Immune-Related Mechanistic Underpinnings of Neurodegeneration
Our group investigates the immune-related mechanisms underlying neurodegenerative diseases like Parkinson’s and ALS through multi-omics data integration and collaborates globally to validate bioinformatics findings with experimental evidence.
Data Science in Biomedical Research
We apply data science techniques to public and disease-specific databases for discovering disease-related molecules and regulators, using customized workflows to handle high-dimensional omics data from various platforms.
Advancing Life Sciences With Systems Biology and Multi-Omics Approaches
We leverage Systems Biology techniques and published data sets to extract new insights from large omics datasets and focus on multi-omics integration as a central approach in our research.
Welcome to Systems Bioinformatics Lab
Immune-Related Mechanistic Underpinnings of Neurodegeneration
At the SBL, our focus is on genomic data science of neurodegenerative medicine. More specifically, using multi-omics data wrangling, analysis, and integration, we are trying to understand mechanistic underpinnings of neurodegenerative disorders in human, such as Parkinson’s disease and ALS with a pronounced immune component. Corroborating our bioinformatics-based findings with experimental evidence thanks to our national and international collaborators, we hope to shed more and more light on the immune component of neurodegeneration and how it shapes the response of neurons to ongoing neurotoxicity in disease
Data Science in Biomedical Research
With the exponential increase in data volumes due to decreasing costs, integrative and technically more accurate analysis of omics data in the context of data science has become increasingly important in all areas of the biomedical sciences more than ever. From a technical standpoint, we leverage data sets deposited in public repositories as well as disease-specific databases to discover novel candidate molecules and master regulators associated with disease initiation and progression using statistical learning, data mining, and data science applications. Considering intrinsic characteristics of raw data from various high dimensional sequencing and array-based platforms used in epigenome, transcriptome, and mostly proteome research, we generate customized analysis workflows for individual data sets to extract meaningful biological information from skewed and likely confounded input data.
Advancing Life Sciences With Systems Biology and Multi-Omics Approaches
Given the experimental designs that yield omics data sets with large sample size, various Systems Biology techniques might as well be used to bring new perspectives to old data. And in this spirit, we take full advantage of published data sets to bring out the true potential these methods hold for our ongoing research. Additionally, such approaches have a great use for multi-omics data integration independent of the type of the input data. Considering the practical feasibility of currently available multimodal omics data for various integrative analyses, we will continue to move along this path as the mainstream of our research.