We plan to create the largest openly accessible collection of information on autism.
Through a collaborative effort that includes researchers from Stanford, UCLA, the New York Genome Center, Cold Spring Harbor Laboratory, and the Simons Foundation, we have amassed a collection of whole genomes and phenotypic measurements on thousands of individuals from families with autism. This platform will help researchers explore connections across data and individuals to more precisely understand autism.
The iHART platform provides a unique and sophisticated portal into a “sandbox” for exploratory data integration, systems biology discovery and pattern recognition, as well as a means for hypothesis testing, experimental design and validation.
Our cloud-based computing and technology platform enables state-of-the-art computational analyses from systems biology, machine learning and inference algorithms to inspire users to exploit the full potential of available data relevant to deciphering autism.
Our initiative aims to characterize the different categories of autism and to provide a comprehensive scientific resource that will allow complex queries across a wide array of data including but not limited to: phenotypes, genomics, proteomics, metabolomics, measurements and imaging of brain activity, biomarker and diagnostic test results, treatment protocols and physician narratives.
Ruzzo EK, Pérez-Cano L, Jung JY, Wang LK, Kashef-Haghighi D, Hartl C, Singh C, Xu J, Hoekstra JN, Leventhal O, Leppä VM, Gandal MJ, Paskov K, Stockham N, Polioudakis D, Lowe JK, Prober DA, Geschwind DH, Wall DP. “Inherited and De Novo Genetic Risk for Autism Impacts Shared Networks.” Cell. 2019 Aug 8;178(4):850-866.e26. doi: 10.1016/j.cell.2019.07.015.