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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
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  • Publishes cutting-edge data-mining methods
  • Innovative data science and big data research
  • Promotes open peer review and transparency

Publishing innovative data science and big data research, BioData Mining advances research on all aspects of data mining applied to high-dimensional biological and biomedical information. Focusing on computational aspects of knowledge discovery from large-scale genetic, transcriptomic, genomic, proteomic, metabolomic, and clinical data, we publish cutting-edge data-mining methods. The journal features an open peer-review workflow to ensure maximum transparency.

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

Impact Factor: 1.857 (2017) * 

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Science Citation Index Expanded (SciSearch), Journal Citation Reports/Science Edition, PubMedCentral, SCOPUS, INSPEC, EMBASE, Chemical Abstracts Service (CAS), Google Scholar, ACM, BIOSIS, CNKI, Current Abstracts, DOAJ, EBSCO Academic Search, EBSCO Biomedical Reference Collection, EBSCO Discovery Service, EBSCO Polytechnic Studies Collection: India, EBSCO STM Source, EBSCO TOC Premier, Expanded Academic, Health Reference Center Academic, OCLC WorldCat Discovery Service, ProQuest-ExLibris Primo, ProQuest-ExLibris Summon, Reaxys

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  • Journal Citation Reports®
    2017 Impact Factor
  • 1.857
  • 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