… in the news …
Diagnose … Mental Illness, (New Trail) 2018.05
AI to combat invasive species, (The Star) 2018.05.09
AI revolutionizing medical science, (Folio) 2018.04.18
AI reshaping our lives, (Folio) 2018.04.16
Interview wrt AI (MacLean’s) 2017.11
Diagnosing Schizophrenia 2017.07
AI … medicine 2016.05
Computational Psychiatry 2015.10
fMRI (Collaboration with IBM) 2015.09, 06
NMR Spectroscopy 2015.05
MITAC Globalink students 2014.07
(for pre-2008: see here)
Featured in article, in New Trail (UofA Alumni Magazine), p29 -- Spring 2018 (Wendy Glauser)
Our team, from the University of Alberta, participated in the
where we tied for 1st place on the one of the two specific challenges.
(in Nature Schizophrenia) describing a way diagnose schizophrenia.
See also Metrics
[Research sponsored by CIHR, NSERC, IBM CAS Alberta, AMII]
Interviewed for article in Toronto Star: here (9 May 2016)
Interviewed for article at UofA FoS page: here (1 Apr 2016)
Global's Su-Ling Goh talks to Professors Russ Greiner and Matt Brown about their research into using computers to analyze MRI brain scans to diagnose and treat mental illness (in collaboration with IBM).
Global News Clip (Video [ 1:30 ] - 2015.10.14)
Many diseases cause significant changes to the concentrations of small molecules (a.k.a. metabolites) that appear in a person’s biofluids, which means such diseases can often be readily detected from a person’s “metabolic profile"—i.e., the list of concentrations of those metabolites. This information can be extracted from a biofluid’s Nuclear Magnetic Resonance (NMR) spectrum. We present a system, BAYESIL, that can quickly, accurately, and autonomously produce a person’s metabolic profile.
International undergrads can join U of Alberta research groups for the summer, with funding provided by the Mitacs Globalink program. My lab had the honor of hosting two Mitacs Globalink Summer Students (July 2014).
This was covered by
The best treatment for a woman with breast cancer depends on whether her tumor is Estrogen Receptor positive or not (ER+ vs ER-). A team of researchers, from both Alberta Health Services and AICML, used a machine learning approach to produce a tool that can effectively predict this ER status of a patient, based on the expression values of just 3 genes. They show that this predictive tool is very robust, as it also works extremely well in multiple other studies, across different platforms.
The article itself is