High throughput technologies have totally transformed clinical research and as a result, many of the multi-omics (For example transcriptomics, proteomics, metabolomics, single-cell transcriptomics, etc.) and multi-modal data sets (Example: image, literature, etc.) are available. It is necessary to use an integrative strategy that incorporates multi-omics and multi-modal data to emphasize the interrelationships of the relevant biomolecules and their functions. We have developed several potential diagnostics tools, workflows, and machine-learning approaches for data integration and interpretation in response to the development of high-throughput techniques.
We integrate them with the aim to identify novel therapeutic targets or mechanisms for a particular disease for example colon cancer. We explore many different types of data sets that include stakeholders' experimental data, hospital data generated from trials, and public (ex: TCGA, GEO, etc) data sets. Some examples of integrative analytics can be found here microbiome and inflammatory markers in the infant cohort (Wood and Acharjee et al., Allergy, 2021); microbiome, metabolome, and single-cell sequence data in the colon cancer cohort (Bosch and Acharjee et al., 2022; Quraishi and Acharjee et al., J Crohns Colitis, 2020). Our group is consisting of multiple experts of people for example biochemists, bioinformaticians, computer scientists, etc and we promote interdisciplinary research.