I am a computational biologist that completed my PhD in systems biology with focus on dynamic metabolic modelling, which integrated proteomic data and metabolic flux data. Following that, I worked on metagenomics research applying various computational and mathematical approaches (e.g. machine learning and Bayesian modelling) to study the human lung microbiome associated with asthma. Currently I am in the Centre for Genetics and Genomics studying rheumatoid arthritis treatment with the aim of personalising treatment. This involves analysing genetics, transcriptomics and metabolomics data, both separately and in an integrated manner. Furthermore, I am also working on multi-ancestry genetics research, developing methods to ensure all ancestries (including admixed) are represented in genome wide association studies. 

Multi-ancestry Genetics: Aim to develop method that would represent all ancestry (including admixed) in analyses. As current methods tend to involve removal of samples that does not easily integrate with known major ancestry information. 

Multi-omics Analyses: Aim to identify biomarkers and understand mechanisms of diseases & treament (personalised medicine). Experience currently limited to genetics, transcriptomics, metabolomics and metagenomics. Looking to expand into epigenomics and proteomics.

HLA Genetics: Exploring the HLA region's impact on autoimmune diseases further. This include developing new methods to better model the HLA region.

I have broad experience in molecular biology analyses using statistical and machine learning methods. Further, I have experience processing  genotype, RNA-seq, metabolomics, metagenomics (DNA-seq), and proteomics dataset.

Below are some of the methods I am familiar with and their use cases.

• Genetic algorithm (parameter estimation)
• ODE models (metabolic models)
• Random forest (differential abundance [metagenomics])
• Bayesian hierarchical models  (drug comparative effectivness, differential abundance[metagenomics])
• Linear (mixed) models (differential abundance [metabolomics], differential expression [transcriptomics], time decay analyses [metagenomics], SNP/HLA association [genetics], eQTL [genetics+transcriptomics])
• Network analyses (species-species interaction [metagenomics], protein-protein interaction [proteomics])
• Survival analyses (drug immunogenicity)
• Bayesian network (drug-drug interaction [multimorbidity study])
• Partial least-square regression (metabolomics)