Dr Animesh Acharjee PhD

• PhD in Omics data analysis, Wageningen University, The Netherlands
• MSc in Bioinformatics, IBAB, Bangalore, India
• BTech in Electrical Engineering, NERIST, India

Dr. Acharjee did his undergraduate degree in Electrical Engineering from North Eastern Regional Institute of Technology (NERIST), Itanagar, India and Masters in Bioinformatics from Institute of Bioinformatics and Applied Biotechnology, Bangalore , India. After his Masters, he earned his PhD from Wageningen University, The Netherlands, on applied machine learning and data analysis. After his PhD he moved to Lyon, France, for his post-doctoral study with Synergie Lyon Cancer Centre as a Biostatistician where he extensively worked on big data analytics, cloud computing. After his post-doctoral study he was offered a scientist position with BASF Cropdesign, Belgium.

Before joining University of Birmingham and Queen Elizabeth Hospital he was working with University of Cambridge, Cambridge, UK focusing on metabolic driven diseases like Obesity, T2-diabetis using high throughput metabolomics, lipidomics technologies. His research interests includes integrative data analytics, predictive biomarker discovery, bioinformatics methods for diagnostics and network biology. Throughout his career, he was offered many fellowships from British Council, Dutch Government and Newton fellowships. He has published many papers in the international journals and actively collaborate with many Universities, for example Harvard University and University of Cambridge. So far, he has published 83 papers, and his h index is 25 based on google scholar.

• Integrative Multimodal Data Analytics
• Health Data Fundamentals
• MRes - Cancer Science
• Genome Medicine
• Clinical Bioinformatics
• Foundation Year-2 (FY-2)

1. Integrative analytics

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).