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Harnessing Innovative Machine Learning Techniques to Combat Drug Resistance in Solid Tumors

Edited by:

Wendy Mao, PhD, BioNTech SE, United States
Hao Zhang, PhD, Chongqing Medical University, China

Submission Status: Open   |   Submission Deadline: 28 November 2025
 

Journal of Translational Medicine is calling for submissions to our Collection on Harnessing Innovative Machine Learning Techniques to Combat Drug Resistance in Solid Tumors. 

Topics of Interest
The collection welcomes original research, reviews, commentary and methodology articles on the following topics:

•    Ensemble Learning Techniques;
•    Applications in predicting drug resistance profiles;
•    Comparative studies of ensemble methods versus traditional approaches;
•    Deep Learning in Oncology;
•    Neural network architectures for tumor characterization;
•    Image analysis and interpretation for drug response prediction;
•    Reinforcement Learning;
•    Adaptive treatment strategies using reinforcement learning;
•    Simulation models for drug resistance evolution;
•    Explainable AI;
•    Methods for interpreting machine learning models in clinical settings;
•    The role of explainability in improving treatment outcomes;
•    Quantum Computing Applications;
•    Quantum algorithms for optimizing drug discovery processes;
•    Examples of quantum-enhanced machine learning techniques in oncology.
 

Image credit: © Gorodenkoff / Stock.adobe.com

New Content ItemThis Collection supports and amplifies research related to SDG 3: Good Health & Well-Being.

About the Collection

In recent years, the challenge of drug resistance in solid tumors has emerged as a significant barrier to effective cancer treatment, often leading to treatment failure and poor patient outcomes. As traditional therapeutic approaches struggle to overcome this hurdle, there is a growing interest in leveraging innovative machine learning techniques to enhance our understanding of drug resistance mechanisms and improve therapeutic strategies. This article collection aims to gather cutting-edge research that explores the intersection of machine learning and oncology, focusing on how advanced algorithms can be applied to predict drug resistance, optimize treatment plans, and ultimately foster personalized medicine approaches in cancer care. By fostering interdisciplinary collaboration and sharing novel insights, this collection seeks to pave the way for transformative advancements in the fight against cancer.

Aims and Scope
This article collection explores integrating advanced machine learning methodologies—including ensemble learning, deep learning, reinforcement learning, explainable AI, and quantum computing—in understanding and overcoming drug resistance in solid tumors. We invite contributions highlighting novel algorithms, examples of innovative machine learning technique implementation, and interdisciplinary approaches, ultimately leading to improved therapeutic outcomes and enhanced patient care in oncology.

Topics of Interest
The collection welcomes original research, reviews, commentary and methodology articles on the following topics:

•    Ensemble Learning Techniques;
•    Applications in predicting drug resistance profiles;
•    Comparative studies of ensemble methods versus traditional approaches;
•    Deep Learning in Oncology;
•    Neural network architectures for tumor characterization;
•    Image analysis and interpretation for drug response prediction;
•    Reinforcement Learning;
•    Adaptive treatment strategies using reinforcement learning;
•    Simulation models for drug resistance evolution;
•    Explainable AI;
•    Methods for interpreting machine learning models in clinical settings;
•    The role of explainability in improving treatment outcomes;
•    Quantum Computing Applications;
•    Quantum algorithms for optimizing drug discovery processes;
•    Examples of quantum-enhanced machine learning techniques in oncology.
 

Meet the Guest Editors

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Wendy Mao, PhD, BioNTech SE, United States

Wendy Mao is a Senior Scientist, TCR Discovery at BioNTech, where works on streamlining TCR discovery, screening, and characterization to improve the breadth of available cell therapies for cancer, both for personalized therapies as well as shared targets. Previously, Wendy was a Scientist at Kite Pharma working on discovery and characterization of shared antigen TCRs for use as off-the-shelf cancer therapies. 

Wendy received her Ph.D. in Immunology from Johns Hopkins University. Her academic training was focused on understanding the interplay between epigenetics and immune response to prostate cancer, as well as the effect of altering metabolic pathways on efficacy of PARP inhibition. 

Hao Zhang, PhD, Chongqing Medical University, China

Hao Zhang, MD, Post-Doc, is an Associate Researcher in the Department of Neurosurgery at The Second Affiliated Hospital of Chongqing Medical University. His research primarily focuses on the tumor microenvironment, cancer immunotherapy, small molecule drugs, molecular mechanisms, bioinformatics, targeted therapy, and signal transduction. To date, Dr. Zhang has published 47 papers as the first or corresponding author in renowned journals such as Signal Transduction and Targeted Therapy, Molecular Cancer, Journal of Hematology & Oncology, Cancer Communications, Theranostics, and Journal of Experimental Clinical Cancer Research. His work has garnered a total of 3,089 citations, and he holds an H-index of 28.

Dr. Zhang has led four research projects, including a Youth Project funded by the National Natural Science Foundation. His contributions to the field have been widely recognized, and he was ranked as the 51st most influential figure in neurosurgery in China over the past five years. 


Submission Guidelines

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This Collection welcomes submission of original research articles. Should you wish to submit a different article type, please read our submission guidelines to confirm that type is accepted by the journal. 

Articles for this Collection should be submitted via our submission system, Editorial Manager. Please select the appropriate Collection title “Harnessing Innovative Machine Learning Techniques to Combat Drug Resistance in Solid Tumors" from the dropdown menu.

Articles will undergo the journal’s standard peer-review process and are subject to all the journal’s standard policies. Articles will be added to the Collection as they are published.

The Editors have no competing interests with the submissions which they handle through the peer-review process. The peer-review of any submissions for which the Editors have competing interests is handled by another Editorial Board Member who has no competing interests.