Search International and National Patent Collections

1. (WO2018006152) SYSTEMS AND METHODS FOR GENERATING AND TRAINING CONVOLUTIONAL NEURAL NETWORKS USING BIOLOGICAL SEQUENCES AND RELEVANCE SCORES DERIVED FROM STRUCTURAL, BIOCHEMICAL, POPULATION AND EVOLUTIONARY DATA

Pub. No.:    WO/2018/006152    International Application No.:    PCT/CA2016/050777
Publication Date: Fri Jan 12 00:59:59 CET 2018 International Filing Date: Tue Jul 05 01:59:59 CEST 2016
IPC: G06N 3/08
G06F 19/18
G06F 19/24
Applicants: DEEP GENOMICS INCORPORATED
Inventors: XIONG, Hui Yuan
FREY, Brendan
Title: SYSTEMS AND METHODS FOR GENERATING AND TRAINING CONVOLUTIONAL NEURAL NETWORKS USING BIOLOGICAL SEQUENCES AND RELEVANCE SCORES DERIVED FROM STRUCTURAL, BIOCHEMICAL, POPULATION AND EVOLUTIONARY DATA
Abstract:
We describe systems and methods for generating and training convolutional neural networks using biological sequences and relevance scores derived from structural, biochemical, population and evolutionary data. The convolutional neural networks take as input biological sequences and additional information and output molecular phenotypes. Biological sequences may include DNA, RNA and protein sequences. Molecular phenotypes may include protein-DNA interactions, protein-RNA interactions, protein-protein interactions, splicing patterns, polyadenylation patterns, and microRNA-RNA interactions, which may be described using numerical, categorical or ordinal attributes. Intermediate layers of the convolutional neural networks are weighted using relevance score sequences, for example, conservation tracks. The resulting molecular phenotype convolutional neural networks may be used in genetic testing, to identify drug targets, to identify patients that respond similarly to a drug, to ascertain health risks, or to connect patients that have similar molecular phenotypes.