How Can AI Personalized Gut Microbiome Analysis Help Colorectal Cancer Classification?
Against this backdrop, a research team from Tokyo Institute of Technology (Tokyo Tech), Japan, decided to adopt a different approach capable of addressing this limitation. As outlined in their paper, which was recently published in Genome Biology, the team employed an explainable AI framework that provides local, rather than global, explanations for its CRC predictions. “Local explanation techniques make it possible to discover the most contributing bacteria for each individual CRC patient, enabling us to examine inter-individual differences between subjects within a disease group,” explains Associate Professor Takuji Yamada, the main author of the study.
The team used a framework called “Shapley additive explanations” (SHAP), which originated from a concept in game theory called the Shapley value. Put simply, the Shapley value tells us how a payout should be distributed among the players of a coalition or group. Similarly, in their study, the team used SHAP to calculate the contribution of each bacterial species to each individual CRC prediction.
Using this approach along with data from five CRC datasets, the researchers discovered that projecting the SHAP values into a two-dimensional (2D) space allowed them to see a clear separation between healthy and CRC subjects. Clustering this 2D information resulted in four distinct subgroups of CRC subjects, each differing in the CRC probability and the associated bacteria. Most remarkably, the results were consistent across the five datasets, showcasing the wide applicability of this method.
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With these promising results, the team anticipates their approach to make solid contributions in the gut microbiome research community. “Considering the increasing use of machine learning in microbiome-disease association studies, our novel method could be beneficial for a more personalized microbiome data exploration as well as help uncover potential disease subgroups along with their potential associated biomarkers,” speculates Dr. Yamada. Further, the technique is also applicable to other diseases with known links to the gut microbiome, such as ulcerative colitis, Crohns Disease and diabetes.
Hopefully, explainable AI will reveal more such secrets of the gut microbiome in the near future, so stay tuned!
Source: Eurekalert
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