Guest Editors
Prof. Jialin Meng
Email: mengjialin@ahmu.edu.cn
Affiliation: Department of Urology, First Affiliated Hospital of Anhui Medical University, Hefei, China.
Department of Chemistry, Institute of Biomimetic Materials & Chemistry, University of Science and Technology of China, Hefei, China.
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Research Interests: prevalence of CP/CPPS; mechanisms of chronic prostatitis; mice model; multi-omics molecular subtyping; tumor exosome
Dr. Xiaofan Lu
Email: lux@igbmc.fr
Affiliation: Department of Cancer and Functional Genomics, Institute of Genetics and Molecular and Cellular Biology, CNRS/INSERM/UNISTRA, Illkirch 67400, France.
Homepage:
Research Interests: Molecular subtypes; genetic landscape; precision oncology; MOVICS
Summary
Dear Colleagues,
Machine learning has been widely applied in disease subtyping, particularly in the integration of molecular and clinical data to improve the accuracy and efficiency of diagnosis and treatment. By analyzing large-scale datasets, machine learning algorithms can identify patterns and relationships between different disease subtypes and their corresponding clinical features, such as imaging, lab tests, and symptoms. This approach allows for the identification of novel disease subtypes, as well as the characterization of existing subtypes with greater precision. Furthermore, machine learning models can be used to predict treatment response and patient outcomes, providing personalized treatment recommendations for each patient. Overall, the application of machine learning in disease subtyping has the potential to greatly improve patient care and outcomes.
We are pleased to invite you to submit papers with the results achieved novel insight for disease subtyping, including but not limited to improve clinical diagnosis, prognosis prediction or personalized treatment. Articles concern methods such as multi-omics, single-cell, or real-world validation are welcome to the current Special Issue.
Prof. Jialin Meng
Dr. Xiaofan Lu
Research areas may include (but not limited to) the following:
· Deep learning
· Feature selection
· Multi-omics integration
· Unsupervised clustering
· Transfer learning
· Graph-based methods
· Precision medicine
· Network analysis
· Reinforcement learning.
· Unsupervised learning
· Clustering algorithms
· Biomarker discovery
· Explainable artificial intelligence
· Stratified medicine
· Predictive modeling
· Dimensionality reduction
· Subtype identification
· Stratification based on treatment response
Keywords
Machine Learning, Disease Subtyping, Personalized Treatment, Multi-omics, Prognosis Prediction, Real-world Validation
Published Papers
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