AIMC Topic: Antineoplastic Agents
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Prediction of pathological complete response to chemotherapy for breast cancer using deep neural network with uncertainty quantification.
Medical physics
Oct 6, 2024
BACKGROUND: The I-SPY 2 trial is a national-wide, multi-institutional clinical trial designed to evaluate multiple new therapeutic drugs for high-risk breast cancer. Previous studies suggest that pathological complete response (pCR) is a viable indic...
iACP-DFSRA: Identification of Anticancer Peptides Based on a Dual-channel Fusion Strategy of ResCNN and Attention.
Journal of molecular biology
Oct 1, 2024
Anticancer peptides (ACPs) have been widely applied in the treatment of cancer owing to good safety, rational side effects, and high selectivity. However, the number of ACPs that have been experimentally validated is limited as identification of ACPs...
Wee1 inhibitor optimization through deep-learning-driven decision making.
European journal of medicinal chemistry
Sep 29, 2024
Deep learning has gained increasing attention in recent years, yielding promising results in hit screening and molecular optimization. Herein, we employed an efficient strategy based on multiple deep learning techniques to optimize Wee1 inhibitors, w...
Machine learning- a new paradigm in nanoparticle-mediated drug delivery to cancerous tissues through the human cardiovascular system enhanced by magnetic field.
Scientific reports
Sep 27, 2024
Nanoparticle-mediated drug delivery offers a promising approach to targeted cancer therapy, leveraging the ability of nanoparticles to deliver therapeutic agents directly to cancerous tissues with minimal impact on surrounding healthy cells. The pres...
Integrating machine learning and multi-omics analysis to develop an asparagine metabolism immunity index for improving clinical outcome and drug sensitivity in lung adenocarcinoma.
Immunologic research
Sep 25, 2024
Lung adenocarcinoma (LUAD) is a malignancy affecting the respiratory system. Most patients are diagnosed with advanced or metastatic lung cancer due to the fact that most of their clinical symptoms are insidious, resulting in a bleak prognosis. Given...
Combining clinical and molecular data for personalized treatment in acute myeloid leukemia: A machine learning approach.
Computer methods and programs in biomedicine
Sep 18, 2024
BACKGROUND AND OBJECTIVE: The standard of care in Acute Myeloid Leukemia patients has remained essentially unchanged for nearly 40 years. Due to the complicated mutational patterns within and between individual patients and a lack of targeted agents ...
Can Machine Learning Overcome the 95% Failure Rate and Reality that Only 30% of Approved Cancer Drugs Meaningfully Extend Patient Survival?
Journal of medicinal chemistry
Sep 10, 2024
Despite implementing hundreds of strategies, cancer drug development suffers from a 95% failure rate over 30 years, with only 30% of approved cancer drugs extending patient survival beyond 2.5 months. Adding more criteria without eliminating nonessen...
Imatinib adherence prediction using machine learning approach in patients with gastrointestinal stromal tumor.
Cancer
Sep 6, 2024
BACKGROUND: Nonadherence to imatinib is common in patients with gastrointestinal stromal tumor (GIST), which is associated with poor prognosis and financial burden. The primary aim of this study was to investigate the adherence rate in patients with ...
Artificial Intelligence-Enhanced Risk Stratification of Cancer Therapeutics-Related Cardiac Dysfunction Using Electrocardiographic Images.
Circulation. Cardiovascular quality and outcomes
Sep 2, 2024
BACKGROUND: Risk stratification strategies for cancer therapeutics-related cardiac dysfunction (CTRCD) rely on serial monitoring by specialized imaging, limiting their scalability. We aimed to examine an application of artificial intelligence (AI) to...
Humans
Artificial Intelligence
Retrospective Studies
Male
Ventricular Function, Left
Stroke Volume
Middle Aged
Female
Risk Factors
Aged
Ventricular Dysfunction, Left
Anthracyclines
United States
Trastuzumab
Cardiotoxicity
Electrocardiography
Risk Assessment
Predictive Value of Tests
Antineoplastic Agents
Time Factors
MCMVDRP: a multi-channel multi-view deep learning framework for cancer drug response prediction.
Journal of integrative bioinformatics
Sep 2, 2024
Drug therapy remains the primary approach to treating tumours. Variability among cancer patients, including variations in genomic profiles, often results in divergent therapeutic responses to analogous anti-cancer drug treatments within the same coho...
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