In-seok Lee
Yonsei University · Engineering
About the Lab
Professor In-seok Lee's research lab specializes in computational systems biology and functional genomics, focusing on constructing and applying gene co-functional networks to decipher complex biological processes in humans, animals, and microbes. The lab develops advanced bioinformatics methods—such as network-based gene set enrichment analysis (NGSEA) and genome-scale functional networks like XooNet—to uncover gene interactions underlying disease mechanisms, virulence in pathogens, and phenotypic traits. By integrating multi-omics data and leveraging genomic context from both assembled genomes and metagenomic contigs, the lab advances systems-level understanding of gene function and regulatory circuits. Their work bridges computational modeling with biological discovery, particularly in human disease research and plant-pathogen interactions.
Research Overview
Research Output Trend
Figures are computed from collected data and may differ slightly.
Selected Papers
9Gene set enrichment analysis (GSEA) is a popular tool to identify underlying biological processes in clinical samples using their gene expression phenotypes. GSEA measures the enrichment of annotated gene sets that represent biological processes for differentially expressed genes (DEGs) in clinical samples. GSEA may be suboptimal for functional gene sets; however, because DEGs from the expression dataset may not be functional genes per se but dysregulated genes perturbed by bona fide functional
Advanced high-throughput sequencing technology accumulated massive amount of genomics and transcriptomics data in the public databases. Due to the high technical accessibility, DNA and RNA sequencing have huge potential for the study of gene functions in most species including animals and crops. A proven analytic platform to convert sequencing data to gene functional information is co-functional network. Because all genes exert their functions through interactions with others, network analysis i
Various genome-wide approaches to identifying the genetic components that underlie phenotypes in animals and humans have been developed during the last several decades. The relationship between gene and phenotype, however,cannot be represented by a simple one-to-one correspondence. Rather, many genes are typically related to a single phenotype and many phenotypes can be associated with a single gene, a major theme within the study of complex phenotypes. Therefore, to dissect the genetics of comp
Multiple genes and their interactions are involved in most human diseases. This pathway-centric view of human pathology is beginning to guide our approaches to disease research. Analytical algorithms describing human gene networks have been developed for three major tasks in disease research: (i) disease gene prioritization, (ii) disease module discovery, and (iii) stratification of complex diseases. To understand the underlying biology of human diseases, identification of disease genes and dise
Bacterial species in the genus Xanthomonas infect virtually all crop plants. Although many genes involved in Xanthomonas virulence have been identified through molecular and cellular studies, the elucidation of virulence-associated regulatory circuits is still far from complete. Functional gene networks have proven useful in generating hypotheses for genetic factors of biological processes in various species. Here, we present a genome-scale co-functional network of Xanthomonas oryze pv. oryzae (
The gene neighborhood in prokaryotic genomes has been effectively utilized in inferring cofunctional networks in various organisms. Previously, such genomic context information has been sought among completely assembled prokaryotic genomes. Here, we present a method to infer functional gene networks according to the gene neighborhood in metagenome contigs, which are incompletely assembled genomic fragments. Given that the amount of metagenome sequence data has now surpassed that of completely as
Although approved programmed cell death protein (PD)-1 inhibitors show durable responses, clinical benefits to these agents are only seen in one-third of patients in most cancer types. Therefore, strategies for improving the response to PD-1 inhibitor for treating various cancers including non-small cell lung cancer (NSCLC) are urgently needed. Compared with genome and transcriptome, tumor DNA methylome in anti-PD-1 response was relatively unexplored. We compared the pre-treatment methylation st
Gene co-expression network inference from bulk tissue samples often misses cell-type-specific interactions, which can be detected through single-cell gene expression data. However, the noise and sparsity of single-cell data challenge the inference of these networks. We developed scNET, a framework for integrative cell-type-specific co-expression network inference from single-cell transcriptome data, demonstrating its utility in augmenting the human interactome for more accurate disease gene pred
In the fabrication process of optical elements (OEs) by the laser scanning method using a dichromated gelatin (DCG) photoplate, the expansion and drying stress of gelatine caused by inhomogeneous liquid flow inside the gelatine affects the shape of OEs. The reason this inhomogeneous liquid flow exists in the energy oversaturated parts of OEs is the presence of surplus energy. In order to obtain the OEs of desired spherical lens shape, the drying stress should be reduced and therefore the maximum
Research Areas
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