{"id":45981,"date":"2022-10-17T10:46:26","date_gmt":"2022-10-17T14:46:26","guid":{"rendered":"https:\/\/blogarchive.utc.edu\/news\/?p=45981"},"modified":"2024-01-19T18:48:30","modified_gmt":"2024-01-19T23:48:30","slug":"utc-scientist-a-round-1-winner-in-global-research-challenge","status":"publish","type":"post","link":"https:\/\/blogarchive.utc.edu\/news\/2022\/10\/utc-scientist-a-round-1-winner-in-global-research-challenge\/","title":{"rendered":"UTC scientist a round 1 winner in global research challenge"},"content":{"rendered":"

A computational biologist at the University of Tennessee at Chattanooga has just cleared the first hurdle in a global race of experts looking for solutions to challenges to come as a result of older people outnumbering the young by 2030.<\/p>\n

The race is the U.S. National Academy of Medicine\u2019s (NAM) Healthy Longevity Global Grand Challenge, and a research proposal by Hong Qin, a UTC professor of computer science, has been selected to advance to the second of three rounds of review. Qin\u00a0proposed the use of artificial intelligence in searching for a common mechanism in the so-called \u201cbiological clock\u201d responsible for how humans age.<\/p>\n

\"Hong

UTC Professor of Computer Science Hong Qin<\/p><\/div>\n

Titled, \u201cUncovering molecular mechanisms of aging clocks with interpretable deep learning,\u201d Qin\u2019s proposal earned him a $50,000 cash award for its selection to advance from the initial, \u201ccatalyst\u201d phase, to round 2, the \u201caccelerator\u201d phase of the competition. He will now begin testing his proposal toward documenting results in preparation for his round 2 entry in 2023. It\u2019s the latest work in what Qin said is almost 18 years of studying aging.<\/p>\n

\u201cThis is an ambitious project. I\u2019ve been studying aging since 2004,\u201d he said. \u201cThe second-round review for the NAM competition requires a separate submission and, hopefully, I will have some good preliminary results that show this idea is not just cool, but it actually works.\u201d<\/p>\n

That idea is to use create \u201cartificial neural networks\u201d for \u201cdeep learning\u201d designed to mimic the human brain\u2019s biological neurons involved in processing information and coming to conclusions or making decisions.<\/p>\n

Qin will use neural networks to analyze massive amounts of existing data on biological markers for aging in humans and in mice and several other animal species toward the possibility of detecting a common aging mechanism.<\/p>\n

\u201cI\u2019m trying to understand what this biological aging clock is and how it works in humans, a mouse, a fruit fly, even a single-celled organism like a fungus,\u201d Qin said. \u201cThis is because, in humans, there is a set of biological markers. In a mouse, there is another set. Although different species have different biomarkers of the aging clock, they most likely reflect a common mechanism which is a black box at the moment. We know these different sets of biomarkers in different species on the surface, and most biologists think there are common mechanisms\u2014or at least overlapping mechanisms\u2014in these aging clocks, even though we do know about them.<\/p>\n

\u201cThe good thing about deep learning is we don\u2019t have to know what the mechanism is. We can let the artificial intelligence learn\u2014from all the data\u2014what the best model or mechanisms are to explain the data.\u201d<\/p>\n

The NAM success also is opening doors. At a recent conference, Qin met the director of a National Institutes of Health (NIH) research program who invited him to submit a proposal to a relevant program. \u201cIt has put me on the radar of other research entities,\u201d he said.<\/p>\n

Qin credits the UTC SimCenter and its early support and funding for his \u201cbasic idea of this line of research\u201d in 2017 with positioning him to succeed in advancing it. Qin also said that SimCenter\u2019s large data storage facility enabled him and his students to analyze the GTEx (genotype-tissue expression) data set for the NAM award challenge. The GTEx data set is made available by NIH through a data usage agreement.<\/p>\n

\u201cThat data set is so large, I had to use two external hard drives. SimCenter\u2019s large-scale storage made it feasible for us to do some analysis on this extremely large data set,\u201d Qin said. \u201cWithout SimCenter\u2019s large-scale data storage, we would not even be able to have handled the data set.\u201d<\/p>\n

The SimCenter is a multidisciplinary research hub and the University\u2019s core high-performance computing and storage facility. It is one of 10 such university facilities funded by the Tennessee Higher Education Commission to advance multidisciplinary research in applied computational science and engineering toward growth in research funding, excellence in integrated research and education; and increased stature and economic competitiveness for Tennessee.<\/p>\n

SimCenter Director and Computer Science Professor Tony Skjellum said Qin\u2019s work with the facility is an example of how the SimCenter fulfills its purpose.<\/p>\n

\u201cThat\u2019s part of the mission we have at UTC. We fund scientists like Hong (Qin)\u2014we have peer-reviewed funding, and we have internal startup funding for projects with A.I. and simulation in them,\u201d Skjellum said. \u201cWe award peer-reviewed internal funding, and he\u2019s taken that and leveraged it into lots of successes in many different areas of funding and research. We sort of gave him an internal start and he\u2019s taken that to make this latest success.\u201d<\/p>\n

Every year, the SimCenter invites research proposals, funding as many as eight or nine with up to $100,000 each for one year of work. Skjellum said the most recent funding awards went to five large-scale proposals and two smaller ones.<\/p>\n

Since its launch in October 2019, the NAM\u00a0Healthy Longevity Global Competition<\/strong><\/a>\u00a0has brought together 11 global collaborators representing more than 50 countries and territories. More on Qin\u2019s catalyst-round win is here<\/strong><\/a>.<\/p>\n","protected":false},"excerpt":{"rendered":"

Hong Qin, a UTC professor of computer science, has been selected to advance to the second of three rounds of review in the U.S. National Academy of Medicine\u2019s Healthy Longevity Global Grand Challenge.<\/p>\n

Continue Reading <\/a><\/span><\/p>\n","protected":false},"author":1300,"featured_media":45987,"comment_status":"open","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"_genesis_hide_title":false,"_genesis_hide_breadcrumbs":false,"_genesis_hide_singular_image":false,"_genesis_hide_footer_widgets":false,"_genesis_custom_body_class":"","_genesis_custom_post_class":"","_genesis_layout":"","_ef_editorial_meta_date_first-draft-date":"","_ef_editorial_meta_paragraph_assignment":"","_ef_editorial_meta_checkbox_needs-photo":"","_ef_editorial_meta_number_word-count":"","_ef_editorial_meta_checkbox_slider":"","_ef_editorial_meta_checkbox_featurette":"","_jetpack_memberships_contains_paid_content":false,"footnotes":""},"categories":[64,123753,59,12739,12741,7712,8244,48007],"tags":[59632,122153,122154],"class_list":{"0":"post-45981","1":"post","2":"type-post","3":"status-publish","4":"format-standard","5":"has-post-thumbnail","7":"category-artificial-intelligence","8":"category-college-of-engineering-and-computer-science","9":"category-computational-engineering","10":"category-computer-science","11":"category-faculty-and-staff","12":"category-honors-and-awards","13":"category-news","14":"category-research-academics","15":"tag-hong-qin","16":"tag-tony-skjellum","17":"tag-u-s-national-academy-of-medicines-nam-healthy-longevity-global-grand-challenge","18":"entry"},"yoast_head":"\nUTC scientist a round 1 winner in global research challenge | UTC News Archive: Jul 2007 - 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