	62
AlphaFold2-aware protein–DNA binding site prediction using graph transformer	62
Deep drug-target binding affinity prediction with multiple attention blocks	62
Learning spatial structures of proteins improves protein–protein interaction prediction	62
Application of artificial intelligence and machine learning for COVID-19 drug discovery and vaccine design	61
Bioinformatics and system biology approach to identify the influences of SARS-CoV-2 infections to idiopathic pulmonary fibrosis and chronic obstructive pulmonary disease patients	61
DeepImmuno: deep learning-empowered prediction and generation of immunogenic peptides for T-cell immunity	61
Machine learning approach to gene essentiality prediction: a review	60
Updated review of advances in microRNAs and complex diseases: taxonomy, trends and challenges of computational models	59
A heterogeneous network embedding framework for predicting similarity-based drug-target interactions	59
A novel antibacterial peptide recognition algorithm based on BERT	59
Machine learning methods, databases and tools for drug combination prediction	59
CAMOIP: a web server for comprehensive analysis on multi-omics of immunotherapy in pan-cancer	58
ITP-Pred: an interpretable method for predicting, therapeutic peptides with fused features low-dimension representation	58
A comprehensive overview and critical evaluation of gene regulatory network inference technologies	58
Improving cancer driver gene identification using multi-task learning on graph convolutional network	58
Evaluating the state of the art in missing data imputation for clinical data	58
m6A regulator-mediated methylation modification patterns and characteristics of immunity and stemness in low-grade glioma	58
Prediction of anticancer peptides based on an ensemble model of deep learning and machine learning using ordinal positional encoding	58
A deep learning method for repurposing antiviral drugs against new viruses via multi-view nonnegative matrix factorization and its application to SARS-CoV-2	58
A molecular modelling approach for identifying antiviral selenium-containing heterocyclic compounds that inhibit the main protease of SARS-CoV-2: an in silico investigation	57
Comparative analysis of molecular fingerprints in prediction of drug combination effects	57
Accurate protein function prediction via graph attention networks with predicted structure information	57
Drug–drug interaction prediction with learnable size-adaptive molecular substructures	57
GAERF: predicting lncRNA-disease associations by graph auto-encoder and random forest	57
FusionDTA: attention-based feature polymerizer and knowledge distillation for drug-target binding affinity prediction	57
Exploring associations of non-coding RNAs in human diseases via three-matrix factorization with hypergraph-regular terms on center kernel alignment	57
Prediction and collection of protein–metabolite interactions	57
ATSE: a peptide toxicity predictor by exploiting structural and evolutionary information based on graph neural network and attention mechanism	56
Cell–cell communication inference and analysis in the tumour microenvironments from single-cell transcriptomics: data resources and computational strategies	56
Attention-based Knowledge Graph Representation Learning for Predicting Drug-drug Interactions	55
Accurate and fast cell marker gene identification with COSG	55
DLpTCR: an ensemble deep learning framework for predicting immunogenic peptide recognized by T cell receptor	54
fastDRH: a webserver to predict and analyze protein–ligand complexes based on molecular docking and MM/PB(GB)SA computation	54
Spatial transcriptomics prediction from histology jointly through Transformer and graph neural networks	54
PhaTYP: predicting the lifestyle for bacteriophages using BERT	54
Improving protein–ligand docking and screening accuracies by incorporating a scoring function correction term	54
Comparative studies of AlphaFold, RoseTTAFold and Modeller: a case study involving the use of G-protein-coupled receptors	54
BioRED: a rich biomedical relation extraction dataset	53
Multi-omics approaches for revealing the complexity of cardiovascular disease	53
LSTM-PHV: prediction of human-virus protein–protein interactions by LSTM with word2vec	53
MDA-GCNFTG: identifying miRNA-disease associations based on graph convolutional networks via graph sampling through the feature and topology graph	53
Large-scale comparative review and assessment of computational methods for anti-cancer peptide identification	53
Deep learning in retrosynthesis planning: datasets, models and tools	53
HVIDB: a comprehensive database for human–virus protein–protein interactions	53
Ferroptosis-related lncRNA pairs to predict the clinical outcome and molecular characteristics of pancreatic ductal adenocarcinoma	52
STALLION: a stacking-based ensemble learning framework for prokaryotic lysine acetylation site prediction	51
DeepIPs: comprehensive assessment and computational identification of phosphorylation sites of SARS-CoV-2 infection using a deep learning-based approach	51
Predicting potential small molecule–miRNA associations based on bounded nuclear norm regularization	51
ENNAVIA is a novel method which employs neural networks for antiviral and anti-coronavirus activity prediction for therapeutic peptides	51
Network pharmacology: towards the artificial intelligence-based precision traditional Chinese medicine	51
Machine learning-based tumor-infiltrating immune cell-associated lncRNAs for predicting prognosis and immunotherapy response in patients with glioblastoma	51
Pharmacometabonomics: data processing and statistical analysis	50
Recent advances in network-based methods for disease gene prediction	50
Protein design via deep learning	49
SC-MEB: spatial clustering with hidden Markov random field using empirical Bayes	49
Identification of drug–target interactions via multiple kernel-based triple collaborative matrix factorization	49
ConSIG: consistent discovery of molecular signature from OMIC data	49
Accurate prediction of inter-protein residue–residue contacts for homo-oligomeric protein complexes	48
Updated review of advances in microRNAs and complex diseases: experimental results, databases, webservers and data fusion	48
Drug–target interaction predication via multi-channel graph neural networks	48
Machine-designed biotherapeutics: opportunities, feasibility and advantages of deep learning in computational antibody discovery	48
A cross-study analysis of drug response prediction in cancer cell lines	48
Machine learning for synergistic network pharmacology: a comprehensive overview	48
AttentionSiteDTI: an interpretable graph-based model for drug-target interaction prediction using NLP sentence-level relation classification	48
XOmiVAE: an interpretable deep learning model for cancer classification using high-dimensional omics data	48
iAMP-CA2L: a new CNN-BiLSTM-SVM classifier based on cellular automata image for identifying antimicrobial peptides and their functional types	47
Demystifying emerging bulk RNA-Seq applications: the application and utility of bioinformatic methodology	47
Updated review of advances in microRNAs and complex diseases: towards systematic evaluation of computational models	47
NPI-GNN: Predicting ncRNA–protein interactions with deep graph neural networks	47
A review of biomedical datasets relating to drug discovery: a knowledge graph perspective	46
Unsupervised and self-supervised deep learning approaches for biomedical text mining	46
DeepLncLoc: a deep learning framework for long non-coding RNA subcellular localization prediction based on subsequence embedding	46
Towards deep phenotyping pregnancy: a systematic review on artificial intelligence and machine learning methods to improve pregnancy outcomes	45
Computational resources for identifying and describing proteins driving liquid–liquid phase separation	45

Comprehensive assessment of cellular senescence in the tumor microenvironment	44
RNMFLP: Predicting circRNA–disease associations based on robust nonnegative matrix factorization and label propagation	44
AMDE: a novel attention-mechanism-based multidimensional feature encoder for drug–drug interaction prediction	44
Computational identification of eukaryotic promoters based on cascaded deep capsule neural networks	44
Artificial intelligence and machine learning approaches using gene expression and variant data for personalized medicine	44
KGANCDA: predicting circRNA-disease associations based on knowledge graph attention network	44
AVPIden: a new scheme for identification and functional prediction of antiviral peptides based on machine learning approaches	44
PreDTIs: prediction of drug–target interactions based on multiple feature information using gradient boosting framework with data balancing and feature selection techniques	43
Transcriptome analysis of cepharanthine against a SARS-CoV-2-related coronavirus	43
Predicting miRNA–disease associations via learning multimodal networks and fusing mixed neighborhood information	43
GraphCDR: a graph neural network method with contrastive learning for cancer drug response prediction	43
Immune infiltration and clinical significance analyses of the coagulation-related genes in hepatocellular carcinoma	43
Integrated unsupervised–supervised modeling and prediction of protein–peptide affinities at structural level	43
Identifying multi-functional bioactive peptide functions using multi-label deep learning	43
epitope3D: a machine learning method for conformational B-cell epitope prediction	43
FireProtASR: A Web Server for Fully Automated Ancestral Sequence Reconstruction	43
Integrative machine learning framework for the identification of cell-specific enhancers from the human genome	43
Modeling and analyzing single-cell multimodal data with deep parametric inference	42
Predicting drug–drug interactions by graph convolutional network with multi-kernel	42
Protein–RNA interaction prediction with deep learning: structure matters	42
Bioinformatics resources for SARS-CoV-2 discovery and surveillance	42
Deep structural clustering for single-cell RNA-seq data jointly through autoencoder and graph neural network	42
Identification and characterization of circRNAs encoded by MERS-CoV, SARS-CoV-1 and SARS-CoV-2	41
MathFeature: feature extraction package for DNA, RNA and protein sequences based on mathematical descriptors	41
AniAMPpred: artificial intelligence guided discovery of novel antimicrobial peptides in animal kingdom	40
Porpoise: a new approach for accurate prediction of RNA pseudouridine sites	40
DeepFeature: feature selection in nonimage data using convolutional neural network	40
A network embedding framework based on integrating multiplex network for drug combination prediction	40
A hybrid deep learning framework for gene regulatory network inference from single-cell transcriptomic data	40
Knowledge-based BERT: a method to extract molecular features like computational chemists	40
Bioinformatics and system biology approach to identify the influences of COVID-19 on cardiovascular and hypertensive comorbidities	40
TrimNet: learning molecular representation from triplet messages for biomedicine	39
Identifying anti-coronavirus peptides by incorporating different negative datasets and imbalanced learning strategies	39
A new thinking: extended application of genomic selection to screen multiomics data for development of novel hypoxia-immune biomarkers and target therapy of clear cell renal cell carcinoma	39
GCRFLDA: scoring lncRNA-disease associations using graph convolution matrix completion with conditional random field	39
Cloud 3D-QSAR: a web tool for the development of quantitative structure–activity relationship models in drug discovery	39
spaCI: deciphering spatial cellular communications through adaptive graph model	39
Critical downstream analysis steps for single-cell RNA sequencing data	39
Anthem: a user customised tool for fast and accurate prediction of binding between peptides and HLA class I molecules	39
PreTP-EL: prediction of therapeutic peptides based on ensemble learning	38
Advances in bulk and single-cell multi-omics approaches for systems biology and precision medicine	38
A comprehensive survey on computational methods of non-coding RNA and disease association prediction	38
DeepTTA: a transformer-based model for predicting cancer drug response	38
Heterogeneous graph attention network based on meta-paths for lncRNA–disease association prediction	38
RNA–RNA interactions between SARS-CoV-2 and host benefit viral development and evolution during COVID-19 infection	38
Data science in unveiling COVID-19 pathogenesis and diagnosis: evolutionary origin to drug repurposing	38
SARS-CoV-2 3D database: understanding the coronavirus proteome and evaluating possible drug targets	37
SGANRDA: semi-supervised generative adversarial networks for predicting circRNA–disease associations	37
Accurate feature selection improves single-cell RNA-seq cell clustering	37
Diseasome and comorbidities complexities of SARS-CoV-2 infection with common malignant diseases	37
Identification of biomarkers and pathways for the SARS-CoV-2 infections that make complexities in pulmonary arterial hypertension patients	37
kTWAS: integrating kernel machine with transcriptome-wide association studies improves statistical power and reveals novel genes	37
Virus classification for viral genomic fragments using PhaGCN2	36
NmRF: identification of multispecies RNA 2’-O-methylation modification sites from RNA sequences	36
De novo molecular design with deep molecular generative models for PPI inhibitors	36
Is acupuncture effective in the treatment of COVID-19 related symptoms? Based on bioinformatics/network topology strategy	36
ConsRM: collection and large-scale prediction of the evolutionarily conserved RNA methylation sites, with implications for the functional epitranscriptome	36
DeepDRK: a deep learning framework for drug repurposing through kernel-based multi-omics integration	36
Benchmarks in antimicrobial peptide prediction are biased due to the selection of negative data	36
DeepDISOBind: accurate prediction of RNA-, DNA- and protein-binding intrinsically disordered residues with deep multi-task learning	36
DeepDRIM: a deep neural network to reconstruct cell-type-specific gene regulatory network using single-cell RNA-seq data	36
Prediction of RNA secondary structure including pseudoknots for long sequences	36
A geometric deep learning framework for drug repositioning over heterogeneous information networks	36
Machine learning approaches for drug combination therapies	36
Prediction of potential miRNA–disease associations based on stacked autoencoder	35
Deep-AFPpred: identifying novel antifungal peptides using pretrained embeddings from seq2vec with 1DCNN-BiLSTM	35
Oxford nanopore sequencing in clinical microbiology and infection diagnostics	35
Forman persistent Ricci curvature (FPRC)-based machine learning models for protein–ligand binding affinity prediction	35
Global characterization of B cell receptor repertoire in COVID-19 patients by single-cell V(D)J sequencing	35
Exploring pharmacological active ingredients of traditional Chinese medicine by pharmacotranscriptomic map in ITCM	35
Iterative feature representation algorithm to improve the predictive performance of N7-methylguanosine sites	35
Assessing the performance of computational predictors for estimating protein stability changes upon missense mutations	35
Accelerating bioactive peptide discovery via mutual information-based meta-learning	35
ResNet18DNN: prediction approach of drug-induced liver injury by deep neural network with ResNet18	35
Topological network measures for drug repositioning	34
iLoc-miRNA: extracellular/intracellular miRNA prediction using deep BiLSTM with attention mechanism	34
A merged molecular representation deep learning method for blood–brain barrier permeability prediction	34
scGMAI: a Gaussian mixture model for clustering single-cell RNA-Seq data based on deep autoencoder	34
Hyperbolic relational graph convolution networks plus: a simple but highly efficient QSAR-modeling method	34
Locating transcription factor binding sites by fully convolutional neural network	34
DSN-DDI: an accurate and generalized framework for drug–drug interaction prediction by dual-view representation learning	34
CHERRY: a Computational metHod for accuratE pRediction of virus–pRokarYotic interactions using a graph encoder–decoder model	34
Deep learning in bioinformatics and biomedicine	33
A transferable deep learning approach to fast screen potential antiviral drugs against SARS-CoV-2	33
META-DDIE: predicting drug–drug interaction events with few-shot learning	33
Deciphering the genetic code of DNA methylation	33
HCC subtypes based on the activity changes of immunologic and hallmark gene sets in tumor and nontumor tissues	33
Single-cell Hi-C data analysis: safety in numbers	33
EGRET: edge aggregated graph attention networks and transfer learning improve protein–protein interaction site prediction	33
Predicting drug–target binding affinity through molecule representation block based on multi-head attention and skip connection	33
A comprehensive comparison on cell-type composition inference for spatial transcriptomics data	32
High dimensionality reduction by matrix factorization for systems pharmacology	32
A deep learning approach for filtering structural variants in short read sequencing data	32
Ligand-based approach for predicting drug targets and for virtual screening against COVID-19	32
A point cloud-based deep learning strategy for protein–ligand binding affinity prediction	32