AI/ML/DL for Bioinformatics/Computational Biology
Application of machine/deep learning to single cell RNA sequence (scRNAseq), Assay for Transposase-Accessible Chromatin using sequence (ATAC-seq), etc. Analysis of high throughput sequencing genomics data (ChIP-Seq, DNase-Seq, and/or ATAC-Seq). Deep learning, explainable AI for computational biology problems - quality control (QC), clustering, denoising, imputation, trajectory inference, immunogenicity prediction, RNA binding protein prediction, transcription factor binding prediction, Gene regulatory network (GRN) inference, antimicrobial resistance prediction, CyTOF, flow and mass cytometry. Analysis of multiplexed imaging, mass spectrometry imaging (MSI), CODEX imaging, spatial transcriptomics in tandem with single-cell genomics data and in general multi-omics, and multimodal analysis. Exploration of latent structures in single cell data, finding common embeddings/integration for mutimodal biological data. Automated cell population discovery, exploration of latent structures in single cell data, finding common embeddings/integration for mutimodal biological data.
Imaging-based: Clustering, Spatial Transcriptomics
Next-generation sequencing (NGS) is becoming commonplace and within a few years it will be routinely used like standard medical imaging modalities (CT, MRI,...). Hence personal genomes will be increasingly utilized for precision medicine. It is therefore very important to develop new approaches using latest data science tools for solving problems in bioinformatics. Prasath Lab is interested in leveraging AI/ML/DL to solve challenges in large-scale data processing in the computational biology domain. Given the expertise and experience with the multidisciplinary projects and the proven track-record in bringing quantitative approaches from mathematics, computer science, and statistics we are well-poised to be a connector among different domains. We are also part of multiple consortia (e.g. LungMAP, RDCRN,...) and our research interests span the full spectrum of bioinformatics: