Machine Learning Method Differentiates Between Healthy Male, Female Microbiomes

San Diego, CA, June 27, 2016 — The week-long International Conference on Machine Learning (ICML) ended June 24, and the last day included the 2016 ICML Workshop on Computational Biology.  CSE professors Larry Smarr and Rob Knight as well as Qualcomm Institute data scientist Mehrdad Yazdani were represented in a poster presentation and paper on “Using Topological Data Analysis to find discrimination between microbial states in human microbiome data.” Borrowing a statistical method originally from topology, the co-authors applied Topological Data Analysis (TDA) as an “unsupervised learning and data exploration tool to identify changes in microbial states.” Analysis of healthy male and female subjects allowed researchers to clearly separate the microbiome data (for saliva and for stool) that, using existing methods, previously made it impossible to discriminate between the genders in comparing…


Link to Full Article: Machine Learning Method Differentiates Between Healthy Male, Female Microbiomes