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Research uses data mining to help early identification, prevention of Alzheimer’s disease

Monday, September 28, 2015

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News Topics: computer science, engineering, health and fitness

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A University of Texas at Arlington computer scientist will analyze complex data and use imaging genomics to predict a person’s probability of contracting Alzheimer’s disease.

Heng Huang

Heng Huang, professor in the Computer Science and Engineering Department, will use data and medical information to predict whether a person is more likely to contract Alzheimer's disease.

Heng Huang, a professor in the Computer Science and Engineering Department, won a five-year, $2 million grant through the National Institutes of Health to use multi-dimensional and longitudinal imaging genomic data to identify biomarkers that may be used for early prediction of Alzheimer’s disease, with the hope that the effects of the disease may be reversed or prevented.

Huang will develop data-mining techniques to analyze genotype and phenotype data from the Alzheimer's Disease Neuroimaging Initiative or ADNI cohort, a database to study the brains of Alzheimer’s patients.

“Most people with mild cognitive impairment eventually contract Alzheimer’s, but some go back to normal. We want to predict MCI based on genomic information so that we may accurately predict whether they will get Alzheimer’s or not,” Huang said. “From a biomedical standpoint, this is an opportunity to have very good knowledge of the genome technique and neuroimaging. From the computing side, we’ll be using very advanced algorithms to understand and study the brain.”

According to Huang, most existing studies use a single genetic or neuroimaging data source. Longitudinal data changes every six months or so, as does the brain, so he will build computational models and software to incorporate biological knowledge into the existing data sets.

This will include identifying the genetic basis on which MCI turns into Alzheimer’s, multi-dimensional biomarker detection of genetic and phenotypic biomarkers, and identifying and studying longitudinal data on MCI-to-Alzheimer’s conversion. Researchers at the University of North Carolina at Chapel Hill will help with clinical studies and corroborate the results.

“Dr. Huang’s research in mild cognitive impairment and its relationship to Alzheimer’s disease has the potential to change the way we view this disease and how we treat it,” said Duane Dimos, UT Arlington’s vice president for research. “The innovations in healthcare brought about by this research can help people live longer, healthier, and happier lives. This research will help the University advance its work in the health sciences and will lead to important discoveries that support our strategic focus on health and the human condition,”

Khosrow Behbehani, dean of the College of Engineering, lauded Huang’s accomplishment and the impact Huang’s research could have on the understanding of Alzheimer’s and the brain in general.

“The support of government for understanding the brain and brain diseases has created many opportunities for research into diseases such as Alzheimer’s,” Behbehani said. “Dr. Huang’s novel approach to studying mild cognitive impairment could contribute significantly to a better understanding of the human brain.”

About The University of Texas at Arlington

The University of Texas at Arlington is a comprehensive research institution of more than 51,000 students in campus-based and online degree programs and is the second-largest institution in The University of Texas System.  The Chronicle of Higher Education ranked UT Arlington as one of the 20 fastest-growing public research universities in the nation in 2014. U.S. News & World Report ranks UT Arlington fifth in the nation for undergraduate diversity. The University is a Hispanic-Serving Institution and is ranked as a “Best for Vets” college by Military Times magazine. Visit to learn more, and find UT Arlington rankings and recognition at

-- written by Jeremy Agor


The University of Texas at Arlington is an Equal Opportunity and Affirmative Action employer.