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 2021-11-01 to 2025-11-01.)
ArticleCitations
Dynamic changes of synergy relationship between lncRNA and immune checkpoint in cancer progression653
Building multiscale models with PhysiBoSS, an agent-based modeling tool427
cfMethylPre: deep transfer learning enhances cancer detection based on circulating cell-free DNA methylation profiling368
Analysis of super-enhancer using machine learning and its application to medical biology345
Melanoma 2.0. Skin cancer as a paradigm for emerging diagnostic technologies, computational modelling and artificial intelligence334
Large-scale predicting protein functions through heterogeneous feature fusion311
A comprehensive benchmark of tools for efficient genomic interval querying264
A robust and scalable graph neural network for accurate single-cell classification233
A social theory-enhanced graph representation learning framework for multitask prediction of drug–drug interactions226
Attribute-guided prototype network for few-shot molecular property prediction220
Hi-C3: a statistical inference-based model for reconstructing higher-order cell–cell communication networks212
PLMFit: benchmarking transfer learning with protein language models for protein engineering207
Assessing protein model quality based on deep graph coupled networks using protein language model199
Integrating AlphaFold and deep learning for atomistic interpretation of cryo-EM maps189
Protein–DNA binding sites prediction based on pre-trained protein language model and contrastive learning175
PRIEST: predicting viral mutations with immune escape capability of SARS-CoV-2 using temporal evolutionary information162
GAABind: a geometry-aware attention-based network for accurate protein–ligand binding pose and binding affinity prediction162
Learning discriminative and structural samples for rare cell types with deep generative model145
Novel multi-omics deconfounding variational autoencoders can obtain meaningful disease subtyping144
Improving drug response prediction via integrating gene relationships with deep learning141
Predicting microbe–drug associations with structure-enhanced contrastive learning and self-paced negative sampling strategy134
Multiple errors correction for position-limited DNA sequences with GC balance and no homopolymer for DNA-based data storage127
Machine learning modeling of RNA structures: methods, challenges and future perspectives125
MicroHDF: predicting host phenotypes with metagenomic data using a deep forest-based framework125
Detection of transcription factors binding to methylated DNA by deep recurrent neural network121
Phage quest: a beginner’s guide to explore viral diversity in the prokaryotic world116
Cox-Sage: enhancing Cox proportional hazards model with interpretable graph neural networks for cancer prognosis115
Directed evolution of antimicrobial peptides using multi-objective zeroth-order optimization114
Computational refinement and multivalent engineering of complementarity-determining region-grafted nanobodies on a humanized scaffold for retaining antiviral efficacy114
Blood-based transcriptomic signature panel identification for cancer diagnosis: benchmarking of feature extraction methods113
scGAD: a new task and end-to-end framework for generalized cell type annotation and discovery107
Deep learning reveals determinants of transcriptional infidelity at nucleotide resolution in the allopolyploid line by goldfish and common carp hybrids105
Computational analyses of bacterial strains from shotgun reads105
Computational model for ncRNA research103
COWID: an efficient cloud-based genomics workflow for scalable identification of SARS-COV-2103
Machine learning–augmented m6A-Seq analysis without a reference genome101
Evaluating large language models for annotating proteins100
dHICA: a deep transformer-based model enables accurate histone imputation from chromatin accessibility99
CharID: a two-step model for universal prediction of interactions between chromatin accessible regions98
QOT: Quantized Optimal Transport for sample-level distance matrix in single-cell omics97
Balancing the transcriptome: leveraging sample similarity to improve measures of gene specificity94
BayesKAT: bayesian optimal kernel-based test for genetic association studies reveals joint genetic effects in complex diseases93
CpGFuse: a holistic approach for accurate identification of methylation states of DNA CpG sites93
mbDecoda: a debiased approach to compositional data analysis for microbiome surveys92
Graph-RPI: predicting RNA–protein interactions via graph autoencoder and self-supervised learning strategies89
Combining power of different methods to detect associations in large data sets88
DeepCheck: multitask learning aids in assessing microbial genome quality87
scAnno: a deconvolution strategy-based automatic cell type annotation tool for single-cell RNA-sequencing data sets87
ETLD: an encoder-transformation layer-decoder architecture for protein contact and mutation effects prediction86
CLT-seq as a universal homopolymer-sequencing concept reveals poly(A)-tail-tuned ncRNA regulation85
Ensemble classification based feature selection: a case of identification on plant pentatricopeptide repeat proteins84
A robust statistical approach for finding informative spatially associated pathways82
Correction to: Addressing barriers in comprehensiveness, accessibility, reusability, interoperability and reproducibility of computational models in systems biology81
SCSMD: Single Cell Consistent Clustering based on Spectral Matrix Decomposition80
Letter regarding article named ‘Is acupuncture effective in the treatment of COVID-19 related symptoms? Based on bioinformatics/network topology strategy’80
Inferring disease-associated circRNAs by multi-source aggregation based on heterogeneous graph neural network79
Addressing scalability and managing sparsity and dropout events in single-cell representation identification with ZIGACL79
A multichannel graph neural network based on multisimilarity modality hypergraph contrastive learning for predicting unknown types of cancer biomarkers77
Protein phosphorylation database and prediction tools76
Making PBPK models more reproducible in practice76
Clustering scRNA-seq data with the cross-view collaborative information fusion strategy76
Detecting tipping points of complex diseases by network information entropy75
ADENet: a novel network-based inference method for prediction of drug adverse events75
Distant metastasis identification based on optimized graph representation of gene interaction patterns74
PMiSLocMF: predicting miRNA subcellular localizations by incorporating multi-source features of miRNAs73
AptaDiff: de novo design and optimization of aptamers based on diffusion models73
A novel prognostic framework for HBV-infected hepatocellular carcinoma: insights from ferroptosis and iron metabolism proteomics72
ULDNA: integrating unsupervised multi-source language models with LSTM-attention network for high-accuracy protein–DNA binding site prediction72
DriverOmicsNet: an integrated graph convolutional network for multi-omics exploration of cancer driver genes72
Clustered tree regression to learn protein energy change with mutated amino acid71
From intuition to AI: evolution of small molecule representations in drug discovery71
Benchmarking of computational methods for m6A profiling with Nanopore direct RNA sequencing71
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