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Bioinformatics Advances|June 7, 2022
HPiP: an R/Bioconductor package for predicting host-pathogen protein-protein interactions from protein sequences using ensemble machine learning approachMatineh Rahmatbakhsh, Mohamed Taha Moutaoufik, Alla Gagarinova, et al.Bioinformatics Advances|April 24, 2023
Different approaches to Imaging Mass Cytometry data analysisVladan MilosevicBioinformatics Advances|May 5, 2023
MonoNet: enhancing interpretability in neural networks via monotonic featuresAn-Phi Nguyen, Dana Lea Moreno, Nicolas Le-Bel, et al.Bioinformatics Advances|April 10, 2023
Motif elucidation in ChIP-seq datasets with a knockout controlDanielle Denisko, Coby Viner, Michael M HoffmanBioinformatics Advances|April 6, 2023
Computational speed-up of large-scale, single-cell model simulations via a fully integrated SBML-based formatArnab Mutsuddy, Cemal Erdem, Jonah R Huggins, et al.Bioinformatics Advances|April 11, 2023
AGRN: accurate gene regulatory network inference using ensemble machine learning methodsDuaa Mohammad Alawad, Ataur Katebi, Md Wasi Ul Kabir, et al.Bioinformatics Advances|March 31, 2023
EasyCellType: marker-based cell-type annotation by automatically querying multiple databasesRuoxing Li, Jianjun Zhang, Ziyi LiBioinformatics Advances|May 1, 2023
Predicting phenotypes from novel genomic markers using deep learningShivani Sehrawat, Keyhan Najafian, Lingling JinBioinformatics Advances|September 30, 2022
metabolomicsR: a streamlined workflow to analyze metabolomic data in RXikun Han, Liming LiangBioinformatics Advances|December 4, 2023
TaxaHFE: a machine learning approach to collapse microbiome datasets using taxonomic structureAndrew Oliver, Matthew Kay, Danielle G LemayPageof 115