Multi-Omics and Multi-Modal Integration 

Dr. Acharjee and his team applies novel approaches to the diverse multi omics data e.g. clinical, genetics, transcriptomics, proteomics, metabolomics, single cell transcriptomics to integrate them and identify novel therapeutic mechanisms and disease mechanisms. The data sets used in those studies are often public (ex: TCGA, GEO etc) or stakeholders’ experimental data. To perform an integration, We  use machine learning/AI methods derived from multiple experiments across cancer and metabolic diseases.  Some of the examples of integration are microbiome and inflammatory markers in infant cohort, microbiome, metabolome and single cell sequence data in the colon cancer cohort , and multiple metabolomics data sets integration.

Novel Tool Development 

Group is also interested to develop new bioinformatics tools and workflows that can be useful for the clinician or biologist. Some of the examples are microbiome analysis workflow, statistical power calculations online tool, automatic feature selection form high dimensional omics data sets .

Diagnostics 

This aspect considers single omics or clinical data including variety of machine learning and AI methods to discover diagnostics markers. Some examples include identification of the markers from cytokine profiling data , diagnostic marker from miRNA, metabolomics biomarker identification .