Biology is not random, just largely unknown. There are almost an infinite amount of possible interactions, but only a sparse handful constitutes a complex living system. To narrow the search space, massive amounts of biological data are being generated to capture snapshots or snippets of such living systems. In this effort, bioinformatics algorithms play a key role in decoding these large datasets and enable the reconstruction of underlying biological principles both at the molecular and system level.
The Young Laboratory at KAIST draws upon ideas from data science, applied statistics, and machine learning to tackle fundamental questions in quantitative biology and biomedical engineering. Our algorithms are optimized according to a specific data generation process which means we often ask for copies of experimental protocols and lab notes from our collaborators. In particular, we are interested in (1) decoding the human genome by developing probabilistic models at single-nucleotide resolution and (2) interpreting those molecular insights within the context of the large biological networks. See below for ongoing projects.
1.Probabilistic modeling at single-nucleotide resolution
a.Genetic variants of the non-coding genome
c.Rational design of biomolecules
2.Reconstructing the gene regulatory network
a.Posttranscriptional gene regulation