[HTML][HTML] The rise of deep learning in drug discovery
Highlights•Deep learning technology has gained remarkable success.•We highlight the
recent applications of deep learning in drug discovery research.•Some popular deep …
recent applications of deep learning in drug discovery research.•Some popular deep …
Machine learning and feature selection for drug response prediction in precision oncology applications
M Ali, T Aittokallio - Biophysical reviews, 2019 - Springer
In-depth modeling of the complex interplay among multiple omics data measured from
cancer cell lines or patient tumors is providing new opportunities toward identification of …
cancer cell lines or patient tumors is providing new opportunities toward identification of …
Gene expression based inference of cancer drug sensitivity
S Chawla, A Rockstroh, M Lehman, E Ratther… - Nature …, 2022 - nature.com
Inter and intra-tumoral heterogeneity are major stumbling blocks in the treatment of cancer
and are responsible for imparting differential drug responses in cancer patients. Recently …
and are responsible for imparting differential drug responses in cancer patients. Recently …
GraphCDR: a graph neural network method with contrastive learning for cancer drug response prediction
Predicting the response of a cancer cell line to a therapeutic drug is an important topic in
modern oncology that can help personalized treatment for cancers. Although numerous …
modern oncology that can help personalized treatment for cancers. Although numerous …
Improving prediction of phenotypic drug response on cancer cell lines using deep convolutional network
Background Understanding the phenotypic drug response on cancer cell lines plays a vital
role in anti-cancer drug discovery and re-purposing. The Genomics of Drug Sensitivity in …
role in anti-cancer drug discovery and re-purposing. The Genomics of Drug Sensitivity in …
Rethinking drug repositioning and development with artificial intelligence, machine learning, and omics
Pharmaceutical industry and the art and science of drug development are sorely in need of
novel transformative technologies in the current age of digital health and artificial …
novel transformative technologies in the current age of digital health and artificial …
Machine learning and data mining frameworks for predicting drug response in cancer: An overview and a novel in silico screening process based on association rule …
A major challenge in cancer treatment is predicting the clinical response to anti-cancer
drugs on a personalized basis. The success of such a task largely depends on the ability to …
drugs on a personalized basis. The success of such a task largely depends on the ability to …
Predicting cancer drug response using a recommender system
Motivation As we move toward an era of precision medicine, the ability to predict patient-
specific drug responses in cancer based on molecular information such as gene expression …
specific drug responses in cancer based on molecular information such as gene expression …
An overview of machine learning methods for monotherapy drug response prediction
F Firoozbakht, B Yousefi… - Briefings in …, 2022 - academic.oup.com
For an increasing number of preclinical samples, both detailed molecular profiles and their
responses to various drugs are becoming available. Efforts to understand, and predict, drug …
responses to various drugs are becoming available. Efforts to understand, and predict, drug …
Predicting patient response with models trained on cell lines and patient-derived xenografts by nonlinear transfer learning
Preclinical models have been the workhorse of cancer research, producing massive
amounts of drug response data. Unfortunately, translating response biomarkers derived from …
amounts of drug response data. Unfortunately, translating response biomarkers derived from …