Metagenomics

Metagenome-wide analysis studies provide a unique set of microbial features for biomarker discovery of associated disease as well as for studying diversity and dynamics of microbial communities under different conditions. In general, metagenome-wide analysis studies focus on one major approach (i.e., statistical, predictive, or comparative network analysis) and are limited to generating taxonomy profiles using one tool.

Main research directions:

  1. Developing integrative machine learning and comparative network approaches for metagenome-wide analysis studies of the human microbiota and associated diseases (e.g., diabetes, obesity, and cardiovascular disease).
  2. Developing dynamic network analysis algorithms and tools for studying the temporal variation of microbial communities in a variety of environments.
  3. Developing integrated models and analyses for the integration of multi-view (multi-modal) longitudinal data such as the data collected using ongoing efforts in the Integrative Human Microbiome Project (iHMP) which are creating integrated data sets of microbiome and host functional properties (e.g., omics data).
  4. Developing more sophisticated methodologies for across-studies and/or across-disease meta-analysis of rapidly increasing amounts of publically available metagenomics data.

Selected publications:

Abbas M, Le T, Bensmail H, Honavar V, El-Manzalawy Y (2018) Microbiomarkers Discovery in Inflammatory Bowel Diseases using Network-Based Feature Selection. Proceedings of the 9th ACM Conference on Bioinformatics, Computational Biology and Health Informatics: ACM. pp. 172-177.

Abbas M, El-Manzalawy Y (2017) Predictive and Comparative Network Analysis of the Gut Microbiota in Type 2 Diabetes. Proceedings of the 8th ACM Conference on Bioinformatics, Computational Biology and Health Informatics: ACM. pp. 313-320.