BMC Bioinformatics

Papers
(The H4-Index of BMC Bioinformatics is 42. 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 2020-03-01 to 2024-03-01.)
ArticleCitations
CellProfiler 4: improvements in speed, utility and usability561
MitoHiFi: a python pipeline for mitochondrial genome assembly from PacBio high fidelity reads253
PnB Designer: a web application to design prime and base editor guide RNAs for animals and plants244
PACVr: plastome assembly coverage visualization in R217
So you think you can PLS-DA?164
Statistical power for cluster analysis100
TSEBRA: transcript selector for BRAKER88
DPDDI: a deep predictor for drug-drug interactions88
ATLAS: a Snakemake workflow for assembly, annotation, and genomic binning of metagenome sequence data86
Prediction of heart disease and classifiers’ sensitivity analysis84
MADGAN: unsupervised medical anomaly detection GAN using multiple adjacent brain MRI slice reconstruction78
ChromoMap: an R package for interactive visualization of multi-omics data and annotation of chromosomes74
Prediction of liquid–liquid phase separating proteins using machine learning73
Graph-based prediction of Protein-protein interactions with attributed signed graph embedding72
Propedia: a database for protein–peptide identification based on a hybrid clustering algorithm67
COPLA, a taxonomic classifier of plasmids64
MethylNet: an automated and modular deep learning approach for DNA methylation analysis64
multiGSEA: a GSEA-based pathway enrichment analysis for multi-omics data63
Conditional permutation importance revisited60
DrugShot: querying biomedical search terms to retrieve prioritized lists of small molecules59
PyClone-VI: scalable inference of clonal population structures using whole genome data57
Malaria parasite detection in thick blood smear microscopic images using modified YOLOV3 and YOLOV4 models56
Testing the advantages and disadvantages of short- and long- read eukaryotic metagenomics using simulated reads54
MicrobeAnnotator: a user-friendly, comprehensive functional annotation pipeline for microbial genomes54
Broad-coverage biomedical relation extraction with SemRep51
Evaluating metagenomics tools for genome binning with real metagenomic datasets and CAMI datasets51
To denoise or to cluster, that is not the question: optimizing pipelines for COI metabarcoding and metaphylogeography51
Amino acid encoding for deep learning applications50
GPU accelerated adaptive banded event alignment for rapid comparative nanopore signal analysis49
A random forest based computational model for predicting novel lncRNA-disease associations48
Web-based LinRegPCR: application for the visualization and analysis of (RT)-qPCR amplification and melting data47
A learning-based method for drug-target interaction prediction based on feature representation learning and deep neural network47
Robust principal component analysis for accurate outlier sample detection in RNA-Seq data46
Simulating ComBat: how batch correction can lead to the systematic introduction of false positive results in DNA methylation microarray studies46
NBZIMM: negative binomial and zero-inflated mixed models, with application to microbiome/metagenomics data analysis45
A representation learning model based on variational inference and graph autoencoder for predicting lncRNA-disease associations45
VADR: validation and annotation of virus sequence submissions to GenBank45
MiBiOmics: an interactive web application for multi-omics data exploration and integration44
DeepSuccinylSite: a deep learning based approach for protein succinylation site prediction44
Correlation AnalyzeR: functional predictions from gene co-expression correlations44
Keras R-CNN: library for cell detection in biological images using deep neural networks44
CellSeg: a robust, pre-trained nucleus segmentation and pixel quantification software for highly multiplexed fluorescence images43
SAveRUNNER: an R-based tool for drug repurposing42
Selecting single cell clustering parameter values using subsampling-based robustness metrics42
0.049463987350464