BioData Mining

SJR Rank #3,407H-index: 46SJR: 1.245Best quartile: Q1Open AccessAPC: US$2,690Listed in DOAJ
Journal website: aims, scope and author guidelines
Publisher

BioMed Central Ltd

Country

United Kingdom

Type

journal

SJR by Edition

2022202500.350.71.051.4

Subject Categories

  • BiochemistryQ1
  • Computational MathematicsQ1
  • Computational Theory and MathematicsQ1
  • Computer Science ApplicationsQ1
  • GeneticsQ1
  • Molecular BiologyQ2

Areas: Biochemistry, Genetics and Molecular Biology · Computer Science · Mathematics

Most published research topics

Bioinformatics and Genomic NetworksGene expression and cancer classificationGenetic Associations and EpidemiologyMachine Learning in BioinformaticsGenomics and Rare DiseasesBiomedical Text Mining and OntologiesMachine Learning in HealthcareGenomics and Phylogenetic StudiesSingle-cell and spatial transcriptomicsComputational Drug Discovery Methods

Source: OpenAlex

Publication Statistics

2025 Docs3-Year DocsCitable Docs03570105140

Documents per Year

202220250255075100

Detailed Information

ISSN

1756-0381

SJR Best Quartile

Q1

Region

Western Europe

Citations per Document (2 years)

9.74

References per Document

58.70

Coverage

2009-2026

Scope

BioData Mining is an open access, open peer-reviewed journal encompassing research on all aspects of data mining applied to high-dimensional biological and biomedical data, focusing on computational aspects of knowledge discovery from large-scale genetic, transcriptomic, genomic, proteomic, and metabolomic data. Topical areas include, but are not limited to: -Development, evaluation, and application of novel data mining and machine learning algorithms. -Adaptation, evaluation, and application of traditional data mining and machine learning algorithms. -Open-source software for the application of data mining and machine learning algorithms. -Design, development and integration of databases, software and web services for the storage, management, retrieval, and analysis of data from large scale studies. -Pre-processing, post-processing, modeling, and interpretation of data mining and machine learning results for biological interpretation and knowledge discovery.

Metrics from SCImago Journal Rank 2025. View on SCImago