Research Aim: To conduct comprehensive genomic profiling and variant analysis across various cancer cell lines

Research Aim: To explore the genetic variants and pathways involved in the pathogenesis of Parkinson's disease

Research Aim: To analyze the microbial populations and antimicrobial resistance genes associated with colorectal tumors,

Research Aim: To study the gut and urinary microbiomes of prostate cancer patients, focusing on microbial diversity and the presence of virulence factors that may influence disease progression.

Research Aim: Leveraging metagenomics to provide insights into the underlying mechanisms linking the microbiome, the goal is ultimately to develop targeted interventions for improved management and prevention of the disease.

Research Aim: To employ environmental metagenomics across diverse sample types for public health research purposes and to comprehensively assess microbial communities to monitor environmental health.

Research Aim: Understanding the genetic mechanisms underlying multidrug resistance pathogens and comparing strains from clinical, food, or environmental sources.

Research Aim: To study the distribution, prevalence, and genomic characteristics of virulence factors in clinical pathogens across diverse geographical regions.

Research Aim: To implement a One Health approach integrating human, animal, and environmental data to track outbreaks and spread of pathogens.

Research Aim: To uncover the genetic diversity, evolutionary relationships, and functional capabilities of pathogens from different species causing the same diseases across regions.

Research Aim: To investigate the genetic variants in plants that contribute to stress resistance, aiming to understand the molecular mechanisms and evolutionary adaptations that enhance plant resilience under environmental stress conditions.

Research Aim: To explore the genomic foundations of probiotic strains, identifying and understanding the functional genes associated to their nutritional and industrial benefits.

Research Aim: To systematically identify and utilize biosynthetic genes from natural sources, employing multi-omics data and machine learning techniques to develop peptide-based anti-cancer therapeutics to improve precision and efficacy in cancer treatment.

Research Aim: To innovate a comprehensive methodology by leveraging multi-omics datasets and advanced machine learning algorithms to systematically identify, enhance, and develop new therapeutic antibacterial peptides derived from various natural sources.

Research Aim: To innovate a systematic approach for identifying and optimizing peptides from natural sources, utilizing multi-omics data and machine learning to develop effective anti-malarial drugs that address the challenges of malaria and drug-resistant strains.

Research Aim: To systematically explore and harness biosynthetic genes from natural sources, integrating multi-omics data and machine learning techniques to develop innovative peptide-based antiviral therapeutics, with the goal of enhancing precision and efficacy in the treatment of viral infections.

Research Aim: To systematically explore and harness biosynthetic genes from natural sources, integrating multi-omics data and machine learning techniques to develop innovative peptide-based antifungi therapeutics, with the goal of enhancing precision and efficacy in the treatment of fungi infections.

Project Aim: To build core skills in Python and genomic preprocessing, covering diverse data types like sequence, RNA-Seq, CNV, and clinical data. Key outcomes include mastering data wrangling, feature engineering, and preparing data for advanced ML pipelines in genomics.

Project Aim: This hands on program guides you from the fundamentals of Python programming to building interactive bioinformatics tools for health tech applications. You will master data analysis using Biopython, learn professional version control with GitHub, and develop dynamic web apps using Streamlit.