FDSN Seminar Series: Application of Machine Learning in Microbiological Research
The Department of Food Science and Nutrition will host Renmao Tian for a seminar titled “Application of Machine Learning in Microbiological Research” on Thursday, March 23, beginning at 12:45 p.m. The virtual seminar will take place on Zoom.
Machine learning is the core of artificial intelligence, and has great potential in research and industry for classification and regression analysis. Here, we demonstrate the power of machine learning in classifying plasmid sequences from genomic data. Plasmids are extrachromosomal DNA found in microorganisms. They often carry beneficial genes that help bacteria adapt to harsh conditions, but they can also carry genes that make bacteria harmful to humans. Plasmids are also important tools in genetic engineering, gene therapy, and drug production. However, it can be difficult to identify plasmid sequences from chromosomal sequences in genomic and metagenomic data. We have developed a new tool called PlasmidHunter, which uses machine learning to predict plasmid sequences based on gene content profile. PlasmidHunter has achieved high accuracies (up to 96.7 percent) and fast speeds in benchmark tests, outperforming other existing tools.
Renmao Tian acquired his Ph.D. in environmental microbiology from Hong Kong University of Science and Technology in 2015. After graduation, he worked as a postdoctoral research associate at the University of Oklahoma from 2016 to 2019 on several projects funded by the United States Department of Energy, with a focus on microbiome in soil, water, and animal guts. He then joined the Institute for Food Safety and Health at Illinois Tech as a research scientist in 2019, focusing on food pathogens, machine learning, and bioinformatics tool development. With experience in both experimental and computational biology, Tian is well-versed in studying microbial diversity and function using genomics, metagenomics, and metatranscriptomics approaches. His extensive research centers on microbes from a variety of environments including food, animals, soil, groundwater, seawater, and more. He takes advantage of state-of-the-art technologies in microbial genomics studies, such as third generation sequencing (e.g. Nanopore) and machine learning.Join Zoom