Briefings in Bioinformatics

Article	Citations
Computational model for ncRNA research	585
Genome sequencing data analysis for rare disease gene discovery	397
Large-scale predicting protein functions through heterogeneous feature fusion	318
A multichannel graph neural network based on multisimilarity modality hypergraph contrastive learning for predicting unknown types of cancer biomarkers	312
Building multiscale models with PhysiBoSS, an agent-based modeling tool	277
Benchmarking of computational methods for m6A profiling with Nanopore direct RNA sequencing	272
CharID: a two-step model for universal prediction of interactions between chromatin accessible regions	243
Combining power of different methods to detect associations in large data sets	217
Detection of transcription factors binding to methylated DNA by deep recurrent neural network	213
COWID: an efficient cloud-based genomics workflow for scalable identification of SARS-COV-2	206
Self-supervised learning with chemistry-aware fragmentation for effective molecular property prediction	200
ETLD: an encoder-transformation layer-decoder architecture for protein contact and mutation effects prediction	194
CLT-seq as a universal homopolymer-sequencing concept reveals poly(A)-tail-tuned ncRNA regulation	186
Ensemble classification based feature selection: a case of identification on plant pentatricopeptide repeat proteins	167
QOT: Quantized Optimal Transport for sample-level distance matrix in single-cell omics	159
Balancing the transcriptome: leveraging sample similarity to improve measures of gene specificity	154
A robust statistical approach for finding informative spatially associated pathways	152
Correction to: Addressing barriers in comprehensiveness, accessibility, reusability, interoperability and reproducibility of computational models in systems biology	148
Letter regarding article named ‘Is acupuncture effective in the treatment of COVID-19 related symptoms? Based on bioinformatics/network topology strategy’	146
SCSMD: Single Cell Consistent Clustering based on Spectral Matrix Decomposition	134
Ensemble learning based on matrix completion improves microbe-disease association prediction	133
Addressing scalability and managing sparsity and dropout events in single-cell representation identification with ZIGACL	129
Directed evolution of antimicrobial peptides using multi-objective zeroth-order optimization	126
SGNNMD: signed graph neural network for predicting deregulation types of miRNA-disease associations	119
CpGFuse: a holistic approach for accurate identification of methylation states of DNA CpG sites	117

Clustered tree regression to learn protein energy change with mutated amino acid	117
Machine learning–augmented m6A-Seq analysis without a reference genome	114
Cox-Sage: enhancing Cox proportional hazards model with interpretable graph neural networks for cancer prognosis	114
Evaluating large language models for annotating proteins	110
dHICA: a deep transformer-based model enables accurate histone imputation from chromatin accessibility	109
Multi-modal domain adaptation for revealing spatial functional landscape from spatially resolved transcriptomics	105
Improving the performance of single-cell RNA-seq data mining based on relative expression orderings	104
Exploring the immune evasion of SARS-CoV-2 variant harboring E484K by molecular dynamics simulations	104
Inferring disease-associated circRNAs by multi-source aggregation based on heterogeneous graph neural network	103
Protein phosphorylation database and prediction tools	97
A social theory-enhanced graph representation learning framework for multitask prediction of drug–drug interactions	97
scAnno: a deconvolution strategy-based automatic cell type annotation tool for single-cell RNA-sequencing data sets	97
DeepCheck: multitask learning aids in assessing microbial genome quality	96
Clustering scRNA-seq data with the cross-view collaborative information fusion strategy	96
PMiSLocMF: predicting miRNA subcellular localizations by incorporating multi-source features of miRNAs	96
BayesKAT: bayesian optimal kernel-based test for genetic association studies reveals joint genetic effects in complex diseases	95
Deep learning in integrating spatial transcriptomics with other modalities	94
mbDecoda: a debiased approach to compositional data analysis for microbiome surveys	93
cfMethylPre: deep transfer learning enhances cancer detection based on circulating cell-free DNA methylation profiling	93
Graph-RPI: predicting RNA–protein interactions via graph autoencoder and self-supervised learning strategies	93
Blood-based transcriptomic signature panel identification for cancer diagnosis: benchmarking of feature extraction methods	92
Attribute-guided prototype network for few-shot molecular property prediction	91
scGAD: a new task and end-to-end framework for generalized cell type annotation and discovery	87
A novel prognostic framework for HBV-infected hepatocellular carcinoma: insights from ferroptosis and iron metabolism proteomics	86
A chronotherapeutics-applicable multi-target therapeutics based on AI: Example of therapeutic hypothermia	85
Computational analyses of bacterial strains from shotgun reads	84
Deep learning reveals determinants of transcriptional infidelity at nucleotide resolution in the allopolyploid line by goldfish and common carp hybrids	84
Dynamic changes of synergy relationship between lncRNA and immune checkpoint in cancer progression	83
PLMFit: benchmarking transfer learning with protein language models for protein engineering	83
A comprehensive benchmark of tools for efficient genomic interval querying	83
Protein–DNA binding sites prediction based on pre-trained protein language model and contrastive learning	82
ULDNA: integrating unsupervised multi-source language models with LSTM-attention network for high-accuracy protein–DNA binding site prediction	82
Machine learning modeling of RNA structures: methods, challenges and future perspectives	82
DriverOmicsNet: an integrated graph convolutional network for multi-omics exploration of cancer driver genes	78
Multiple errors correction for position-limited DNA sequences with GC balance and no homopolymer for DNA-based data storage	78
Machine learning methods, databases and tools for drug combination prediction	78
Novel multi-omics deconfounding variational autoencoders can obtain meaningful disease subtyping	77
Distant metastasis identification based on optimized graph representation of gene interaction patterns	76
A review on the application of bioinformatics tools in food microbiome studies	74
PRIEST: predicting viral mutations with immune escape capability of SARS-CoV-2 using temporal evolutionary information	74
A robust and scalable graph neural network for accurate single-cell classification	73
IGCNSDA: unraveling disease-associated snoRNAs with an interpretable graph convolutional network	73
Assessing protein model quality based on deep graph coupled networks using protein language model	73
MicroHDF: predicting host phenotypes with metagenomic data using a deep forest-based framework	73
AptaDiff: de novo design and optimization of aptamers based on diffusion models	72
Detecting tipping points of complex diseases by network information entropy	72
Making PBPK models more reproducible in practice	72
Clover: tree structure-based efficient DNA clustering for DNA-based data storage	71
Learning discriminative and structural samples for rare cell types with deep generative model	70
Improving drug response prediction via integrating gene relationships with deep learning	70

GAABind: a geometry-aware attention-based network for accurate protein–ligand binding pose and binding affinity prediction	69
ADENet: a novel network-based inference method for prediction of drug adverse events	69
Melanoma 2.0. Skin cancer as a paradigm for emerging diagnostic technologies, computational modelling and artificial intelligence	69
Subtype-DCC: decoupled contrastive clustering method for cancer subtype identification based on multi-omics data	67
Predicting microbe–drug associations with structure-enhanced contrastive learning and self-paced negative sampling strategy	67
Identification of vital chemical information via visualization of graph neural networks	67
Integrating AlphaFold and deep learning for atomistic interpretation of cryo-EM maps	66
From intuition to AI: evolution of small molecule representations in drug discovery	66
HighFold: accurately predicting structures of cyclic peptides and complexes with head-to-tail and disulfide bridge constraints	66
Analysis of super-enhancer using machine learning and its application to medical biology	66
Denoising adaptive deep clustering with self-attention mechanism on single-cell sequencing data	65
scDeepInsight: a supervised cell-type identification method for scRNA-seq data with deep learning	65
Forecasting dominance of SARS-CoV-2 lineages by anomaly detection using deep AutoEncoders	65
Therapeutic peptides identification via kernel risk sensitive loss-based k-nearest neighbor model and multi-Laplacian regularization	65
Deciphering the etiology and role in oncogenic transformation of the CpG island methylator phenotype: a pan-cancer analysis	64
A novel approach to study multi-domain motions in JAK1’s activation mechanism based on energy landscape	63
Capturing large genomic contexts for accurately predicting enhancer-promoter interactions	62
A comprehensive benchmarking of differential splicing tools for RNA-seq analysis at the event level	61
Efficient prediction of peptide self-assembly through sequential and graphical encoding	61
IEPAPI: a method for immune epitope prediction by incorporating antigen presentation and immunogenicity	61
PSnoD: identifying potential snoRNA-disease associations based on bounded nuclear norm regularization	61
scAMAC: self-supervised clustering of scRNA-seq data based on adaptive multi-scale autoencoder	61
A transformer-based deep learning survival prediction model and an explainable XGBoost anti-PD-1/PD-L1 outcome prediction model based on the cGAS-STING-centered pathways in hepatocellular carcinoma	61
SPNE: sample-perturbed network entropy for revealing critical states of complex biological systems	61
ConSIG: consistent discovery of molecular signature from OMIC data	60
BETA: a comprehensive benchmark for computational drug–target prediction	59
Complexity of enhancer networks predicts cell identity and disease genes revealed by single-cell multi-omics analysis	59
MiRNA–disease association prediction based on meta-paths	59
ncRNAInter: a novel strategy based on graph neural network to discover interactions between lncRNA and miRNA	59
Drug repositioning based on weighted local information augmented graph neural network	58
Robust discovery of gene regulatory networks from single-cell gene expression data by Causal Inference Using Composition of Transactions	58
Development of interactive biological web applications with R/Shiny	57
Paradigms, innovations, and biological applications of RNA velocity: a comprehensive review	56
Estimation of non-equilibrium transition rate from gene expression data	56
LRcell: detecting the source of differential expression at the sub–cell-type level from bulk RNA-seq data	55
SAMURAI: shallow analysis of copy number alterations using a reproducible and integrated bioinformatics pipeline	55
Optimizing genomic control in mixed model associations with binary diseases	55
Concepts and methods for transcriptome-wide prediction of chemical messenger RNA modifications with machine learning	55
Revealing the antimicrobial potential of traditional Chinese medicine through text mining and molecular computation	54
Robustness and resilience of computational deconvolution methods for bulk RNA sequencing data	54
Construct a variable-length fragment library for de novo protein structure prediction	54
Integrated multimodal hierarchical fusion and meta-learning for enhanced molecular property prediction	54
SGCLDGA: unveiling drug–gene associations through simple graph contrastive learning	54
A risk assessment framework for multidrug-resistant Staphylococcus aureus using machine learning and mass spectrometry technology	53
Multilevel superposition for deciphering the conformational variability of protein ensembles	53
TransIntegrator: capture nearly full protein-coding transcript variants via integrating Illumina and PacBio transcriptomes	53
Knowledge-guided multi-level network modeling with experimental characterization identifies PRKCA as a novel biomarker and tumor suppressor triggering ferroptosis in prostate cancer	53
Multi-omics regulatory network inference in the presence of missing data	53
Predicting molecular properties based on the interpretable graph neural network with multistep focus mechanism	52
miRPreM and tiRPreM: Improved methodologies for the prediction of miRNAs and tRNA-induced small non-coding RNAs for model and non-model organisms	52
HLA3D: an integrated structure-based computational toolkit for immunotherapy	52
SPANN: annotating single-cell resolution spatial transcriptome data with scRNA-seq data	52
ReCIDE: robust estimation of cell type proportions by integrating single-reference-based deconvolutions	52
BatchDTA: implicit batch alignment enhances deep learning-based drug–target affinity estimation	51
Interpretable high-order knowledge graph neural network for predicting synthetic lethality in human cancers	51
A comprehensive computational benchmark for evaluating deep learning-based protein function prediction approaches	51
Correction to: sciCNV: high-throughput paired profiling of transcriptomes and DNA copy number variations at single-cell resolution	50
FactVAE: a factorized variational autoencoder for single-cell multi-omics data integration analysis	50
Phylogenetic inference of inter-population transmission rates for infectious diseases	50
Systematic investigation of the homology sequences around the human fusion gene breakpoints in pan-cancer – bioinformatics study for a potential link to MMEJ	50
Inferring kinase–phosphosite regulation from phosphoproteome-enriched cancer multi-omics datasets	50
Seq2Topt: a sequence-based deep learning predictor of enzyme optimal temperature	50
Data-driven selection of analysis decisions in single-cell RNA-seq trajectory inference	50
RiboChat: a chat-style web interface for analysis and annotation of ribosome profiling data	50
Development and validation of an explainable machine learning model for predicting multidimensional frailty in hospitalized patients with cirrhosis	50
A novel computational model ITHCS for enhanced prognostic risk stratification in ESCC by correcting for intratumor heterogeneity	50
A review of methods for predicting DNA N6-methyladenine sites	49
dSCOPE: a software to detect sequences critical for liquid–liquid phase separation	49
scEWE: high-order element-wise weighted ensemble clustering for heterogeneity analysis of single-cell RNA-sequencing data	49
PredLLPS_PSSM: a novel predictor for liquid–liquid protein separation identification based on evolutionary information and a deep neural network	48
Comparative epigenome analysis using Infinium DNA methylation BeadChips	47
Improving multi-population genomic prediction accuracy using multi-trait GBLUP models which incorporate global or local genetic correlation information	47
Deciphering gene contributions and etiologies of somatic mutational signatures of cancer	47
The improved de Bruijn graph for multitask learning: predicting functions, subcellular localization, and interactions of noncoding RNAs	47
Inferring single-cell resolution spatial gene expression via fusing spot-based spatial transcriptomics, location, and histology using GCN	47
MAMnet: detecting and genotyping deletions and insertions based on long reads and a deep learning approach	47
slORFfinder: a tool to detect open reading frames resulting from trans-splicing of spliced leader sequences	47
CHAI: consensus clustering through similarity matrix integration for cell-type identification	47
Advancing microbial diagnostics: a universal phylogeny guided computational algorithm to find unique sequences for precise microorganism detection	46
AI-guided discovery and optimization of antimicrobial peptides through species-aware language model	46

HLAIImaster: a deep learning method with adaptive domain knowledge predicts HLA II neoepitope immunogenic responses	46
RBP-TSTL is a two-stage transfer learning framework for genome-scale prediction of RNA-binding proteins	46
Beyond static structures: protein dynamic conformations modeling in the post-AlphaFold era	45
An automatic immunofluorescence pattern classification framework for HEp-2 image based on supervised learning	45
A novel heterophilic graph diffusion convolutional network for identifying cancer driver genes	45
Deep learning in structural bioinformatics: current applications and future perspectives	45
Contrastive learning-based computational histopathology predict differential expression of cancer driver genes	45
The landscape of the methodology in drug repurposing using human genomic data: a systematic review	44
Learning genotype–phenotype associations from gaps in multi-species sequence alignments	44
AMDBNorm: an approach based on distribution adjustment to eliminate batch effects of gene expression data	44
Correction to: Diagnostic Prediction of portal vein thrombosis in chronic cirrhosis patients using data-driven precision medicine model	44
HHOMR: a hybrid high-order moment residual model for miRNA-disease association prediction	44
Learning single-cell chromatin accessibility profiles using meta-analytic marker genes	44
EDS-Kcr: deep supervision based on large language model for identifying protein lysine crotonylation sites across multiple species	43
MUSCLE: multi-view and multi-scale attentional feature fusion for microRNA–disease associations prediction	43
Machine learning-assisted substrate binding pocket engineering based on structural information	43
toxCSM: comprehensive prediction of small molecule toxicity profiles	43
ComABAN: refining molecular representation with the graph attention mechanism to accelerate drug discovery	43
Impact of computational approaches in the fight against COVID-19: an AI guided review of 17 000 studies	43
DRdriver: identifying drug resistance driver genes using individual-specific gene regulatory network	43
Evaluation of single-cell RNAseq labelling algorithms using cancer datasets	43
Correction to: PHR-search: a search framework for protein remote homology detection based on the predicted protein hierarchical relationships	42
iEnhance: a multi-scale spatial projection encoding network for enhancing chromatin interaction data resolution	42
Differentially expressed genes prediction by multiple self-attention on epigenetics data	42
Incremental modelling and analysis of biological systems with fuzzy hybrid Petri nets	42
scEGG: an exogenous gene-guided clustering method for single-cell transcriptomic data	42
Transfer learning of clinical outcomes from preclinical molecular data, principles and perspectives	41
A tool for feature extraction from biological sequences	41
Exploring the kinase-inhibitor fragment interaction space facilitates the discovery of kinase inhibitor overcoming resistance by mutations	41
MulNet: a scalable framework for reconstructing intra- and intercellular signaling networks from bulk and single-cell RNA-seq data	41
CRISP: a deep learning architecture for GC × GC–TOFMS contour ROI identification, simulation and analysis in imaging metabolomics	41
Identify potential drug candidates within a high-quality compound search space	41
Bioinformatics toolbox for exploring target mutation-induced drug resistance	40
CACIMAR: cross-species analysis of cell identities, markers, regulations, and interactions using single-cell RNA sequencing data	40
GiGs: graph-based integrated Gaussian kernel similarity for virus–drug association prediction	40
Interpretable artificial intelligence model for accurate identification of medical conditions using immune repertoire	39
EGRET: edge aggregated graph attention networks and transfer learning improve protein–protein interaction site prediction	39
Optimized phenotyping of complex morphological traits: enhancing discovery of common and rare genetic variants	39
MGEGFP: a multi-view graph embedding method for gene function prediction based on adaptive estimation with GCN	39
BloodNet: An attention-based deep network for accurate, efficient, and costless bloodstain time since deposition inference	39
D3EGFR: a webserver for deep learning-guided drug sensitivity prediction and drug response information retrieval for EGFR mutation-driven lung cancer	39
A kinetic model for solving a combination optimization problem in ab-initio Cryo-EM 3D reconstruction	38
Microbe-bridged disease-metabolite associations identification by heterogeneous graph fusion	38
HINGRL: predicting drug–disease associations with graph representation learning on heterogeneous information networks	38
Whole-genome bisulfite sequencing data analysis learning module on Google Cloud Platform	38
Predicting miRNA-disease associations based on graph attention networks and dual Laplacian regularized least squares	38
CosGeneGate selects multi-functional and credible biomarkers for single-cell analysis	38
Predictive modelling of acute Promyelocytic leukaemia resistance to retinoic acid therapy	38
Predicting differentially methylated cytosines in TET and DNMT3 knockout mutants via a large language model	38
A parameter-free deep embedded clustering method for single-cell RNA-seq data	38
NSCGRN: a network structure control method for gene regulatory network inference	38
Single-cell mosaic integration and cell state transfer with auto-scaling self-attention mechanism	38
Benchmarking genome assembly methods on metagenomic sequencing data	38
A comprehensive benchmark study of methods for identifying significantly perturbed subnetworks in cancer	37
Advancing edge-based clustering and graph embedding for biological network analysis: a case study in RASopathies	37
Advancing single-cell RNA-seq data analysis through the fusion of multi-layer perceptron and graph neural network	37
MetaGeno: a chromosome-wise multi-task genomic framework for ischaemic stroke risk prediction	37
siRNADiscovery: a graph neural network for siRNA efficacy prediction via deep RNA sequence analysis	36
Combining evolution and protein language models for an interpretable cancer driver mutation prediction with D2Deep	36
Prediction of multi-relational drug–gene interaction via Dynamic hyperGraph Contrastive Learning	36
Integrative analysis of multi-omics and imaging data with incorporation of biological information via structural Bayesian factor analysis	36
DeepHost: phage host prediction with convolutional neural network	36
Spatially contrastive variational autoencoder for deciphering tissue heterogeneity from spatially resolved transcriptomics	36
Multi-level multi-view network based on structural contrastive learning for scRNA-seq data clustering	36
Identification of molecular subtypes of dementia by using blood-proteins interaction-aware graph propagational network	36
An efficient curriculum learning-based strategy for molecular graph learning	36
A review of biomedical datasets relating to drug discovery: a knowledge graph perspective	35
Current approaches and outstanding challenges of functional annotation of metabolites: a comprehensive review	35
A deep learning method for predicting metabolite–disease associations via graph neural network	35
Adjustment of scRNA-seq data to improve cell-type decomposition of spatial transcriptomics	35
GSCA: an integrated platform for gene set cancer analysis at genomic, pharmacogenomic and immunogenomic levels	35
scIAE: an integrative autoencoder-based ensemble classification framework for single-cell RNA-seq data	35
Data-driven patient stratification of UK Biobank cohort suggests five endotypes of multimorbidity	35
Current computational tools for protein lysine acylation site prediction	35
MAK: a machine learning framework improved genomic prediction via multi-target ensemble regressor chains and automatic selection of assistant traits	35
Review on predicting pairwise relationships between human microbes, drugs and diseases: from biological data to computational models	35
PepTCR-Net: prediction of multi-class antigen peptides by T-cell receptor sequences with deep learning	35
SAM-DTA: a sequence-agnostic model for drug–target binding affinity prediction	35
TP53_PROF: a machine learning model to predict impact of missense mutations in TP53	35
GSTRPCA: irregular tensor singular value decomposition for single-cell multi-omics data clustering	35
Matrix reconstruction with reliable neighbors for predicting potential MiRNA–disease associations	35
Improved prediction of DNA and RNA binding proteins with deep learning models	35
SAM-TB: a whole genome sequencing data analysis website for detection of Mycobacterium tuberculosis drug resistance and transmission	35
scRDAN: a robust domain adaptation network for cell type annotation across single-cell RNA sequencing data	34
Understanding YTHDF2-mediated mRNA degradation by m6A-BERT-Deg	34
Ontology-aware neural network: a general framework for pattern mining from microbiome data	34
Ensembles of knowledge graph embedding models improve predictions for drug discovery	34
Multimodal deep learning for biomedical data fusion: a review	34
A new paradigm for applying deep learning to protein–ligand interaction prediction	34
iProbiotics: a machine learning platform for rapid identification of probiotic properties from whole-genome primary sequences	34
MFPred: prediction of ncRNA families based on multi-feature fusion	34
Detecting sparse microbial association signals adaptively from longitudinal microbiome data based on generalized estimating equations	33
DURIAN: an integrative deconvolution and imputation method for robust signaling analysis of single-cell transcriptomics data	33
Study of transcription factor druggabilty for prostate cancer using structure information, gene regulatory networks and protein moonlighting	33
Explainable deep neural networks for predicting sample phenotypes from single-cell transcriptomics	33