Abstract
Machine learning (ML) modeling of the human microbiome has the potential to identify biomarkers and participate in the early diagnosis of a number of medical conditions. Prior to investigating disease microbiome biomarkers, a healthy state of biomarkers needs to be established. These can vary based on different conditions (including gender). We used the currently available technology to try to discover the difference between the microbiome of a different gender as this can help guide personalized medicine. We trained a Random Forest classifier model that used fecal 16S rRNA sequence data to differentiate between male and female subjects based on the same age, BMI, country of residence, and diet. Before the classification process, bioinformatics analysis was performed using the QIIME2–2020.2 tool which resulted in profiling microbiome communities present in host organisms. The Alpha diversity test that was conducted afterward did not discover differences in male and female subjects. Prior to classification, feature selection has been performed using the forests of trees to evaluate the importance of features in the dataset. It was experimented with 95%, 90%, and 85% coverage of features for each classification run. 5-fold cross-validation yielded high accuracy results on multiple subsets (subjects in the 30s, subjects in the 40s, non-omnivores), going above 80%. Our results have shown that Random Forest, as a stable algorithm, can be modeled on microbiome data to achieve high precision (close to 90%) with relatively few parameters. This suggests that the microbiome of different genders is highly distinguishable, and that gender is a possible suitable marker for personalized decision-making in medicine that involves gut microbiome data. The new methods of analysis of gut microbiome data can be used to confirm these results with more precise analysis.
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Data Availability
All data generated and analysed during this study are deposited to the GiHub repository (https://github.com/dzb07/microbiome).
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Basic-Cicak, D., Hasic Telalovic, J. (2023). Data Science of Microbiome: Does Gender Matter. In: Ademović, N., Mujčić, E., Mulić, M., Kevrić, J., Akšamija, Z. (eds) Advanced Technologies, Systems, and Applications VII. IAT 2022. Lecture Notes in Networks and Systems, vol 539. Springer, Cham. https://doi.org/10.1007/978-3-031-17697-5_49
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