Interdisciplinary Sciences-Computational Life Sciences

Scope & Guideline

Bridging Disciplines for Groundbreaking Scientific Discoveries

Introduction

Welcome to your portal for understanding Interdisciplinary Sciences-Computational Life Sciences, featuring guidelines for its aims and scope. Our guidelines cover trending and emerging topics, identifying the forefront of research. Additionally, we track declining topics, offering insights into areas experiencing reduced scholarly attention. Key highlights include highly cited topics and recently published papers, curated within these guidelines to assist you in navigating influential academic dialogues.
LanguageEnglish
ISSN1913-2751
PublisherSPRINGER HEIDELBERG
Support Open AccessNo
CountryGermany
TypeJournal
Convergefrom 2009 to 2024
AbbreviationINTERDISCIP SCI / Interdiscip. Sci.
Frequency4 issues/year
Time To First Decision-
Time To Acceptance-
Acceptance Rate-
Home Page-
AddressTIERGARTENSTRASSE 17, D-69121 HEIDELBERG, GERMANY

Aims and Scopes

The journal 'Interdisciplinary Sciences-Computational Life Sciences' focuses on the convergence of computational techniques and biological sciences, emphasizing innovative methodologies and interdisciplinary approaches.
  1. Computational Drug Discovery and Development:
    This area encompasses the use of machine learning, deep learning, and computational modeling to predict drug-target interactions, drug repurposing, and the identification of potential therapeutic compounds.
  2. Biological Data Analysis and Prediction:
    The journal emphasizes advanced analytical techniques for biological data, including gene expression data, protein interactions, and biomarker identification, often utilizing neural networks and other machine learning methods.
  3. Neuroimaging and Brain-Related Research:
    Research focusing on the application of computational methods to analyze neuroimaging data, aiming to understand various neurological disorders and brain functions.
  4. Systems Biology and Network Analysis:
    This includes the modeling and analysis of biological networks, such as gene regulatory networks and protein-protein interaction networks, to uncover complex biological relationships.
  5. Interdisciplinary Applications of AI in Life Sciences:
    The journal highlights the integration of artificial intelligence in various life science domains, promoting collaborative research that bridges computer science and biology.
Recent publications in the journal reveal several emerging themes that highlight the evolving landscape of computational life sciences.
  1. Integrative Multi-Omics Approaches:
    There is a growing trend towards integrating data from multiple omics layers (genomics, proteomics, metabolomics) to gain comprehensive insights into biological processes and disease mechanisms.
  2. Graph-Based Learning and Network Models:
    Research utilizing graph-based models and network learning techniques is on the rise, showcasing their effectiveness in modeling complex biological interactions and relationships.
  3. AI and Machine Learning Innovations:
    Innovations in artificial intelligence, particularly deep learning models, are increasingly being applied to various biological challenges, including drug discovery, disease prediction, and medical imaging.
  4. Personalized Medicine and Precision Health:
    Emerging themes around personalized medicine are gaining traction, focusing on how computational methods can tailor treatments based on individual patient data.
  5. Real-Time Data Analysis in Clinical Settings:
    The trend towards real-time data analysis and decision support systems in clinical environments is becoming more pronounced, driven by advancements in computational technologies.

Declining or Waning

While the journal maintains a diverse scope, certain themes have shown a decline in frequency over the recent years, indicating a shift in research focus.
  1. Traditional Statistical Methods in Biology:
    There has been a noticeable decline in the publication of papers relying solely on traditional statistical methods, as researchers increasingly favor machine learning and advanced computational approaches.
  2. Basic Laboratory Techniques:
    Papers focusing on conventional laboratory techniques and methodologies appear to be waning, with a shift towards computational modeling and bioinformatics.
  3. Single-Omics Studies:
    Research that focuses on single-omics approaches (such as genomics or proteomics in isolation) is less prevalent, as the trend moves towards multi-omics integration for a more holistic understanding of biological systems.
  4. General Reviews without Novel Insights:
    The journal has seen fewer general review articles that do not present novel insights or methodologies, reflecting a preference for original research contributions that push the boundaries of current knowledge.

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