Machine learning method differentiates between healthy male, female microbiomes
Wednesday, June 29, 2016 - 06:31
in Mathematics & Economics
The week-long International Conference on Machine Learning (ICML) ended June 24, and the last day included the 2016 ICML Workshop on Computational Biology. UC San Diego professors Larry Smarr (computer science) and Rob Knight (pediatrics, computer science) 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."