Network Modeling and Analysis in Health Informatics and Bioinformatics

Scope & Guideline

Advancing methodologies for better health outcomes.

Introduction

Explore the comprehensive scope of Network Modeling and Analysis in Health Informatics and Bioinformatics through our detailed guidelines, including its aims and scope. Stay updated with trending and emerging topics, and delve into declining areas to understand shifts in academic interest. Our guidelines also showcase highly cited topics, featuring influential research making a significant impact. Additionally, discover the latest published papers and those with high citation counts, offering a snapshot of current scholarly conversations. Use these guidelines to explore Network Modeling and Analysis in Health Informatics and Bioinformatics in depth and align your research initiatives with current academic trends.
LanguageEnglish
ISSN2192-6662
PublisherSPRINGER WIEN
Support Open AccessNo
CountryUnited States
TypeJournal
Convergefrom 2012 to 2024
AbbreviationNETW MODEL ANAL HLTH / Netw. Model. Anal. Health
Frequency1 issue/year
Time To First Decision-
Time To Acceptance-
Acceptance Rate-
Home Page-
AddressPrinz-Eugen-Strasse 8-10, A-1040 Vienna, AUSTRIA

Aims and Scopes

The journal "Network Modeling and Analysis in Health Informatics and Bioinformatics" focuses on innovative methodologies and research that leverage network modeling and analysis techniques to address complex problems in health informatics and bioinformatics. It aims to bridge the gap between computational methods and practical applications in health and biological sciences.
  1. Network Biology and Pharmacology:
    Explores the interactions within biological networks, including protein-protein interaction (PPI) networks, and employs network pharmacology to identify drug targets and mechanisms of action.
  2. Artificial Intelligence and Machine Learning:
    Utilizes AI and machine learning techniques for diagnostic imaging, predictive modeling, and personalized medicine, enhancing the accuracy and efficiency of healthcare solutions.
  3. Deep Learning in Medical Imaging:
    Focuses on the application of deep learning algorithms to analyze medical images (e.g., MRI, CT, PET), improve diagnostic accuracy, and automate image segmentation.
  4. Computational Systems Biology:
    Investigates biological systems through computational modeling to understand disease mechanisms and identify therapeutic targets, often integrating multi-omics data.
  5. Epidemiological Modeling:
    Develops mathematical models to simulate disease spread, evaluate public health interventions, and predict outcomes in infectious diseases, particularly relevant in the context of pandemics.
  6. Bioinformatics and Genomics:
    Applies bioinformatics tools for genome analysis, including gene identification, functional annotation, and the exploration of genetic variations associated with diseases.
Recent publications in the journal reflect emerging trends and themes that are gaining traction, highlighting the dynamic nature of research in health informatics and bioinformatics.
  1. Explainable AI in Healthcare:
    There is a growing emphasis on explainable AI methods that enhance the interpretability of machine learning models, particularly in sensitive applications like healthcare diagnostics.
  2. Integration of Multi-Omics Data:
    Research increasingly focuses on integrating various omics data (genomics, proteomics, metabolomics) to gain comprehensive insights into complex health conditions and disease mechanisms.
  3. Telehealth and Digital Health Solutions:
    The COVID-19 pandemic has accelerated interest in telehealth technologies and digital health solutions, which are now a critical area of research for improving patient care and access to healthcare services.
  4. Network-Based Drug Discovery:
    Emerging studies are leveraging network-based approaches for drug discovery, focusing on identifying novel therapeutic targets and understanding drug interactions through network analyses.
  5. Biometric Data Analysis:
    There is a rising trend in utilizing biometric data for health monitoring and diagnosis, with applications in mental health, chronic disease management, and personalized healthcare.

Declining or Waning

As the journal's focus evolves, certain themes have shown a decline in prominence, reflecting shifts in research priorities and methodologies within the field.
  1. Traditional Statistical Methods:
    There has been a noticeable decrease in the use of conventional statistical approaches in favor of more advanced computational techniques and machine learning methodologies.
  2. Basic Laboratory Techniques:
    Research focusing solely on basic laboratory techniques without computational or modeling components is less frequently published, as the field increasingly emphasizes integrative and computational analyses.
  3. Generic Health Informatics Applications:
    Papers that deal with generic applications of health informatics, without a strong focus on network or computational approaches, are becoming less common as the field gravitates towards more specialized and innovative applications.

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