OOIR: Observatory of International Research

Papers

(The H4-Index of Briefings in Bioinformatics is 68. 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 2020-11-01 to 2024-11-01.)

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
A deep learning method for predicting metabolite–disease associations via graph neural network	157
AlgPred 2.0: an improved method for predicting allergenic proteins and mapping of IgE epitopes	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
Graph representation learning in bioinformatics: trends, methods and applications	97
Drug repositioning based on the heterogeneous information fusion graph convolutional network	97
Current challenges for unseen-epitope TCR interaction prediction and a new perspective derived from image classification	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
Pan-cancer analysis of NLRP3 inflammasome with potential implications in prognosis and immunotherapy in human cancer	87
DeepDDS: deep graph neural network with attention mechanism to predict synergistic drug combinations	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
Attention is all you need: utilizing attention in AI-enabled drug discovery	81
MG-BERT: leveraging unsupervised atomic representation learning for molecular property prediction	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
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
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
Bioinformatics and machine learning approach identifies potential drug targets and pathways in COVID-19	76
Text mining approaches for dealing with the rapidly expanding literature on 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
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
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
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