DATE: Thu, Sept 21, 2017
TIME: 1 pm
TITLE: Deep BioNLP for Definition-based Embedding of Gene Products and Functional Analysis of Genes
PRESENTER: Ahmad Pesaranghader
Dalhousie University

Many important applications in computational molecular biology such as gene clustering, protein function prediction, protein interaction evaluation and disease gene prioritization require functional similarity. Moreover, to expedite the selection of candidate genes for gene-disease research, genetic association studies, biomarker and drug target selection, and animal models of human diseases, it is essential to have search engines that can retrieve genes by their functions from proteome databases. By the fast advancement in this domain, these engines can substantially gain benefits from the functional similarity calculation of gene products. In this talk, by restating the expressive power of deep neural networks, we introduce a novel deep BioNLP model which efficiently measures the functional similarity of proteins and gene products (e.g. microRNA and mRNA). For this purpose, the model largely benefits from - natural language definition of - (taxonomical) biological descriptors of genes and the semantic similarity conveyed by these entities.