Research Interests

Dr. Acharjee applies novel approaches to the diverse multi omics data e.g. genetics, transcriptomics, proteomics, metabolomics, single cell transcriptomics to integrate them and identify novel therapeutic mechanisms and/or disease mechanisms. The data sets used in those studies are often public (ex: TCGA, GEO etc) or stakeholders’ experimental data. To perform an integration, Dr. Acharjee often uses machine learning/AI methods derived from multiple experiments across many diseases. Some of the examples of integration are here: microbiome and inflammatory markers in infant cohort (Wood and Acharjee et al., Allergy, 2021); microbiome, metabolome and single cell sequence data in the colon cancer cohort (Bisht et al., Int J Mol Sci. 2021; Quraishi and Acharjee et al., J Crohns Colitis, 2020) and multiple metabolomics data sets integration (Acharjee et al., BMC Bioinformatics, 2016).

2. Diagnostics
Unlike previous portfolio, this aspect considers single omics or clinical data including variety of machine learning methods. Some examples include identification of the markers from cytokine profiling data (Bravo-Merodio and Acharjee et al., Sci Data. 2019), diagnostic marker from miRNA (Di Pietro et al, Br J Sports Med. 2021); metabolomics biomarker identification (Ament et al., Transl Stroke Res., 2021; Acharjee et al., Metabolomics, 2018).

3. Data analytics methods and workflow development
Dr. Acharjee is also interested to develop new bioinformatics tools /workflows that can be useful for the clinician or biologist. Some of the examples are: Microbiome analysis workflow (Bisht and Acharjee et al., Comput Biol Med, 2021), statistical power calculations online tool (Acharjee et al., BMC Medical Genomics, 2020), automatic feature selection form high dimensional omics data sets (Bravo-Merodio et al., J Transl Med. 2019).