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BIODATA MINING

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EDITORIAL: EQUITY, DIVERSITY, AND INCLUSION IN DATA SCIENCE

We are proud to publish an editorial co-authored by our co-Editor-in-Chief Jason
H. Moore, on ten important roles for academic leaders to promote equity,
diversity, and inclusion in data science. Read here to see how mentors,
champions, exemplars, practitioners, listeners, empathizers, ombuds, referees,
advocates, and activists can work to create a support network which works
together to chip away at inequality and inequity throughout data science.


ANNOUNCING OUR NEW AIMS & SCOPE!

We 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

Our 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

We 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

 * Recent
 * Most accessed


 1. PREDICTIVE MODELING OF ALS PROGRESSION: AN XGBOOST APPROACH USING CLINICAL
    FEATURES
    
    Authors: Richa Gupta, Mansi Bhandari, Anhad Grover, Taher Al-shehari,
    Mohammed Kadrie, Taha Alfakih and Hussain Alsalman
    
    Content type: Research 2 December 2024


 2. SUPERVISED MULTIPLE KERNEL LEARNING APPROACHES FOR MULTI-OMICS DATA
    INTEGRATION
    
    Authors: Mitja Briscik, Gabriele Tazza, László Vidács, Marie-Agnès Dillies
    and Sébastien Déjean
    
    Content type: Research 23 November 2024


 3. DEEP LEARNING-BASED EMERGENCY DEPARTMENT IN-HOSPITAL CARDIAC ARREST SCORE
    (DEEP EDICAS) FOR EARLY PREDICTION OF CARDIAC ARREST AND CARDIOPULMONARY
    RESUSCITATION IN THE EMERGENCY DEPARTMENT
    
    Authors: Yuan-Xiang Deng, Jyun-Yi Wang, Chia-Hsin Ko, Chien-Hua Huang,
    Chu-Lin Tsai and Li-Chen Fu
    
    Content type: Research 23 November 2024


 4. TRANSCRIPTOME-BASED NETWORK ANALYSIS RELATED TO REGULATORY T CELLS
    INFILTRATION IDENTIFIED RCN1 AS A POTENTIAL BIOMARKER FOR PROGNOSIS IN CLEAR
    CELL RENAL CELL CARCINOMA
    
    Authors: Yang Qixin, Huang Jing, He Jiang, Liu Xueyang, Yu Lu and Li Yuehua
    
    Content type: Research 14 November 2024


 5. DECIPHERING THE TISSUE-SPECIFIC FUNCTIONAL EFFECT OF ALZHEIMER RISK SNPS
    WITH DEEP GENOME ANNOTATION
    
    Authors: Pradeep Varathan Pugalenthi, Bing He, Linhui Xie, Kwangsik Nho,
    Andrew J. Saykin and Jingwen Yan
    
    Content type: Research 13 November 2024

Most recent articles RSS

View all articles


 1. USING GRAPH THEORY TO ANALYZE BIOLOGICAL NETWORKS
    
    Authors: Georgios A Pavlopoulos, Maria Secrier, Charalampos N Moschopoulos,
    Theodoros G Soldatos, Sophia Kossida, Jan Aerts, Reinhard Schneider and
    Pantelis G Bagos
    
    Content type: Review 28 April 2011


 2. TEN QUICK TIPS FOR MACHINE LEARNING IN COMPUTATIONAL BIOLOGY
    
    Authors: Davide Chicco
    
    Content type: Review 8 December 2017


 3. A SURVEY OF VISUALIZATION TOOLS FOR BIOLOGICAL NETWORK ANALYSIS
    
    Authors: Georgios A Pavlopoulos, Anna-Lynn Wegener and Reinhard Schneider
    
    Content type: Review 28 November 2008


 4. APPLICATIONS OF THE MAPREDUCE PROGRAMMING FRAMEWORK TO CLINICAL BIG DATA
    ANALYSIS: CURRENT LANDSCAPE AND FUTURE TRENDS
    
    Authors: Emad A Mohammed, Behrouz H Far and Christopher Naugler
    
    Content type: Review 29 October 2014


 5. SEARCH EXTENSION TRANSFORMS WIKI INTO A RELATIONAL SYSTEM: A CASE FOR
    FLAVONOID METABOLITE DATABASE
    
    Authors: Masanori Arita and Kazuhiro Suwa
    
    Content type: Research 17 September 2008

Most accessed articles RSS

View all articles


EDITORS-IN-CHIEF

Dr Jason Moore, University of Pennsylvania, USA
Dr Marylyn Ritchie, University of Pennsylvania, USA


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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EDITORS' PROFILES

Dr Jason Moore is the Edward Rose Professor of Informatics, Professor of
Biostatistics and Epidemiology, and Professor of Genetics at the Perelman School
of Medicine of the University of Pennsylvania. He serves as the first permanent
Director of the Institute for Biomedical Informatics and founding Director of
the Division of Informatics in the Department of Biostatistics and Epidemiology.
He also serves as Senior Associate Dean for Informatics. His work has been
communicated in more than 400 scientific publications and he serves as PI on
several NIH R01 grants. He has been recognized as a national leader in
informatics through election as a Fellow of the American Association for the
Advancement of Sciences (AAAS) and as a Kavli Fellow of the National Academy of
Sciences (NAS). He was recently elected a Fellow of the American College of
Medical Informatics (ACMI).

Marylyn D. Ritchie, PhD is a Professor in Genetics, Director of the Center for
Translational Bioinformatics, and Associate Director for Bioinformatics in the
Institute for Biomedical Informatics at the University of Pennsylvania School of
Medicine.  Dr. Ritchie is a computational geneticist and biomedical
informatician with a focus on detecting disease-susceptibility genes associated
with common, complex human disease and integrating electronic health records
with genomics. She has expertise in developing novel bioinformatics tools for
complex analysis of big data in genetics, genomics, and clinical databases, in
particular in the area of Pharmacogenomics.  Some of her methods include
Multifactor Dimensionality Reduction (MDR), the Analysis Tool for Heritable and
Environmental Network Associations (ATHENA), and the Biosoftware suite for
annotating/ filtering variants and genomic regions as well as building models of
biological relevance for gene-gene interactions and rare-variant
burden/dispersion tests.  Dr. Ritchie has over 15 years of experience in the
analysis of complex data and has authored over 300 publications (H-index 76).
 She is one of Thomas Reuters Most Highly Cited Researchers for 2014.  Dr.
Ritchie has extensive experience in leading large collaborative efforts; has
been a part of national networks using electronic health records and genomics
data; has excellent organizational and leadership skills.  Dr. Ritchie is well
suited to be a collaborator on this project.


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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
   
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