Machine learning and feature selection for drug response prediction in precision oncology applications
By Mehreen Ali, Tero Aittokallio. 2018
169 citations recorded by the source corpus.
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 tailored therapies for individual cancer patients. Supervised machine learning algorithms are increasingly being applied to the omics profiles as they enable integrative analyses among the high-dimensional data sets, as well as personalized predictions of therapy responses using multi-omics panels of response-predictive biomarkers identified through feature selection and cross-validation. However, technical variability and frequent missingness in input “big data” require the application of dedicated data preprocessing pipelines that often lead to some loss of information and compressed view of the biological signal. We describe here the state-of-the-art machine learning methods for anti-cancer drug response modeling and prediction and give our perspective on further opportunities to make better use of high-dimensional multi-omics profiles along with knowledge about cancer pathways targeted by anti-cancer compounds when predicting their phenotypic responses.
Subfields: Computational Theory and Mathematics, Molecular Biology
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Citations in the corpus
- Artificial intelligence and machine learning in precision and genomic medicine (2021, methodology): “The superiority of gene expression profile datasets may make it challenging to understand prediction models, but this can be reduced using TANDEM, a two-stage method [137].”
- ARTIFICIAL INTELLIGENCE AND PERSONALIZED MEDICINE (2019, background): “[ 52–54] In addition to the creation of learning systems, there are many initiatives to aggregate data on patients and patient materials to enable data mining and AI-based analyses, for example in cancer contexts,[55] but also for more general settings as well.”