Sendurai Mani: Machine Learning in Gut Microbiome Load
Sendurai Mani, Dean of Translational Oncology at Brown University Warren Alpert Medical School, posted on LinkedIn about recent paper by Suguru Nishijima et al., titled “Fecal microbial load is a major determinant of gut microbiome variation and a confounder for disease associations” published on Cell.
Authors: Suguru Nishijima, Evelina Stankevic, Oliver Aasmets, Thomas S.B. Schmidt, Naoyoshi Nagata, Marisa Isabell Keller, Pamela Ferretti, Helene Bæk Juel, Anthony Fullam, Shahriyar Mahdi Robbani, Christian Schudoma, Johanne Kragh Hansen, Louise Aas Holm, Mads Israelsen, Robert Schierwagen, Nikolaj Torp, Anja Telzerow, Rajna Hercog, Stefanie Kandels, Diënty H.M. Hazenbrink, Maja Thiele.
“Machine Learning in Gut Microbiome Load.
The microbes in our gut differ not just in composition but also in abundance. While traditional sequencing methods reveal which microbes are present and in what proportions, they don’t show the actual number of microbes. A recent study by Nishijima et al. introduced a machine-learning model to predict fecal microbial loads (the number of microbial cells per gram of feces) using relative abundance data. Microbial load has been linked to key host factors like age, diet, and medication. By applying their model to one of the largest metagenomic datasets to date, comprising over 34,500 samples, these authors found that microbial load is the primary driver of gut microbiome variation. This study highlights the importance of microbial load in understanding the gut microbiome’s role in health and disease and paves the way for personalized medicine and microbiome.”
Sendurai A. Mani is an Indian-American oncologist and molecular biologist. Currently holding the Dean’s Chair for Translational Oncology at Brown University Warren Alpert Medical School, Dr. Mani also serves as the Associate Director for Translational Oncology at the Legorreta Cancer Center at Alpert Medical School.
Dr. Mani’s research focuses on finding the mechanisms driving cancer metastasis and therapy resistance, particularly in breast cancer. He is renowned for his discovery of cancer stem cells and their role in tumor progression and treatment resistance.
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