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Announcement: New Editor-in-Chief of BioData Mining 

New Content ItemThe journal is excited to announce that Dr. Nicholas P. Tatonetti will be taking over the role of Editor-in-Chief from Dr. Marylyn D. Ritchie. Dr. Tatonetti will join Dr. Jason Moore, who will continue his term as Editor-in-Chief.  

Dr. Nicholas Tatonetti is Professor and Vice Chair of Operations in the Department of Computational Biomedicine and Associate Director of Computational Oncology in the Cancer Center at Cedars-Sinai Medical Center. They received a PhD from Stanford University where they focused on the development of novel statistical and computational methods for observational data mining. Over the past 14 years, Dr. Tatonetti has applied these methods to drug safety surveillance and the discovery of dangerous adverse drug effects and has identified and validated previously unknown serious drug-drug interactions. Their lab at Cedars-Sinai is focused on using massive-scale real clinical and molecular data for making robust and validated scientific discoveries, with a particular focus on detecting, explaining, and validating drug effects and drug interactions.  Dr. Tatonetti has published over 150 peer-reviewed scientific publications across medicine, systems biology, machine learning, and bioinformatics. Dr. Tatonetti is passionate about the integration of real-world data (such as those stored in the electronic health records) and high-dimensional biological data (captured using next-generation sequencing, high-throughput screening, and other "omics" technologies) to reimagine and rescale the scientific method.  

BioData Mining has greatly benefited from the expertise and leadership of the current Editors-in-Chief, and we would like to take this opportunity to thank them for their years of service in supporting and developing the journal. As Dr. Ritchie steps down from her role as Editor-in-Chief, we wish her all the best for the future.  

Announcing our new Aims & Scope!

New Content ItemWe are excited to announce that our Aims & Scope has undergone some changes, incorporating an encouragement of submissions pertaining to Artificial Intelligence, Machine Learning, and Visual Analytics, and expanding the data types we consider, to include imaging, electronic health records, biobanks, environmental data, social and behavioral data, wearable devices, and social media data. Please click here to check out the new Aims & Scope and types of submissions that we readily encourage you to submit.

Featured series: Next-generation machine learning

New Content ItemOur new thematic series, edited by Jason Moore and Marylyn Ritchie, seeks manuscripts on the topic of machine learning. We are interested in both original research and review papers, especially those that address new and novel machine learning methods and their application to biological and biomedical big data. The series is open, and you can find out more about it (including submission instructions), here.

The Biomedical Informatics Roundtable Podcast

New Content ItemWe are happy to announce that our Editors-in-Chief, Jason H. Moore and Marylyn D. Ritchie, have recently launched a podcast! The Biomedical Informatics Roundtable Podcast aims to bring you discussion of hot topics, recent papers, news, conferences, open data, open-source software, and advice for trainees as well as interviews and spotlights biomedical informatics colleagues from around the world. Please check out Dr Moore and Dr Ritchie’s podcast, here!

Articles

  1. Authors: Georgios A Pavlopoulos, Maria Secrier, Charalampos N Moschopoulos, Theodoros G Soldatos, Sophia Kossida, Jan Aerts, Reinhard Schneider and Pantelis G Bagos

Aims and scope

BioData Mining is an open access, open peer-reviewed, informatics journal encompassing research on all aspects of Artificial Intelligence (AI), Machine Learning, and Visual Analytics, applied to high-dimensional biological and biomedical data, focusing on computational aspects of knowledge discovery from large-scale genetic, genomic, metabolomic data and/or electronic health records, social determinants of health, and environmental exposure data. Please see here for more information on data types and topical areas.

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Annual Journal Metrics

  • Citation Impact 2023
    Journal Impact Factor: 4.0
    5-year Journal Impact Factor: 3.7
    Source Normalized Impact per Paper (SNIP): 1.413
    SCImago Journal Rank (SJR): 0.958

    Speed 2023
    Submission to first editorial decision (median days): 15
    Submission to acceptance (median days): 171

    Usage 2023
    Downloads: 400,374
    Altmetric mentions: 146
     

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