PLoS Computational Biology | 2024 Impact Factor:4.3 | Cite Score:7.2 | Q1

Impact Factor and Journal Rank of PLoS Computational Biology

• About: The PLoS Computational Biology Journal is a peer-reviewed open-access journal published by the Public Library of Science (PLoS). It focuses on research that uses computational methods to understand and model biological systems. The journal covers a wide range of topics, including genomics, systems biology, bioinformatics, and computational neuroscience. It serves as a platform for scholars, researchers, and practitioners to publish original research articles, reviews, and case studies that advance the field of computational biology.
• Objective: The primary objective of the PLoS Computational Biology Journal is to promote research and innovation in computational biology. The journal aims to explore theories, methodologies, algorithms, and applications that enhance our understanding of biological processes through computational techniques. By publishing high-quality research, the journal seeks to address the challenges and opportunities in integrating computational methods with biological research.
• Interdisciplinary Focus: The PLoS Computational Biology Journal adopts an interdisciplinary approach, welcoming contributions from various fields related to computational biology, including but not limited to, Bioinformatics, Genomics, Systems Biology, Computational Neuroscience, Evolutionary Biology, Structural Biology, Mathematical Biology, Biophysics, Data Science, Artificial Intelligence in Biology. This interdisciplinary perspective fosters collaboration and innovation, leading to the development of computational tools and models that address real-world biological challenges in diverse applications.
• Global Reach and Impact: With a broad international readership and authorship, the PLoS Computational Biology Journal has a global reach and impact. Its publications contribute to the dissemination of knowledge and advancements in computational biology worldwide. The journal open-access model ensures that its content is freely available to researchers, educators, and practitioners, driving progress in areas such as personalized medicine, drug discovery, and ecosystem modeling.
• High Standards and Rigorous Review: Maintaining high academic standards, the PLoS Computational Biology Journal conducts a rigorous peer-review process. Each submitted manuscript undergoes thorough evaluation by experts in the field to ensure the quality, originality, and scientific rigor of the research. This stringent review process upholds the integrity and reputation of the journal, ensuring that only high-quality and impactful research is published.
• Significance: The PLoS Computational Biology Journal plays a significant role in advancing research and practice in the field of computational biology. By providing a platform for the publication of cutting-edge research findings, the journal contributes to the growth of knowledge and innovation in computational methods and their applications in biology. It serves as an essential resource for researchers, practitioners, educators, and students interested in understanding and leveraging computational tools to solve complex biological problems and improve human health.

• Journal Home:  Journal Homepage

• Editor-in-Chief:  Feilim Mac Gabhann

• Scope: PLoS Computational Biology is a premier open-access, peer-reviewed journal published by the Public Library of Science (PLoS). It focuses on the intersection of biology and computational science, providing a platform for the dissemination of significant research in computational biology. The journal covers a wide range of topics that employ computational techniques to understand and model biological systems. Here is an overview of its key focus areas and scope:
• 1. Computational and Systems Biology:
  Research on the development and application of computational models to understand complex biological systems and processes.
  Advancements in systems biology, including the modeling of cellular networks, regulatory pathways, and multi-scale biological phenomena.
• 2. Bioinformatics and Genomics:
  Exploration of computational methods for analyzing and interpreting genomic and other high-throughput biological data.
  Research on sequence analysis, genome assembly, annotation, comparative genomics, and functional genomics.
• 3. Structural Biology and Molecular Modeling:
  Advancements in computational techniques for modeling the structure and dynamics of biological macromolecules.
  Research on protein folding, molecular dynamics simulations, and computational drug design.
• 4. Evolutionary Biology and Phylogenetics:
  Exploration of computational approaches to study evolutionary processes and relationships among species.
  Research on phylogenetic analysis, molecular evolution, and evolutionary dynamics.
• 5. Systems Medicine and Computational Pharmacology:
  Advancements in computational methods for understanding disease mechanisms and developing therapeutic strategies.
  Research on disease modeling, drug-target interactions, and personalized medicine.
• 6. Neuroscience and Computational Neurobiology:
  Exploration of computational models to study the structure and function of the nervous system.
  Research on neural networks, brain modeling, and the computational basis of cognition and behavior.
• 7. Mathematical Biology:
  Advancements in the application of mathematical techniques to model biological processes and systems.
  Research on mathematical modeling, quantitative analysis, and theoretical biology.
• 8. Cellular and Molecular Networks:
  Exploration of computational methods to study the interactions and dynamics of cellular and molecular networks.
  Research on network modeling, network inference, and network-based analysis of biological data.
• 9. Computational Ecology and Environmental Biology:
  Advancements in computational techniques for studying ecological and environmental systems.
  Research on population dynamics, ecosystem modeling, and the impact of environmental changes on biological systems.
• 10. Data Science and Machine Learning in Biology:
  Exploration of data science and machine learning techniques for analyzing and interpreting biological data.
  Research on predictive modeling, data integration, and the application of artificial intelligence in biological research.
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• Print ISSN:  1553734X

  Electronic ISSN:  15537358

• Abstracting and Indexing:  Scopus, Science Citation Index Expanded

• Imapct Factor 2024:  4.3

• Subject Area and Category:  Agricultural and Biological Sciences Ecology, Evolution, Behavior and Systematics,Biochemistry, Genetics and Molecular Biology ,Genetics,Molecular Biology,Computer Science,Computational Theory and Mathematics,Environmental Science,Ecology,Mathematics,Modeling and Simulation,Neuroscience,Cellular and Molecular Neuroscience

• Publication Frequency:  

• H Index:  227

• Best Quartile:

  Q1:  Cellular and Molecular Neuroscience

  Q2:  

  Q3:  

  Q4:  

• Cite Score:  7.2

• SNIP:  1.137

• Journal Rank(SJR):  1.503

• Latest Articles:   Latest Articles in PLoS Computational Biology

• Guidelines for Authors: PLoS Computational Biology Author Guidelines

• Paper Submissions: Paper Submissions in PLoS Computational Biology

• Publisher:  Public Library of Science

  Country:  United States

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