Briefings in Bioinformatics

Papers
(The H4-Index of Briefings in Bioinformatics is 71. The table below lists those papers that are above that threshold based on CrossRef citation counts [max. 250 papers]. The publications cover those that have been published in the past four years, i.e., from 2021-05-01 to 2025-05-01.)
ArticleCitations
Cox-Sage: enhancing Cox proportional hazards model with interpretable graph neural networks for cancer prognosis1000
Corrigendum to: Computational design of ultrashort peptide inhibitors of the receptor-binding domain of the SARS-CoV-2 S protein467
Knowledge bases and software support for variant interpretation in precision oncology318
Analysis of super-enhancer using machine learning and its application to medical biology246
Computational model for ncRNA research208
COWID: an efficient cloud-based genomics workflow for scalable identification of SARS-COV-2205
Clustering scRNA-seq data with the cross-view collaborative information fusion strategy196
Letter regarding article named ‘Is acupuncture effective in the treatment of COVID-19 related symptoms? Based on bioinformatics/network topology strategy’189
DeepCheck: multitask learning aids in assessing microbial genome quality188
GAABind: a geometry-aware attention-based network for accurate protein–ligand binding pose and binding affinity prediction173
Balancing the transcriptome: leveraging sample similarity to improve measures of gene specificity171
Genome sequencing data analysis for rare disease gene discovery169
CharID: a two-step model for universal prediction of interactions between chromatin accessible regions164
Defining the functional divergence of orthologous genes between human and mouse in the context of miRNA regulation161
ETLD: an encoder-transformation layer-decoder architecture for protein contact and mutation effects prediction156
CLT-seq as a universal homopolymer-sequencing concept reveals poly(A)-tail-tuned ncRNA regulation153
Combining power of different methods to detect associations in large data sets147
SGNNMD: signed graph neural network for predicting deregulation types of miRNA-disease associations142
Novel multi-omics deconfounding variational autoencoders can obtain meaningful disease subtyping140
Clustered tree regression to learn protein energy change with mutated amino acid133
Blood-based transcriptomic signature panel identification for cancer diagnosis: benchmarking of feature extraction methods131
SCSMD: Single Cell Consistent Clustering based on Spectral Matrix Decomposition123
Attribute-guided prototype network for few-shot molecular property prediction120
Ensemble classification based feature selection: a case of identification on plant pentatricopeptide repeat proteins118
A multichannel graph neural network based on multisimilarity modality hypergraph contrastive learning for predicting unknown types of cancer biomarkers118
Distant metastasis identification based on optimized graph representation of gene interaction patterns117
Computational analyses of bacterial strains from shotgun reads115
BayesKAT: bayesian optimal kernel-based test for genetic association studies reveals joint genetic effects in complex diseases110
mbDecoda: a debiased approach to compositional data analysis for microbiome surveys109
CpGFuse: a holistic approach for accurate identification of methylation states of DNA CpG sites107
A robust statistical approach for finding informative spatially associated pathways107
AptaDiff: de novo design and optimization of aptamers based on diffusion models103
QOT: Quantized Optimal Transport for sample-level distance matrix in single-cell omics102
Exploring the immune evasion of SARS-CoV-2 variant harboring E484K by molecular dynamics simulations101
A chronotherapeutics-applicable multi-target therapeutics based on AI: Example of therapeutic hypothermia100
Correction to: Addressing barriers in comprehensiveness, accessibility, reusability, interoperability and reproducibility of computational models in systems biology97
PMiSLocMF: predicting miRNA subcellular localizations by incorporating multi-source features of miRNAs96
Ensemble learning based on matrix completion improves microbe-disease association prediction96
Directed evolution of antimicrobial peptides using multi-objective zeroth-order optimization94
Inferring disease-associated circRNAs by multi-source aggregation based on heterogeneous graph neural network94
Addressing scalability and managing sparsity and dropout events in single-cell representation identification with ZIGACL93
Building multiscale models with PhysiBoSS, an agent-based modeling tool93
Integrating AlphaFold and deep learning for atomistic interpretation of cryo-EM maps90
dHICA: a deep transformer-based model enables accurate histone imputation from chromatin accessibility89
A social theory-enhanced graph representation learning framework for multitask prediction of drug–drug interactions89
Protein phosphorylation database and prediction tools87
scGAD: a new task and end-to-end framework for generalized cell type annotation and discovery86
From intuition to AI: evolution of small molecule representations in drug discovery86
Large-scale predicting protein functions through heterogeneous feature fusion84
Identification of vital chemical information via visualization of graph neural networks84
ADENet: a novel network-based inference method for prediction of drug adverse events84
MicroHDF: predicting host phenotypes with metagenomic data using a deep forest-based framework82
Assessing protein model quality based on deep graph coupled networks using protein language model82
Machine learning modeling of RNA structures: methods, challenges and future perspectives81
HighFold: accurately predicting structures of cyclic peptides and complexes with head-to-tail and disulfide bridge constraints81
Multiple errors correction for position-limited DNA sequences with GC balance and no homopolymer for DNA-based data storage80
Improving the performance of single-cell RNA-seq data mining based on relative expression orderings80
Clover: tree structure-based efficient DNA clustering for DNA-based data storage78
Machine learning methods, databases and tools for drug combination prediction78
Subtype-DCC: decoupled contrastive clustering method for cancer subtype identification based on multi-omics data77
Improving drug response prediction via integrating gene relationships with deep learning77
Melanoma 2.0. Skin cancer as a paradigm for emerging diagnostic technologies, computational modelling and artificial intelligence77
Predicting MHC class I binder: existing approaches and a novel recurrent neural network solution76
ULDNA: integrating unsupervised multi-source language models with LSTM-attention network for high-accuracy protein–DNA binding site prediction75
Circular RNAs and complex diseases: from experimental results to computational models74
Learning discriminative and structural samples for rare cell types with deep generative model74
Benchmarking of computational methods for m6A profiling with Nanopore direct RNA sequencing73
A robust and scalable graph neural network for accurate single-cell classification73
Deep learning in integrating spatial transcriptomics with other modalities73
PRIEST: predicting viral mutations with immune escape capability of SARS-CoV-2 using temporal evolutionary information72
Detecting tipping points of complex diseases by network information entropy72
A review on the application of bioinformatics tools in food microbiome studies71
Evaluating large language models for annotating proteins71
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