Bioinformatics

About

My research focuses on designing algorithms and development of models and software to extract information from variety of molecular biology data including genomic, transcriptomic, proteomic and phospho-proteomic (i.e. omics data). The main challenges in analyzing these type of data include (i) incompleteness and noisy nature of high-throughput data, (ii) large scale and high dimensionality of the data, and (iii) complexity and dynamic nature of biological systems. I have designed and developed multiple advanced algorithms and computational techniques to address these challenges. These algorithms are developed to integrate omics data in order to identify biomarkers and potential drug targets, and understand the underlying mechanism of complex diseases such as type 2 diabetes, hypertension, psoriasis, and different types of cancer.

Ongoing Projects

Interplay Between Post Translational Modification

• In this project, we are trying to find the relationship between different post translational modification with the focus on phosphorylation and O-glNAcation.

Transfer Learning FOr Alzheimer's Disease

• In this project, we are trying to develop a method to transfer the knowledge between tissues in Alzheirmer's patients.

Phenotyping using Phosphoproteomics

• Protein phosphorylation is a ubiquitous mechanism of post-translational modification that plays a central role in cellular signaling. Phosphorylation is particularly important in the context of cancer, as down-regulation of tumor suppressors and up-regulation of oncogenes (often kinases themselves) by dysregulation of the associated kinase and phosphatase networks are shown to have key roles in tumor growthandprogression.Despiterecentadvancesthatenablelarge-scalemonitoringofproteinphosphorylation, these data are not fully incorporated into such computational tasks as phenotyping and subtyping of cancers. In this project, we develop algorithms to predict cancer subtypes.

Prediction of Kinase-Subsrate Interaction

• Kinases play an important role in cellular regulation and have emerged as an important class of drug targets for many diseases, particularly cancers. Comprehensive identification of the links between kinases and their substrates enhances our ability to understand the underlying mechanism of diseases and signalling networks to drive drug discovery. In this project, we leverage phosphoproteomics data to improve the prediction of kinase-substrate interactions.