You are all cordially invited to the AMLab seminar on Tuesday June 6 at 16:00 in C3.163 (FNWI, Amsterdam Science Park), where Ted Meeds will give a talk titled “Integrating Cancer Genomics Data using Autoencoders”. Afterwards there are the usual drinks and snacks.
Abstract: Integrating multiple sources of molecular measurements (such as RNA, micro RNA, and methylation data) across pan-cancer cohorts is a promising approach to learn general, non-cohort specific, disease profiles. These profiles provide rich representations of patients that can be used to learn novel subtypes and biomarkers, and are useful for survival prognoses and potentially drug-discovery. However, combining cohorts is challenging in part because the main signal in data is tissue-specific. Special care has to be made to avoid simply “learning the tissue”. In this talk I will describe an approach based on the variational auto-encoder, popular in the deep learning community, to learn an unsupervised latent representation of patients (the disease profile) that explicitly removes tissue/cohort information. Preliminary results indicate that the disease profiles carry little information about tissues and by doing so improves the profiles’ usefulness on other validation tasks, such as predicting cohort-specific survival and DNA mutations.