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
A multichannel graph neural network based on multisimilarity modality hypergraph contrastive learning for predicting unknown types of cancer biomarkers118
Ensemble classification based feature selection: a case of identification on plant pentatricopeptide repeat proteins118
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
A robust statistical approach for finding informative spatially associated pathways107
CpGFuse: a holistic approach for accurate identification of methylation states of DNA CpG sites107
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
Inferring disease-associated circRNAs by multi-source aggregation based on heterogeneous graph neural network94
Directed evolution of antimicrobial peptides using multi-objective zeroth-order optimization94
Building multiscale models with PhysiBoSS, an agent-based modeling tool93
Addressing scalability and managing sparsity and dropout events in single-cell representation identification with ZIGACL93
Integrating AlphaFold and deep learning for atomistic interpretation of cryo-EM maps90
A social theory-enhanced graph representation learning framework for multitask prediction of drug–drug interactions89
dHICA: a deep transformer-based model enables accurate histone imputation from chromatin accessibility89
Protein phosphorylation database and prediction tools87
From intuition to AI: evolution of small molecule representations in drug discovery86
scGAD: a new task and end-to-end framework for generalized cell type annotation and discovery86
ADENet: a novel network-based inference method for prediction of drug adverse events84
Large-scale predicting protein functions through heterogeneous feature fusion84
Identification of vital chemical information via visualization of graph neural networks84
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
HighFold: accurately predicting structures of cyclic peptides and complexes with head-to-tail and disulfide bridge constraints81
Machine learning modeling of RNA structures: methods, challenges and future perspectives81
Improving the performance of single-cell RNA-seq data mining based on relative expression orderings80
Multiple errors correction for position-limited DNA sequences with GC balance and no homopolymer for DNA-based data storage80
Clover: tree structure-based efficient DNA clustering for DNA-based data storage78
Machine learning methods, databases and tools for drug combination prediction78
Melanoma 2.0. Skin cancer as a paradigm for emerging diagnostic technologies, computational modelling and artificial intelligence77
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
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
Learning discriminative and structural samples for rare cell types with deep generative model74
Circular RNAs and complex diseases: from experimental results to computational models74
Deep learning in integrating spatial transcriptomics with other modalities73
Benchmarking of computational methods for m6A profiling with Nanopore direct RNA sequencing73
A robust and scalable graph neural network for accurate single-cell classification73
Detecting tipping points of complex diseases by network information entropy72
PRIEST: predicting viral mutations with immune escape capability of SARS-CoV-2 using temporal evolutionary information72
A review on the application of bioinformatics tools in food microbiome studies71
Evaluating large language models for annotating proteins71
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