Understanding the Role of the Microbiome in Cancer Diagnostics and Therapeutics by Creating and Utilizing ML Models
Journal
Applied Sciences
Date Issued
2022
Author(s)
Cekikj, Miodrag
Özdemir Jakimovska, Milena
Özcan, Orhan
Uğur Sezerman, Osman
Abstract
Recent studies have highlighted that gut microbiota can alter colorectal cancer susceptibility
and progression due to its impact on colorectal carcinogenesis. This work represents a comprehensive
technical approach in modeling and interpreting the drug-resistance mechanisms from clinical data
for patients diagnosed with colorectal cancer. To accomplish our aim, we developed a methodology
based on evaluating high-performance machine learning models where a Python-based random forest
classifier provides the best performance metrics, with an overall accuracy of 91.7%. Our approach
identified and interpreted the most significant genera in the cases of resistant groups. Thus far, many
studies point out the importance of present genera in the microbiome and intend to treat it separately.
The symbiotic bacterial analysis generated different sets of joint feature combinations, providing
a combined overview of the model’s predictiveness and uncovering additional data correlations
where different genera joint impacts support the therapy-resistant effect. This study points out the
different perspectives of treatment since our aggregate analysis gives precise results for the genera
that are often found together in a resistant group of patients, meaning that resistance is not due to the
presence of one pathogenic genus in the patient microbiome, but rather several bacterial genera that
live in symbiosis.
and progression due to its impact on colorectal carcinogenesis. This work represents a comprehensive
technical approach in modeling and interpreting the drug-resistance mechanisms from clinical data
for patients diagnosed with colorectal cancer. To accomplish our aim, we developed a methodology
based on evaluating high-performance machine learning models where a Python-based random forest
classifier provides the best performance metrics, with an overall accuracy of 91.7%. Our approach
identified and interpreted the most significant genera in the cases of resistant groups. Thus far, many
studies point out the importance of present genera in the microbiome and intend to treat it separately.
The symbiotic bacterial analysis generated different sets of joint feature combinations, providing
a combined overview of the model’s predictiveness and uncovering additional data correlations
where different genera joint impacts support the therapy-resistant effect. This study points out the
different perspectives of treatment since our aggregate analysis gives precise results for the genera
that are often found together in a resistant group of patients, meaning that resistance is not due to the
presence of one pathogenic genus in the patient microbiome, but rather several bacterial genera that
live in symbiosis.
Subjects
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