This journal publishes research on the application of machine learning and artificial intelligence techniques to analyze complex biological and medical data. It focuses on developing and evaluating computational frameworks for tasks such as pattern discovery in electronic health records, risk prediction for postoperative complications, medical image transmission, cancer subtype identification, confounder identification in observational studies, disease diagnosis from biosignals like EEG and ECG, survival prediction, gene fusion detection, and understanding disease mechanisms through multi-omics and microbiome analysis. The research often involves integrating diverse data types and developing novel algorithms for feature extraction, classification, and prediction.
Based on the Think.Check.Submit framework by DOAJ, COPE & OASPA. All data from verified open sources.
Publication & Citation Trend
Articles published
Times cited
2019
2020
2021
2022
2023
2024
2025
2026
Source: OpenAlex · Note: citations accumulate over time so older years appear higher
SJR Quartile by Discipline
Scimago ranks this journal separately in each subject category — its quartile can differ by discipline.
Computational MathematicsQ1
Computational Theory and MathematicsQ1
Computer Science ApplicationsQ1
BiochemistryQ2
GeneticsQ2
Molecular BiologyQ2
Subject Classification
Web of Science Categories
Mathematical & Computational Biology
Scopus Categories
GeneticsBiochemistryComputer Science ApplicationsComputational MathematicsComputational Theory and MathematicsMolecular Biology
Research Topics (OpenAlex)
Bioinformatics and Genomic NetworksGene expression and cancer classificationGenetic Associations and EpidemiologyMachine Learning in BioinformaticsGenomics and Rare DiseasesBiomedical Text Mining and OntologiesGenomics and Phylogenetic StudiesMachine Learning in HealthcareComputational Drug Discovery MethodsSingle-cell and spatial transcriptomics