Bridging Advanced Research and Clinical Applications: AI and Molecular Insights in Gynecologic Oncology

Dear Colleagues,

The Special Issue aims to integrate cutting-edge molecular research with emerging computational tools to enhance prevention, diagnosis, and treatment strategies for gynecologic malignancies. Rapid advances in genomics, transcriptomics, and proteomics have generated unprecedented volumes of biological data, yet their clinical translation remains challenging. Artificial intelligence (AI) and machine learning (ML) now offer transformative opportunities to decode this complexity by identifying novel gene signatures, prognostic and predictive biomarkers, and therapeutic response patterns.

This Special Issue welcomes original research and comprehensive reviews focused on leveraging AI-driven methodologies to interpret molecular landscapes in cancers such as ovarian, endometrial, cervical, vulvar, and rare gynecologic tumors. Topics of interest include, but are not limited to, gene-profiling approaches, biomarker discovery, integrative multi-omics analyses, risk-stratification models, and decision-support algorithms. Studies that demonstrate robust clinical applicability, improve early detection, or support personalized treatment planning are particularly encouraged.

By bringing together experts in oncology, molecular biology, computational science, and translational medicine, this Special Issue aims to foster interdisciplinary collaboration and accelerate the bench-to-bedside transition of innovative diagnostic and therapeutic tools. Ultimately, the goal is to support precision medicine efforts and improve patient outcomes across the spectrum of gynecologic oncology.

We are pleased to invite submissions to the Special Issue “Bridging Advanced Research and Clinical Applications: AI and Molecular Insights in Gynecologic Oncology” in Biomedicines, Section Cancer Biology and Oncology. Gynecologic cancers—including ovarian, endometrial, cervical, vulvar, and other malignancies—continue to pose significant challenges due to their biological complexity, high mortality rates, and limitations in early detection and personalized treatment. Progress in molecular profiling and high-throughput technologies has expanded our understanding of tumor biology, yet translating these insights into clinical impact remains an urgent research priority.

This Special Issue aims to highlight innovative research that harnesses artificial intelligence (AI), machine learning (ML), and multi-omics data to identify robust prognostic and predictive biomarkers, develop gene signatures, and build computational models that support clinical decision making. We welcome original research articles, comprehensive reviews, and methodological contributions that bridge the gap between advanced molecular science and practical clinical applications. Topics of interest include AI-driven biomarker discovery, integrative genomics, predictive modeling of therapeutic response, and translational studies with clear clinical relevance.

We especially encourage interdisciplinary submissions that demonstrate how AI and molecular insights can improve diagnosis, risk stratification, and tailored therapies for patients with gynecologic malignancies. Contributions that propose new frameworks for integrating computational methods into everyday oncology practice are highly valued.

Manuscripts should be submitted online via the MDPI submission system before 31 July 2026. We look forward to your high-quality contributions.

Dr. Mario Fordellone
Dr. Marco La Verde
Guest Editors

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 250 words) can be sent to the Editorial Office for assessment.

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. Biomedicines is an international peer-reviewed open access monthly 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 2600 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.

• gynecologic cancer biomarkers
• AI-driven molecular analysis
• machine learning models
• ovarian cancer gene signatures
• endometrial cancer profiling
• cervical cancer prediction
• multi-omics integration
• prognostic and predictive algorithms
• precision gynecologic oncology
• translational bioinformatics