Drug candidate identification based on gene expression of treated cells using tensor decomposition-based unsupervised feature extraction for large-scale data.
Y-H TaguchiPublished in: BMC bioinformatics (2019)
The method is specifically designed for large-scale datasets (including hundreds of treatments with compounds), not for conventional small-scale datasets. The obtained results indicate that two compounds that have not been extensively studied, WZ-3105 and CGP-60474, represent promising drug candidates targeting multiple cancers, including melanoma, adenocarcinoma, liver carcinoma, and breast, colon, and prostate cancers, which were analysed in this in silico study.
Keyphrases
- gene expression
- machine learning
- induced apoptosis
- prostate cancer
- dna methylation
- rna seq
- squamous cell carcinoma
- cell cycle arrest
- multidrug resistant
- big data
- electronic health record
- molecular docking
- adverse drug
- deep learning
- oxidative stress
- endoplasmic reticulum stress
- emergency department
- cell death
- radiation therapy
- locally advanced
- artificial intelligence
- single cell