AIMC Topic: Antineoplastic Agents
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Leveraging Convolutional Neural Networks for Predicting Symptom Escalation in Chemotherapy Patients: A Temporal Resampling Approach.
Studies in health technology and informatics
Apr 8, 2025
This paper introduces a novel approach for predicting symptom escalation in chemotherapy patients by leveraging Convolutional Neural Networks (CNNs). Accurate forecasting of symptom escalation is crucial in cancer care, as it enables timely intervent...
Applications, challenges and future directions of artificial intelligence in cardio-oncology.
European journal of clinical investigation
Apr 1, 2025
BACKGROUND: The management of cardiotoxicity related to cancer therapies has emerged as a significant clinical challenge, prompting the rapid growth of cardio-oncology. As cancer treatments become more complex, there is an increasing need to enhance ...
A robust ensemble framework for anticancer peptide classification using multi-model voting approach.
Computers in biology and medicine
Apr 1, 2025
Anticancer peptides (ACPs) hold great potential for cancer therapeutics, yet accurately identifying them remains a challenging task due to the complexity of peptide sequences and their interactions with biological systems. In this study, we propose a...
PathSynergy: a deep learning model for predicting drug synergy in liver cancer.
Briefings in bioinformatics
Mar 4, 2025
Cancer is a major public health problem while liver cancer is the main cause of global cancer-related deaths. The previous study demonstrates that the 5-year survival rate for advanced liver cancer is only 30%. Few of the first-line targeted drugs in...
Data imbalance in drug response prediction: multi-objective optimization approach in deep learning setting.
Briefings in bioinformatics
Mar 4, 2025
Drug response prediction (DRP) methods tackle the complex task of associating the effectiveness of small molecules with the specific genetic makeup of the patient. Anti-cancer DRP is a particularly challenging task requiring costly experiments as und...
Discovery of New HER2 Inhibitors via Computational Docking, Pharmacophore Modeling, and Machine Learning.
Molecular informatics
Feb 1, 2025
The human epidermal growth factor receptor 2 (HER2) is a critical oncogene implicated in the development of various aggressive cancers, particularly breast cancer. Discovering novel HER2 inhibitors is crucial for expanding therapeutic options for HER...
Integrative machine learning approach for identification of new molecular scaffold and prediction of inhibition responses in cancer cells using multi-omics data.
Briefings in functional genomics
Jan 15, 2025
MDM2 (Mouse Double Minute 2), a fundamental governor of the p53 tumor suppressor pathway, has garnered significant attention as a favorable target for cancer therapy. Recent years have witnessed the development and synthesis of potent MDM2 inhibitors...
ACP-DPE: A Dual-Channel Deep Learning Model for Anticancer Peptide Prediction.
IET systems biology
Jan 1, 2025
Cancer is a serious and complex disease caused by uncontrolled cell growth and is becoming one of the leading causes of death worldwide. Anticancer peptides (ACPs), as a bioactive peptide with lower toxicity, emerge as a promising means of effectivel...
Prediction of Cisplatin-Induced Acute Kidney Injury Using an Interpretable Machine Learning Model and Electronic Medical Record Information.
Clinical and translational science
Jan 1, 2025
Predicting cisplatin-induced acute kidney injury (Cis-AKI) before its onset is important. We aimed to develop a predictive model for Cis-AKI using patient clinical information based on an interpretable machine learning algorithm. This single-center r...
A Novel Effective Models for Identifying BRCA Patients and Optimizing Clinical Treatments.
Anti-cancer agents in medicinal chemistry
Jan 1, 2025
OBJECTIVE: This study aimed to develop an effective model that identifies high-risk breast cancer (BRCA) patients and optimizes clinical treatments.
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