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Life Sciences - Systems Biology and Bioinformatics | BioData Mining

BioData Mining

BioData Mining

Editor-in-Chief: M. Ritchie; J. Moore

ISSN: 1756-0381 (electronic version)

Journal no. 13040

BioMed Central

Open access BioMed Central
  • Publishes cutting-edge data-mining methods
  • Innovative data science and big data research
  • Promotes open peer review and transparency
BioData Mining publishes original articles 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.

Related subjects » Computational Science & Engineering - Database Management & Information Retrieval - Information Systems and Applications - Systems Biology and Bioinformatics

Impact Factor: 1.577 (2016) * 

Journal Citation Reports®

Abstracted/Indexed in 

PubMed, PubMedCentral, SCOPUS, EMBASE, Chemical Abstracts Service (CAS), Google Scholar, Academic OneFile, ACM, Current Abstracts, EBSCO Academic Search, EBSCO Biomedical Reference Collection, EBSCO Polytechnic Studies Collection: India, EBSCO STM Source, Expanded Academic, Health Reference Center Academic, OCLC, SCImago, Summon by ProQuest

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  • Journal Citation Reports®
    2016 Impact Factor
  • 1.577
  • Aims and Scope

    Aims and Scope

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    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.
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  • Writing Resources: Training for Authors