Microbiome Analytics

Objectives

1

By analyzing the intestinal flora microorganisms, enzymes, and metabolic pathways using a holistic approach using machine learning.

2

We are developing a reliable classification model to aid in the diagnosis of colon cancer.

3

Identifying disease-related biomarkers and providing treatment recommendations.

4

Development of dietary strategies that can alter the gut microbiome.

Problem

Methodology

Phase 1

Metagenomic data is obtained from sample .

Phase 2

G-S-M Machine Learning Model has been developed.

Phase 3

Enzyme, pathway and microbiota analysis have been performed.

Phase 4

Mircobita- enzyme, microbiota-pathway, enzyme-pathway association have been shown.

Phase 5

Identifying important enzyme, pathway and microbiota.

Workflow

Features of Our Solution

Publications

Integrating Biological Domain Knowledge with Machine Learning for Identifying Colorectal-Cancer-Associated Microbial Enzymes in Metagenomic Data

B. Bakir-Gungor, N. Ş. Ersöz, M. Yousef, 2025, “Integrating Biological Domain Knowledge with Machine Learning for Identifying Colorectal-Cancer-Associated Microbial Enzymes in Metagenomic Data”, Applied Sciences, 15, 2940.

Prediction of colorectal cancer based on taxonomic levels of microorganisms and discovery of taxonomic biomarkers using the Grouping-Scoring-Modeling approach

B. Bakir-Gungor, M. Temiz, B. Canakcimaksutoglu, M. Yousef, 2025, “Prediction of colorectal cancer based on taxonomic levels of microorganisms and discovery of taxonomic biomarkers using the Grouping-Scoring-Modeling approach”, Computers in Biology and Medicine, 187, 109813.

CCPred: Global and population-specific colorectal cancer prediction and metagenomic biomarker identification at different molecular levels using machine learning techniques

B. Bakir-Gungor, M. Temiz, E. Cicekyurt, Y. Inal, M. Yousef, 2024, “CCPred: Global and population-specific colorectal cancer prediction and metagenomic biomarker identification at different molecular levels using machine learning techniques”, Computers in Biology and Medicine, 182, 109098.

Novel Antimicrobial Peptide Design Using Matching Score Motif Representation

U. G. Soylemez, M. Yousef, Z. Kesmen, B. Bakir-Gungor, 2024, “Novel Antimicrobial Peptide Design Using Matching Score Motif Representation”, IEEE/ACM Transactions on Computational Biology and Bioinformatics, 21(6), 1656-1666.

microBiomeGSM: The identification of taxonomic biomarkers from metagenomic data using Grouping, Scoring and Modeling (G-S-M) approach

B. Bakir-Gungor, M. Temiz, A. Jabeer, D. Wu, M. Yousef, 2023, “microBiomeGSM: The identification of taxonomic biomarkers from metagenomic data using Grouping, Scoring and Modeling (G-S-M) approach.”, Frontiers in Microbiology, 14:1264941.

AMP-GSM: Prediction of Antimicrobial Peptides via Grouping-Scoring-Modeling Approach

Ü.G. Söylemez, M. Yousef, B. Bakir-Gungor, 2023, “AMP-GSM: Prediction of Antimicrobial Peptides via Grouping-Scoring-Modeling Approach”, Applied Sciences, 13(8):5106.

Inflammatory Bowel Disease Biomarkers of Human Gut Microbiota Selected via Ensemble Feature Selection Methods

B. Bakir-Gungor, H. Hacilar, A. Jabeer, O.U. Nalbantoglu, O. Aran, M. Yousef, 2022, “Inflammatory Bowel Disease Biomarkers of Human Gut Microbiota Selected via Ensemble Feature Selection Methods”, PeerJ, 10: e13205.

Prediction of Linear Cationic Antimicrobial Peptides Active against Gram-Negative and Gram-Positive Bacteria Based on Machine Learning Models

Ü.G. Söylemez, M. Yousef, Z. Kesmen, M.E. Büyükkiraz, B. Bakir-Gungor, 2022, “Prediction of Linear Cationic Antimicrobial Peptides Active against Gram-Negative and Gram-Positive Bacteria Based on Machine Learning Models”, Applied Sciences, 12, 3631.

Statistical and machine learning techniques in human microbiome studies: contemporary challenges and solutions

Moreno-Indias, L. Lahti, M. Nedyalkova, I. Elbere, G. Roshchupkin, B Bakir-Gungor, 2021, “Statistical and machine learning techniques in human microbiome studies: contemporary challenges and solutions”, Frontiers in Microbiology, 12, 277.