BioData Mining - Springer Nature | 2024 Impact Factor:6.1 | Cite Score: 8.5 | Q1

Impact Factor and Journal Rank of BioData Mining

• About: BioData Mining Journal is a peer-reviewed, open-access publication under Springer Nature that focuses on the extraction of useful information from complex biological data. The journal highlights innovative methodologies and techniques for data mining and knowledge discovery in the fields of bioinformatics, computational biology, and biomedical research.
• Objective:
  The objective of BioData Mining Journal is to provide a platform for researchers, practitioners, and professionals to share cutting-edge research and developments in the field of biological data mining. The journal aims to bridge the gap between computational methods and their application in biological and medical sciences, fostering interdisciplinary collaboration and innovation.
• Focus Areas:
  BioData Mining Journal covers a wide range of topics, including but not limited to: data mining algorithms, machine learning techniques, statistical methods, computational tools, and their applications in genomics, proteomics, metabolomics, and other areas of biological research. The journal encourages submissions that demonstrate novel approaches to analyzing and interpreting biological data, contributing to the advancement of personalized medicine, systems biology, and related fields.
• Peer Review Process:
  The journal employs a rigorous peer-review process to ensure the publication of high-quality research. Each submission is evaluated by experts in the field for its originality, technical soundness, significance, and clarity. The peer review process provides authors with valuable feedback to enhance the quality and impact of their work.
• Innovation and Impact:
  BioData Mining Journal emphasizes innovation and impact in the field of biological data mining. By publishing pioneering research that introduces new techniques, tools, and applications, the journal seeks to advance the capabilities of data mining in uncovering biological insights. The journal promotes the dissemination of research that can drive progress in bioinformatics and computational biology, ultimately contributing to improved healthcare outcomes.
• Global Reach and Accessibility:
  The journal is internationally recognized and attracts submissions from researchers around the world. BioData Mining Journal is committed to making high-quality research accessible to a global audience, supporting the open access model to ensure that knowledge is freely available to researchers, practitioners, and the public. This approach fosters widespread dissemination and utilization of research findings.
• Interdisciplinary Collaboration:
  BioData Mining Journal encourages interdisciplinary collaboration by welcoming contributions from diverse fields such as computer science, biology, medicine, and statistics. By promoting the integration of computational and biological sciences, the journal aims to drive innovative solutions to complex biological problems and support the development of interdisciplinary research networks.

• Journal Home:  Journal Homepage

• Editor-in-Chief:  Prof. Jason Moore

• Scope: Computational Biology:
  Development and application of computational methods and software for analyzing biological data.
• Genomics and Proteomics:
  Studies involving the analysis and interpretation of genome and proteome data.
• Systems Biology:
  Research on the integration and modeling of biological systems and their components.
• Bioinformatics Tools and Databases:
  Creation and enhancement of bioinformatics tools and databases to support biological research.
• Machine Learning in Biology:
  Application of machine learning techniques to address biological questions and improve data analysis.
• Statistical Methods for BioData:
  Development and use of statistical methods to analyze and interpret complex biological data.
• Big Data in Biology:
  Handling and analysis of large-scale biological datasets, including challenges and solutions.
• Data Mining in Healthcare:
  Application of data mining techniques to healthcare data for improving diagnosis, treatment, and understanding of diseases.
• Latest Research Topics for PhD in Machine Learning
• Latest Research Topics for PhD in Artificial Intelligence
• Latest Research Topics for PhD in Data Mining

• Print ISSN:  1756-0381

  Electronic ISSN:  

• Abstracting and Indexing:  Scopus, Science Citation Index Expanded

• Imapct Factor 2024:  6.1

• Subject Area and Category:  Biochemistry, Genetics and Molecular Biology,Biotechnology,Computer Science ,Computer Science Applications,Engineering ,Biomedical Engineering

• Publication Frequency:  

• H Index:  42

• Best Quartile:

  Q1:  Computational Mathematics

  Q2:  

  Q3:  

  Q4:  

• Cite Score:  8.5

• SNIP:  1.570

• Journal Rank(SJR):  1.068

• Latest Articles:   Latest Articles in BioData Mining

• Guidelines for Authors: BioData Mining Author Guidelines

• Paper Submissions: Paper Submissions in BioData Mining

• Publisher:  BioMed Central Ltd.

  Country:  United Kingdom

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