AIMC Topic: Antineoplastic Agents

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PBAC: A pathway-based attention convolution neural network for predicting clinical drug treatment responses.

Journal of cellular and molecular medicine May 1, 2024
Precise and personalized drug application is crucial in the clinical treatment of complex diseases. Although neural networks offer a new approach to improving drug strategies, their internal structure is difficult to interpret. Here, we propose PBAC ...
Cisplatin Computational Biology Signal Transduction Humans Neural Networks, Computer Neoplasms Antineoplastic Agents Deep Learning
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Using Machine Learning to Predict Unplanned Hospital Utilization and Chemotherapy Management From Patient-Reported Outcome Measures.

JCO clinical cancer informatics Apr 1, 2024
PURPOSE: Adverse effects of chemotherapy often require hospital admissions or treatment management. Identifying factors contributing to unplanned hospital utilization may improve health care quality and patients' well-being. This study aimed to asses...
Humans Aged Adult Male Machine Learning Female Hospitalization Quality of Life Antineoplastic Agents Surveys and Questionnaires Middle Aged Patient Reported Outcome Measures Neoplasms
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Hi-GeoMVP: a hierarchical geometry-enhanced deep learning model for drug response prediction.

Bioinformatics (Oxford, England) Mar 29, 2024
MOTIVATION: Personalized cancer treatments require accurate drug response predictions. Existing deep learning methods show promise but higher accuracy is needed to serve the purpose of precision medicine. The prediction accuracy can be improved with ...
Deep Learning Computational Biology Neoplasms Antineoplastic Agents Humans Neural Networks, Computer Precision Medicine
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Optimal fusion of genotype and drug embeddings in predicting cancer drug response.

Briefings in bioinformatics Mar 27, 2024
Predicting cancer drug response using both genomics and drug features has shown some success compared to using genomics features alone. However, there has been limited research done on how best to combine or fuse the two types of features. Using a vi...
Antineoplastic Agents Deep Learning Genomics Genotype Computational Biology Humans Neural Networks, Computer Neoplasms
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Contrastive learning for enhancing feature extraction in anticancer peptides.

Briefings in bioinformatics Mar 27, 2024
Cancer, recognized as a primary cause of death worldwide, has profound health implications and incurs a substantial social burden. Numerous efforts have been made to develop cancer treatments, among which anticancer peptides (ACPs) are garnering reco...
Humans Deep Learning Computational Biology Neoplasms Peptides Machine Learning Antineoplastic Agents Algorithms
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Eravacycline, an antibacterial drug, repurposed for pancreatic cancer therapy: insights from a molecular-based deep learning model.

Briefings in bioinformatics Mar 27, 2024
BACKGROUND: Pancreatic ductal adenocarcinoma (PDAC) remains a serious threat to health, with limited effective therapeutic options, especially due to advanced stage at diagnosis and its inherent resistance to chemotherapy, making it one of the leadin...
Drug Repositioning Carcinoma, Pancreatic Ductal Tetracyclines Cell Movement Apoptosis Pancreatic Neoplasms Cell Line, Tumor Cell Proliferation Humans Anti-Bacterial Agents Antineoplastic Agents Deep Learning
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Preemptive Forecasting of Symptom Escalation in Cancer Patients Undergoing Chemotherapy.

AMIA ... Annual Symposium proceedings. AMIA Symposium Jan 1, 2024
This study evaluates the utility of machine learning (ML) algorithms in early forecasting of total symptom score changes from daily self-reports of 339 chemotherapy patients. The dataset comprised 12 specific symptoms, with severity and distress for ...
Humans Machine Learning Antineoplastic Agents Algorithms Severity of Illness Index Forecasting Support Vector Machine Neoplasms
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USING ARTIFICIAL INTELLIGENCE AND MACHINE LEARNING APPROACHES TO ENHANCE CANCER THERAPY AND DRUG DISCOVERY: A NARRATIVE REVIEW.

Journal of Ayub Medical College, Abbottabad : JAMC Jan 1, 2024
BACKGROUND: This paper looks at how AI and machine learning have been applied over the last ten years to the development of anti-cancer drugs. By speeding up the synthesis of more desirable compounds and the identification of new ones, artificial int...
Antineoplastic Agents Machine Learning Humans Neoplasms Artificial Intelligence Drug Discovery
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Artificial Intelligence Application for Anti-tumor Drug Synergy Prediction.

Current medicinal chemistry Jan 1, 2024
Currently, the main therapeutic methods for cancer include surgery, radiation therapy, and chemotherapy. However, chemotherapy still plays an important role in tumor therapy. Due to the variety of pathogenic factors, the development process of tumors...
Humans Drug Synergism Artificial Intelligence Neoplasms Antineoplastic Agents Deep Learning
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Peptidic Compound as DNA Binding Agent: Fragment-based Design, Machine Learning, Molecular Modeling, Synthesis, and DNA Binding Evaluation.

Protein and peptide letters Jan 1, 2024
BACKGROUND: Cancer remains a global burden, with increasing mortality rates. Current cancer treatments involve controlling the transcription of malignant DNA genes, either directly or indirectly. DNA exhibits various structural forms, including the G...
Machine Learning Protein Binding Computer Simulation DNA Humans Peptides G-Quadruplexes Molecular Dynamics Simulation Molecular Docking Simulation Binding Sites Drug Design Antineoplastic Agents
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