Innovative Machine Learning Strategies for Therapeutic Target Identification in Cancer Drug Discovery

Cancer remains one of the leading causes of morbidity and mortality worldwide, characterized by complex genetic and molecular heterogeneity. Traditional methods for identifying therapeutic targets in oncology are often insufficient to fully grasp the intricate biological networks involved in cancer development and progression. The advent of machine learning and artificial intelligence offers transformative tools capable of analyzing vast, multi-dimensional cancer datasets. By uncovering hidden patterns and associations within genomic, proteomic, and clinical data, machine learning can accelerate the discovery of novel therapeutic targets and personalized treatment strategies in cancer.

This Research Topic aims to highlight the latest advancements and applications of machine learning techniques specifically in the identification of therapeutic targets within cancer drug discovery. We seek to address the unique challenges posed by cancer’s complexity and heterogeneity, demonstrating how innovative computational approaches can revolutionize traditional paradigms. Our goal is to foster interdisciplinary collaboration, bridging computational expertise with clinical oncology, to pave the way for groundbreaking therapeutic interventions against cancer.

This Research Topic will address:

•      Novel Machine Learning Algorithms in Oncology
•      Integration of Multi-Omics Data for Cancer Target Discovery
•      Machine Learning Applications in Single-Cell Cancer Analysis
•      Machine Learning Approaches for Drug Repurposing and Combination Therapies in Cancer
•      Artificial Intelligence in Personalized Cancer Medicine
•     Advanced Machine Learning Techniques for Predicting Cancer Prognosis

We welcome the following article types: Brief Research Report, Case Report, Clinical Trial, Correction, Editorial, General Commentary, Hypothesis & Theory, Methods, Mini Review, Opinion, Original Research, Perspective, Policy and Practice Reviews, Review, Study Protocol, Systematic Review, Technology and Code.

Article types and fees

This Research Topic accepts the following article types, unless otherwise specified in the Research Topic description:

• Brief Research Report
• Case Report
• Clinical Trial
• Data Report
• Editorial
• FAIR² Data
• General Commentary
• Hypothesis and Theory
• Methods
• ... View all formats

Articles that are accepted for publication by our external editors following rigorous peer review incur a publishing fee charged to Authors, institutions, or funders.

Article types

This Research Topic accepts the following article types, unless otherwise specified in the Research Topic description:

• Brief Research Report
• Case Report
• Clinical Trial
• Data Report
• Editorial
• FAIR² Data
• General Commentary
• Hypothesis and Theory
• Methods
• Mini Review
• Opinion
• Original Research
• Perspective
• Review
• Systematic Review
• Technology and Code

Keywords: Machine Learning, Therapeutic Targets, Drug Discovery, Computational Biology, Artificial Intelligence

Important note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.