Download full-text PDF |
Source |
|---|
Publication Analysis
Top Keywords
Similar Publications
Accurate survival prediction of patients with long-bone metastases is challenging, but important for optimizing treatment. The Skeletal Oncology Research Group (SORG) machine learning algorithm (MLA) has been previously developed and internally validated to predict 90-day and 1-year survival. External validation showed promise in the United States and Taiwan.
View Article and Find Full Text PDFAdvancing precision cancer immunotherapy drug development, administration, and response prediction with AI-enabled Raman spectroscopy.
Front Immunol
January 2025
Department of Surgery, Stanford School of Medicine, Stanford University Medical Center, Stanford, CA, United States.
Molecular characterization of tumors is essential to identify predictive biomarkers that inform treatment decisions and improve precision immunotherapy development and administration. However, challenges such as the heterogeneity of tumors and patient responses, limited efficacy of current biomarkers, and the predominant reliance on single-omics data, have hindered advances in accurately predicting treatment outcomes. Standard therapy generally applies a "one size fits all" approach, which not only provides ineffective or limited responses, but also an increased risk of off-target toxicities and acceleration of resistance mechanisms or adverse effects.
View Article and Find Full Text PDFDeveloping and validating a drug recommendation system based on tumor microenvironment and drug fingerprint.
Front Artif Intell
January 2025
General Thoracic Surgery, General Hospital of Ningxia Medical University, Yinchuan, China.
Introduction: Tumor heterogeneity significantly complicates the selection of effective cancer treatments, as patient responses to drugs can vary widely. Personalized cancer therapy has emerged as a promising strategy to enhance treatment effectiveness and precision. This study aimed to develop a personalized drug recommendation model leveraging genomic profiles to optimize therapeutic outcomes.
View Article and Find Full Text PDFMachine learning and molecular dynamics simulations predict potential TGR5 agonists for type 2 diabetes treatment.
Front Chem
January 2025
African Society for Bioinformatics and Computational Biology, Cape Town, South Africa.
Introduction: Treatment of type 2 diabetes (T2D) remains a significant challenge because of its multifactorial nature and complex metabolic pathways. There is growing interest in finding new therapeutic targets that could lead to safer and more effective treatment options. Takeda G protein-coupled receptor 5 (TGR5) is a promising antidiabetic target that plays a key role in metabolic regulation, especially in glucose homeostasis and energy expenditure.
View Article and Find Full Text PDFUsing a machine learning algorithm and clinical data to predict the risk factors of disease recurrence after adjuvant treatment of advanced-stage oral cavity cancer.
Tzu Chi Med J
July 2024
Department of Radiation Oncology, Hualien Tzu Chi Hospital, Buddhist Tzu Chi Medical Foundation, Hualien, Taiwan.
Objectives: Head-and-neck cancer is a major cancer in Taiwan. Most patients are in the advanced stage at initial diagnosis. In addition to primary surgery, adjuvant therapy, including chemotherapy and radiotherapy, is also necessary to treat these patients.
View Article and Find Full Text PDFWant AI Summaries of new PubMed Abstracts delivered to your In-box?
Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!