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A information technology and engineering associate professor and her doctoral student graduate are using an inherited computer network inference model that eventually could anticipate whether a person will are afflicted by bipolar disorder, schizophrenia or any other mental illness.
The findings are detailed within the paper “Inference of SNP-Gene Regulatory Networks by Integrating Gene Expressions and Genetic Perturbations,” which was published in the June edition of Biomed Research International. The main investigators were Jean Gao, an associate professor laptop or computer science and engineering, and Dong-Chul Kim, who recently earned his doctorate in information technology and engineering from UT Arlington.
“We sought out the differences between our genetic computer network and also the brain patterns of 130 patients in the University of Illinois,” Gao said. “This work could lead to earlier diagnosis later on and treatment for those patients struggling with bipolar disorder or schizophrenia. Early diagnosis allows doctors to supply timely treatments that may accelerate aid to help affected patients.”
The UT Arlington researchers teamed with Jiao Wang from the Beijing Genomics Institute at Wuhan, China; and Chunyu Liu, visiting associate professor in the University of Illinois Department of Psychiatry, around the project.
Gao said the findings also can lead to more individualized drug therapies for all those patients in the early stages of mental illnesses.
“Our work will allow doctors to investigate an individual’s genetic pattern and use the appropriate amounts of personalized therapy based on patient-specific data,” Gao said.
One key to the study is designing single nucleotide polymorphism or SNP networks, researchers said.
“SNPs are regulators of genes,” said Kim, who joins the University of Texas-Pan American this fall as an assistant professor. “Those SNPs visualize how individual genes will act. It gives us more of an entire picture.”
The paper is a culmination of four years of work.
Khosrow Behbehani, dean from the College of Engineering, said the research merges the power of information technology and engineering, psychology and genetics.
“This research holds lots of promise in genetic expression,” Behbehani said. “If successful, it opens up the possibility of using the method to other pathological conditions.”
