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.