AIMC Topic: Antineoplastic Agents

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Comparative evaluation of feature reduction methods for drug response prediction.

Scientific reports
Personalized medicine aims to tailor medical treatments to individual patients, and predicting drug responses from molecular profiles using machine learning is crucial for this goal. However, the high dimensionality of the molecular profiles compared...

Improving the prediction of chemotherapy dose-limiting toxicity in colon cancer patients using an AI-CT-based 3D body composition of the entire L1-L5 lumbar spine.

Supportive care in cancer : official journal of the Multinational Association of Supportive Care in Cancer
PURPOSE: Chemotherapy dose-limiting toxicities (DLT) pose a significant challenge in successful colon cancer treatment. Body composition analysis may enable tailored interventions thereby supporting the mitigation of chemotherapy toxic effects. This ...

AI-based classification of anticancer drugs reveals nucleolar condensation as a predictor of immunogenicity.

Molecular cancer
BACKGROUND: Immunogenic cell death (ICD) inducers are often identified in phenotypic screening campaigns by the release or surface exposure of various danger-associated molecular patterns (DAMPs) from malignant cells. This study aimed to streamline t...

Machine-learning derived identification of prognostic signature to forecast head and neck squamous cell carcinoma prognosis and drug response.

Frontiers in immunology
INTRODUCTION: Head and neck squamous cell carcinoma (HNSCC), a highly heterogeneous malignancy is often associated with unfavorable prognosis. Due to its unique anatomical position and the absence of effective early inspection methods, surgical inter...

Discovery of anticancer peptides from natural and generated sequences using deep learning.

International journal of biological macromolecules
Anticancer peptides (ACPs) demonstrate significant potential in clinical cancer treatment due to their ability to selectively target and kill cancer cells. In recent years, numerous artificial intelligence (AI) algorithms have been developed. However...

Novel artificial intelligence-based identification of drug-gene-disease interaction using protein-protein interaction.

BMC bioinformatics
The evaluation of drug-gene-disease interactions is key for the identification of drugs effective against disease. However, at present, drugs that are effective against genes that are critical for disease are difficult to identify. Following a diseas...

Novel machine learning model for predicting cancer drugs' susceptibilities and discovering novel treatments.

Journal of biomedical informatics
BACKGROUND AND OBJECTIVE: Timely treatment is crucial for cancer patients, so it's important to administer the appropriate treatment as soon as possible. Because individuals can respond differently to a given drug due to their unique genomic profiles...

SAGCN: Using Graph Convolutional Network With Subgraph-Aware for circRNA-Drug Sensitivity Identification.

IEEE/ACM transactions on computational biology and bioinformatics
Circular RNAs (circRNAs) play a significant role in cancer development and therapy resistance. There is substantial evidence indicating that the expression of circRNAs affects the sensitivity of cells to drugs. Identifying circRNAs-drug sensitivity a...

iDCNNPred: an interpretable deep learning model for virtual screening and identification of PI3Ka inhibitors against triple-negative breast cancer.

Molecular diversity
Triple-negative breast cancer (TNBC) lacks estrogen, progesterone, and HER2 expression, accounting for 15-20% of breast cancer cases. It is challenging due to low therapeutic response, heterogeneity, and aggressiveness. The PI3Ka isoform is a promisi...