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Teaching Computers to Recognize Unhealthy Guts

A new proof-of-concept study by researchers from the University of California San Diego has succeeded in training computers to “learn” what a healthy versus an unhealthy gut microbiome looks like based on its genetic makeup. Since this can be done by genetically sequencing fecal samples, the research suggests there is great promise for new diagnostic tools that are, unlike blood draws, non-invasive. As recent advances in scientific understanding of Parkinson’s disease and cancer immunotherapy have shown, our gut microbiomes – the trillions of bacteria, viruses and other microbes that live within us – are emerging as one of the richest untapped sources of insight into human health. The problem is these microbes live in a very dense ecology of up to one billion microbes per gram of stool. Determining the state of that ecology is a classic ‘Big Data’ problem, where the data is provided by a powerful combination of genetic sequencing techniques and supercomputing software tools. The challenge then becomes how to mine this data to obtain new insights into the causes of diseases, as well as novel therapies to treat them. The new paper, titled “Using Machine Learning to Identify Major Shifts in Human Gut Microbiome Protein Family Abundance in Disease,” was presented last month at the IEEE International Conference on Big Data. It was written by a joint research team from UC San Diego and the J. Craig Venter Institute (JCVI). At UC San Diego, it included Mehrdad Yazdani, a machine learning and data scientist at the Calit2 Qualcomm Institute; Biomedical Sciences graduate student Bryn C. Taylor and Pediatrics Postdoctoral Scholar Justine Debelius; Rob Knight, a professor in the UC San Diego School of Medicine's Pediatrics Department as well as the Computer Science and Engineering Department; and Larry Smarr, Director of Calit2 and a professor of Computer Science and Engineering. The UC San Diego team also collaborated with Weizhong Li, an associate professor at JCVI. Learn more at

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