Applications of Different Knowledge Graphs and Large Language Models in Diagnosis Cancers

Dear Colleagues,

The diagnosis of cancers is a complex and critical task in healthcare, and the integration of knowledge graphs and large language models has shown great potential in improving accuracy and efficiency. This special issue aims to explore the diverse applications of knowledge graphs and large language models in cancer diagnosis, highlighting their impact, methodologies, and potential for advancing medical research and patient care.

The proposed special issue will feature research articles and review papers that delve into the application of knowledge graphs and large language models in cancer diagnosis. We invite researchers, clinicians, and data scientists to contribute their original work, focusing on the innovative use of these models in areas such as clinical decision support systems, image analysis, genomic data interpretation, biomedical literature mining, and patient risk assessment. The special issue will provide a platform to showcase the latest advancements, discuss challenges and opportunities, and share best practices in leveraging knowledge graphs and large language models to enhance the accuracy and efficiency of cancer diagnosis.

This special issue is expected to attract a wide range of readers, including researchers, oncologists, pathologists, and healthcare professionals, who are interested in the intersection of knowledge graphs, large language models, and cancer diagnosis. By bringing together cutting-edge research and practical applications, this special issue will contribute to disseminating knowledge and fostering collaborations between the medical and computational communities.

Potential topics include but are not limited to the following:

• Deep learning
• Knowledge graphs
• Machine learning
• Large language models
• Cancer diagnosis
• Clinical decision support systems

Dr. Ramin Ranjbarzadeh
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Cancers is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2900 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

• deep learning
• machine learning
• large language models
• cancer diagnosis
• clinical decision support systems
• image analysis
• genomic data interpretation
• biomedical literature mining
• patient risk assessment
• computational oncology
• healthcare informatics
• precision medicine
• artificial intelligence
• medical research
• patient care