The Wall Lab at Stanford University

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Projects


Guess What?

This game is a research study out of Stanford University for parents of children between the ages of 3 and 12 years. Families who participate in this game are helping researchers in the Wall Lab use machine learning and artificial intelligence to analyze behaviors expressed by children while interacting with family members via home video.


Autism Therapy on Glass

The Wall Lab, the Winograd Lab, and Sension are building a new tool on Google Glass, an interdisciplinary effort bringing together some of the brightest minds in psychiatry, behavioral science, human-computer interaction and artificial intelligence to create an assistive tool for facial emotion recognition. The Autism Glass Project seeks to provide individuals with challenges navigating social cues with a clinically validated therapeutic device to aid in interpreting facial expressions.


iHart

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.


GapMap

GapMap engages the community of families with autism to capture geographic, diagnostic, and resource usage information to yield a more complete and dynamically updated understanding of autism resource epidemiology.


The Gut Microbiome in Autism

This study aims to improve our understanding of the link between gut microbiome functionality, genome variation, and ASD phenotype, and reveal the specific mechanisms by which the gut microbiome interacts with autism-related alleles to produce and modify ASD.


NGS Large-Scale Variant Detection in Autism

This is a large-scale genomics analysis of next-generation sequencing (NGS) datasets from autism case-control and family data.


Machine Learning Diagnostic Classifiers

We develop machine learning classifiers aimed at diagnosing ASD and related conditions, using simpler criteria than gold-standard methods with the hope of getting children diagnosed sooner.


Wearable Psychiatry

Using wearables to detect and quantify bipolar disorder.


Connectome

A social network analysis of autism-focused research trends across the country starting in the year 2000.


SysMed

SysMed is a research site devoted to the genetic causes of Autism Spectrum Disorder.


Home Video Project

This proof of concept project evaluated the feasibility of applying our machine learning classifiers to home videos to evaluate accuracy for detection of autism spectrum disorder in a non-clinical setting.


“Route 66” to Autism

Identifying a common molecular signature in ASD gene expression

Integration of all gene expression experiments conducted on different types of tissue from autism cases and matched controls to determine if a unique ASD signature exists.


Genehawk

GeneHawk is a database of associations between genes and disorders found by data mining PubMed.


Genotator

Genotator is a meta-query engine designed to provide high quality gene-disease associations based upon data from 11 highly reliable resources.


COSMOS

COSMOS is a library to manage large-scale analysis workflows focusing on next-generation sequencing genomic data.


Roundup

Roundup is a large-scale orthology database. The orthologs are computed using the Reciprocal Smallest Distance (RSD) algorithm.


Our Research Focus

Translating the thinking of systems biology to the field of autism genetics with the intent to develop effective early-stage diagnostics and targets for therapeutic intervention. The work involves the generation and analysis of genomic and phenotypic databases using computational tools of systems biology, machine learning and network inference.


Clinical Research

Efforts to understand and characterize the clinical significance and utility of human genetic variation. This work involves clinical-grade annotation of human genetic variation, estimating the rates of both true and false positives in present day genetic testing and their likely impacts on the practice of personalized care, the construction of an authoritative knowledgebase for clinical decision support, and efforts in educating present and future doctors on the potentials of genomics in individualized healthcare.


Computational Disease Analyses

Redefining human diseases through computational and comparative network analysis. The work involves the integration and analysis of transcriptomic, genomic and bibliomic data to network all known human diseases. Deliverables include revealing disease connections, properly reshaping blurred boundaries of classification, and opportunities for drug treatment repositioning.


Current Projects

Our lab is interested in the design and application of translational bioinformatics tools to help bring whole-genomic data to the point of clinical care. Our research projects are designed to complement and potentially supplant standard molecular diagnostics currently being used to characterize a patient’s autoimmune deficiencies and/or complex behavioral disorders, with the intent to have a faster and more robust system in place for real-time genomic diagnostics. (Find out more)