BioData Mining

Scope & Guideline

Exploring the Frontiers of Bioinformatics and Molecular Biology

Introduction

Delve into the academic richness of BioData Mining with our guidelines, detailing its aims and scope. Our resource identifies emerging and trending topics paving the way for new academic progress. We also provide insights into declining or waning topics, helping you stay informed about changing research landscapes. Evaluate highly cited topics and recent publications within these guidelines to align your work with influential scholarly trends.
LanguageEnglish
ISSN1756-0381
PublisherBMC
Support Open AccessYes
CountryUnited Kingdom
TypeJournal
Convergefrom 2009 to 2024
AbbreviationBIODATA MIN / BioData Min.
Frequency1 issue/year
Time To First Decision-
Time To Acceptance-
Acceptance Rate-
Home Page-
AddressCAMPUS, 4 CRINAN ST, LONDON N1 9XW, ENGLAND

Aims and Scopes

BioData Mining focuses on the intersection of biological data analysis and computational methodologies, emphasizing the application of advanced data-driven techniques to address complex biomedical questions. The journal promotes innovative research in bioinformatics, machine learning, and systems biology, fostering a multidisciplinary approach to understanding biological systems.
  1. Bioinformatics and Computational Biology:
    The journal emphasizes the development and application of computational methods for analyzing biological data, including genomic, transcriptomic, and proteomic datasets.
  2. Machine Learning and Artificial Intelligence:
    A core focus on machine learning techniques to enhance predictive modeling, classification, and data interpretation in various biomedical contexts.
  3. Integration of Multi-Omics Data:
    Research involving the integration of diverse omics data types (genomics, transcriptomics, proteomics) to provide comprehensive insights into biological processes and disease mechanisms.
  4. Clinical Data Mining and Health Informatics:
    Exploration of methods for analyzing health records, clinical data, and patient outcomes to improve healthcare delivery and disease management.
  5. Development of Predictive Models:
    Constructing and validating predictive models for disease risk assessment, treatment outcomes, and patient stratification using advanced computational techniques.
Recent publications in BioData Mining reveal several emerging themes that reflect the evolving landscape of biomedical research. These trends indicate a growing emphasis on innovative methodologies and interdisciplinary approaches.
  1. Explainable AI in Healthcare:
    There is a rising interest in developing explainable machine learning models to enhance transparency and trust in AI applications within healthcare, facilitating better decision-making.
  2. Use of Generative Models:
    Recent studies are increasingly utilizing generative models, such as Generative Adversarial Networks (GANs), for tasks like data imputation and simulation of complex biological phenomena.
  3. Personalized Medicine Approaches:
    A trend towards personalized medicine, leveraging machine learning to tailor treatments based on individual patient data and characteristics, is gaining momentum.
  4. Integration of Wearables and Real-Time Data:
    Research focusing on the integration of wearable technology and real-time health monitoring data is emerging, highlighting the importance of continuous health assessment.
  5. Ethical AI and Data Privacy:
    Emerging discussions around the ethical implications of AI in healthcare, including data privacy and security, are becoming prominent, reflecting societal concerns over biomedical data usage.

Declining or Waning

While BioData Mining continues to thrive in several domains, certain themes appear to be losing traction in recent publications. This decline may reflect shifts in research priorities or the maturation of methodologies.
  1. Basic Statistical Methods:
    There has been a noticeable decrease in the publication of papers focused solely on traditional statistical methods without integration with machine learning or computational techniques, suggesting a shift towards more complex analytical frameworks.
  2. Single-Omics Analysis:
    Research solely focused on single-omics studies (e.g., genomics alone) seems to be waning, as the trend moves towards multi-omics approaches that provide a more holistic view of biological systems.
  3. Descriptive Studies without Computational Insight:
    Papers that primarily describe biological phenomena without employing computational or machine learning techniques are becoming less common, indicating a preference for data-driven approaches.
  4. Focus on Non-Disease Specific Applications:
    Research that does not specifically address disease mechanisms or clinical applications is declining, as researchers increasingly seek to publish work with direct relevance to health outcomes.

Similar Journals

CANCER INVESTIGATION

Transforming knowledge into breakthroughs in cancer care.
Publisher: TAYLOR & FRANCIS INCISSN: 0735-7907Frequency: 10 issues/year

CANCER INVESTIGATION is a distinguished peer-reviewed journal published by Taylor & Francis Inc, dedicated to the advancing field of cancer research and oncology. With an ISSN of 0735-7907 and E-ISSN of 1532-4192, this journal has been a pivotal resource for professionals and researchers since its inception in 1983, continually contributing to the evolving landscape of cancer investigation until its convergence in 2024. CANCER INVESTIGATION boasts noteworthy rankings in 2023, including Q3 in Cancer Research and Q2 in Medicine (miscellaneous), highlighting its relevance and impact in these critical areas. The journal's commitment to disseminating innovative research and comprehensive reviews makes it an essential platform for those engaged in cancer studies and related disciplines. While currently not available as an open-access publication, CANCER INVESTIGATION remains an invaluable tool for understanding the complexities of cancer, offering insights that drive scientific advancements and improve patient outcomes.

Bioinformatics Advances

Charting New Territories in Bioinformatics
Publisher: OXFORD UNIV PRESSISSN: Frequency: 1 issue/year

Bioinformatics Advances, published by Oxford University Press, is an esteemed academic journal that serves as a vital platform for the dissemination of innovative research in the rapidly evolving fields of bioinformatics and computational biology. With a promising E-ISSN of 2635-0041, this journal has made significant strides since its inception in 2021, achieving a commendable Q1 ranking in both the Computer Science Applications and Genetics categories, alongside respectable Q2 rankings in Molecular Biology and Structural Biology as of 2023. Though currently not an open-access publication, its critical insights cater to an audience keen on advancing knowledge and technology in genomic studies and data analytics. The journal emphasizes high-quality research and aims to facilitate the integration of computational techniques within biological sciences, making it an essential resource for researchers, professionals, and students alike who seek to stay at the forefront of bioinformatics advancements.

PLoS Computational Biology

Unlocking Insights Through Computational Innovation
Publisher: PUBLIC LIBRARY SCIENCEISSN: 1553-734XFrequency: 12 issues/year

PLoS Computational Biology is a premier open-access journal published by the Public Library of Science, committed to advancing the understanding of complex biological data through computational approaches. Since its inception in 2005, the journal has made significant strides in the fields of Cellular and Molecular Neuroscience, Computational Theory and Mathematics, Ecology, Genetics, and Molecular Biology, achieving a notable Q1 ranking in various categories as of 2023. With an exceptional impact factor and an esteemed ranking—such as Rank #23/176 in Computational Theory and Mathematics—PLoS Computational Biology provides a vital platform for researchers, professionals, and students to disseminate their cutting-edge findings and insights. The journal's open-access model ensures that high-quality research is freely accessible worldwide, fostering collaboration and innovation across disciplines. Located in San Francisco, CA, it serves as a hub for the global scientific community, making it an indispensable resource for anyone at the forefront of computational biology and its diverse applications.

Statistical Analysis and Data Mining

Unlocking Insights Through Statistical Innovation
Publisher: WILEYISSN: 1932-1864Frequency: 6 issues/year

Statistical Analysis and Data Mining is a leading journal published by WILEY, dedicated to exploring the latest advancements in statistical methods and data mining techniques. With an ISSN of 1932-1864 and an E-ISSN of 1932-1872, this journal serves as a significant platform for researchers and professionals in statistical analysis, computer science applications, and information systems. Covering a wide range of topics from innovative analytical methodologies to emerging data mining algorithms, the journal aims to disseminate high-quality research that contributes to the evolving landscape of data science. Ranked in the Q2 category for the fields of Analysis, Computer Science Applications, and Information Systems in 2023, it emphasizes its relevance and impact within academia. While it offers limited Open Access options, the insights shared in this publication are integral for those wishing to stay ahead in fast-paced research and data-driven industries. Since its inception in 2008 and continuing through 2024, Statistical Analysis and Data Mining invites submissions that reflect rigorous empirical research coupled with practical implications, making it a vital resource for the academic community.

COMPUTERS IN BIOLOGY AND MEDICINE

Elevating Medical Insights with Computational Excellence
Publisher: PERGAMON-ELSEVIER SCIENCE LTDISSN: 0010-4825Frequency: 16 issues/year

COMPUTERS IN BIOLOGY AND MEDICINE is a prestigious academic journal published by Pergamon-Elsevier Science Ltd, dedicated to advancing the fields of Computer Science Applications and Health Informatics. With an impressive impact factor and ranking within the Q1 quartile for both categories, this journal plays a crucial role in disseminating high-quality research findings that influence cutting-edge developments at the intersection of computing and healthcare. Covering a broad range of topics from computational biology to medical informatics, it serves as a vital resource for researchers, professionals, and students striving to harness technology for medical advancements. The journal has been publishing since 1970 and continues to evolve, incorporating the latest trends and innovations in the field, thereby ensuring that it remains a key contributor to scientific inquiry and knowledge. With accessible content and a global reach, COMPUTERS IN BIOLOGY AND MEDICINE invites submissions that elevate the understanding and application of computational methods in biological and medical contexts.

Journal of Bioinformatics and Computational Biology

Exploring the Intersection of Biology and Computer Science
Publisher: WORLD SCIENTIFIC PUBL CO PTE LTDISSN: 0219-7200Frequency: 6 issues/year

The Journal of Bioinformatics and Computational Biology, published by WORLD SCIENTIFIC PUBL CO PTE LTD, serves as a significant platform for disseminating innovative research in the dynamic fields of bioinformatics and computational biology. With an ISSN of 0219-7200 and an E-ISSN of 1757-6334, this journal facilitates the exchange of ideas and advancements from its inception in 2003 and continues to be pivotal through 2024. Despite its classification in the lower quartiles—Q4 in Biochemistry and Q4 in Molecular Biology, along with Q3 in Computer Science Applications—the journal remains a valuable resource for researchers and students alike, as it emphasizes interdisciplinary approaches essential for tackling complex biological problems through computational methods. Located in Singapore, the journal encourages submissions of high-quality, peer-reviewed articles that offer insights into computational techniques that empower biological research. Although this journal does not offer open access options, its contributions to research are increasingly recognized across various academic platforms. As the field evolves rapidly, this journal continues to attract a growing readership, making it an essential reference point for anyone interested in the intersection of biology and computer science.

Bioinformatics and Biology Insights

Fostering collaboration at the forefront of biological research.
Publisher: SAGE PUBLICATIONS LTDISSN: 1177-9322Frequency: 1 issue/year

Bioinformatics and Biology Insights is a premier open-access journal dedicated to advancing the frontiers of bioinformatics and biology. Published by SAGE Publications Ltd, this journal is a leading platform for disseminating high-quality research that integrates computational methods with biological insights. Since its inception in 2007, the journal has garnered significant recognition, reflecting its commitment to excellence, as evidenced by its impressive impact factor and a strong presence across multiple quartiles in applied mathematics, biochemistry, and computational sciences. With a rank of Q1 in both Applied Mathematics and Computational Mathematics and various other notable rankings in related fields, it serves as an essential resource for researchers, professionals, and students seeking cutting-edge knowledge and innovations. The journal's open-access model ensures that findings are readily available, fostering collaboration and advancement in this dynamic field. Through its comprehensive scope and rigorous peer-review process, Bioinformatics and Biology Insights continues to play a pivotal role in shaping the future of biological research and computational methodologies.

Data Science and Engineering

Advancing the frontiers of data and technology.
Publisher: SPRINGERNATUREISSN: 2364-1185Frequency: 4 issues/year

Data Science and Engineering is a premier open access journal published by SPRINGERNATURE, dedicated to advancing the fields of data science, artificial intelligence, computational mechanics, and information systems. Since its inception in 2016, this journal has rapidly established itself as a leader in the academic community, boasting an impressive Q1 ranking in multiple computer science categories, including Artificial Intelligence, Software, and Information Systems. With a commitment to disseminating high-quality research, it caters to a diverse audience of researchers, professionals, and students eager to explore the intersection of data and technology. The journal's robust global reach, combined with its respected reputation, empowers authors to share their findings widely, facilitating breakthroughs and innovations across the digital landscape. Join the vibrant community of scholars contributing to this integral field of study, and stay informed with the latest research by accessing the journal freely online.

ADVANCED ENGINEERING INFORMATICS

Empowering Innovation Through Open Access Research
Publisher: ELSEVIER SCI LTDISSN: 1474-0346Frequency: 4 issues/year

ADVANCED ENGINEERING INFORMATICS is a prestigious journal published by Elsevier Science Ltd, dedicated to the interdisciplinary fields of Artificial Intelligence and Information Systems. Established in 2002, this journal serves as a vital platform for researchers and practitioners to disseminate groundbreaking insights and innovations that shape the future of engineering and technological integration. With an impressive impact factor and ranked in the Q1 category for both Artificial Intelligence and Information Systems in 2023, it holds a prominent position, with Scopus rankings placing it in the 92nd percentile among 394 journals in Computer Science Information Systems and the 87th percentile among 350 journals in Computer Science Artificial Intelligence. ADVANCED ENGINEERING INFORMATICS embraces an Open Access model, ensuring that cutting-edge research is accessible to a global audience, fostering collaboration and development across academic and professional circles. The journal is committed to advancing knowledge and influencing practice, paving the way for the next generation of technologies that enhance engineering informatics.

Intelligent Data Analysis

Empowering Scholars with Cutting-edge Data Methodologies
Publisher: IOS PRESSISSN: 1088-467XFrequency: 6 issues/year

Intelligent Data Analysis is a highly regarded journal published by IOS Press, specializing in the fields of Artificial Intelligence, Computer Vision, and Pattern Recognition. With its ISSN 1088-467X and E-ISSN 1571-4128, the journal has been a cornerstone of scholarly communication since its inception in 1997, serving as a vital resource for researchers, professionals, and students engaged in advancing methodologies and applications in intelligent data analysis. The journal maintains its significance with impressive Scopus ranks, indicating its notable position within the academic community. Although currently not an Open Access journal, Intelligent Data Analysis offers a wealth of insights and findings, encouraging collaboration and knowledge exchange among its readership. With an impact factor reflective of its rigorous selection processes, the journal traverses a broad range of topics, contributing to ongoing discussions and innovations in its field. As the journal looks toward shaping future research until 2024 and beyond, it remains a pivotal platform for disseminating cutting-edge research and fostering academic inquiry.