The primary aim of this research is to apply computational techniques to improve understanding and management of diseases through:
Development of computational tools for pediatric cancer drug discovery.
Application of functional enrichment analysis and visualization using R and Shiny.
Multi-omics data integration for infectious disease surveillance and genomic epidemiology.
Classification of cancer subtypes using machine learning models.
Comprehensive analysis of whole genome sequencing (WGS) data from pathogenic bacteria.
Built a custom interactive R Shiny tool for functional enrichment analysis, improving accessibility of cancer-related data visualization.
Designed reproducible bioinformatics pipelines for analyzing large-scale genomic datasets.
Applied machine learning algorithms to successfully classify cancer subtypes using methylation signatures.
Strengthened computational frameworks for integrating multi-omics data in both infectious and non-infectious disease researc
This research bridges computational modeling and biomedical research to create scalable, data-driven solutions for health science.
The integration of bioinformatics, machine learning, and multi-omics analysis enhances understanding of:
Molecular signatures driving disease heterogeneity.
Drug target prediction and therapeutic optimization.
Epidemiological tracking and genomic surveillance of pathogens.
The outcomes contribute to the global shift toward precision medicine and computational health informatics, offering reproducible workflows that can be adapted for diverse biomedical research contexts.
Programming Languages: R, Python, Bash
Bioinformatics Frameworks: TCGAbiolinks, DESeq2, edgeR, Galaxy, Bioconductor
Machine Learning: Caret, RandomForest, xgboost, sklearn
Visualization: ggplot2, R Shiny, matplotlib, seaborn
Data Types: WGS, Transcriptomics (RNA-seq), Methylation dat
