Cancer Informatics

Scope & Guideline

Unlocking New Dimensions in Cancer Understanding

Introduction

Immerse yourself in the scholarly insights of Cancer Informatics with our comprehensive guidelines detailing its aims and scope. This page is your resource for understanding the journal's thematic priorities. Stay abreast of trending topics currently drawing significant attention and explore declining topics for a full picture of evolving interests. Our selection of highly cited topics and recent high-impact papers is curated within these guidelines to enhance your research impact.
LanguageEnglish
ISSN-
PublisherSAGE PUBLICATIONS LTD
Support Open AccessNo
Country-
Type-
Converge-
AbbreviationCANCER INFORM / Cancer Inform.
Frequency1 issue/year
Time To First Decision-
Time To Acceptance-
Acceptance Rate-
Home Page-
Address1 OLIVERS YARD, 55 CITY ROAD, LONDON EC1Y 1SP, ENGLAND

Aims and Scopes

Cancer Informatics focuses on the intersection of computational methods and cancer research, aiming to enhance understanding, diagnosis, and treatment of various cancer types. The journal emphasizes the development and application of advanced analytical techniques to explore cancer genomics, patient data, and therapeutic responses.
  1. Computational Biology and Bioinformatics:
    The journal publishes studies that employ computational techniques to analyze biological data, particularly in the context of cancer genomics and transcriptomics.
  2. Predictive Modeling and Machine Learning:
    Research that utilizes machine learning and statistical modeling to predict cancer outcomes, treatment responses, and patient survival is a key focus area.
  3. Immune Profiling and Therapeutic Insights:
    The journal covers research on the role of the immune system in cancer, including immune-related biomarkers and therapeutic strategies utilizing immunotherapy.
  4. Multi-Omics Approaches:
    Studies integrating data from various omics layers (genomics, proteomics, metabolomics) to provide comprehensive insights into cancer biology are highlighted.
  5. Clinical Decision Support Systems:
    The development of computational tools and systems aimed at aiding clinical decisions in cancer treatment and management is a prominent theme in the journal.
Recent publications in Cancer Informatics indicate a dynamic shift towards innovative research themes that leverage advanced computational techniques and multi-disciplinary approaches. The following emerging scopes are gaining traction within the journal.
  1. Artificial Intelligence in Cancer Research:
    The integration of AI and machine learning techniques for cancer diagnosis, prognosis, and treatment prediction is rapidly increasing, reflecting advancements in computational capabilities.
  2. Immune-Oncology and Biomarker Discovery:
    There is a growing focus on understanding immune responses in cancer and identifying biomarkers that can predict responses to immunotherapy.
  3. Patient-Centric and Real-World Data Analysis:
    Research utilizing real-world data to assess treatment efficacy, patient outcomes, and healthcare delivery is on the rise, emphasizing its importance in clinical decision-making.
  4. Integration of Multi-Omics Data:
    The trend towards utilizing multi-omics approaches to provide a holistic view of cancer biology and therapy response is increasingly prevalent.
  5. Computational Drug Discovery and Development:
    Emerging studies focus on computational methods for drug discovery, including the identification of novel therapeutic targets and the evaluation of drug combinations.

Declining or Waning

As the field of cancer informatics evolves, certain themes have shown a decline in frequency or relevance within the journal's publications. The following areas appear to be waning, reflecting shifts in research priorities and methodologies.
  1. Traditional Chemotherapy Studies:
    Research focused solely on traditional chemotherapy approaches is becoming less prominent, as there is a growing emphasis on personalized medicine and targeted therapies.
  2. Epidemiological Studies Without Computational Focus:
    While epidemiological research remains important, studies that do not incorporate computational analysis or bioinformatics are appearing less frequently in the journal.
  3. Single-Gene Studies:
    Investigations centered around single gene analyses are declining, as the field moves towards comprehensive genomic profiling and multi-gene signatures for better predictive capabilities.
  4. Basic Laboratory Techniques Without Computational Integration:
    Papers that describe basic laboratory methods without incorporating computational insights or analyses are increasingly rare, as the focus shifts towards integrative approaches.

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