8 research outputs found
Erratum to: ‘Mapping the genomic architecture of adaptive traits with interspecific introgressive origin: a coalescent-based approach’
Machine learning and feature selection for drug response prediction in precision oncology applications
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.Peer reviewe
The clinical features of Fournier’s gangrene and the predictivity of the Fournier’s Gangrene Severity Index on the outcomes
Machine learning and feature selection for drug response prediction in precision oncology applications
A community effort to assess and improve drug sensitivity prediction algorithms
International audienc
Chromosome level assembly and comparative genome analysis confirm lager-brewing yeasts originated from a single hybridization
Host–parasite co-evolution and its genomic signature
Studies in diverse biological systems have indicated that host-parasite co-evolution is responsible for the extraordinary genetic diversity seen in some genomic regions, such as major histocompatibility (MHC) genes in jawed vertebrates and resistance genes in plants. This diversity is believed to evolve under balancing selection on hosts by parasites. However, the mechanisms that link the genomic signatures in these regions to the underlying co-evolutionary process are only slowly emerging. We still lack a clear picture of the co-evolutionary concepts and of the genetic basis of the co-evolving phenotypic traits in the interacting antagonists. Emerging genomic tools that provide new options for identifying underlying genes will contribute to a fuller understanding of the co-evolutionary process