황세진 교수

BACKGROUND: The systematic analysis of protein-protein interactions can enable a better understanding of cellular organization, processes and functions. Functional modules can be identified from the protein interaction networks derived from experimental data sets. However, these analyses are challenging because of the presence of unreliable interactions and the complex connectivity of the network. The integration of protein-protein interactions with the data from other sources can be leveraged f

The landscape of human phosphorylation networks has not been systematically explored, representing vast, unchartered territories within cellular signaling networks. Although a large number of in vivo phosphorylated residues have been identified by mass spectrometry (MS)-based approaches, assigning the upstream kinases to these residues requires biochemical analysis of kinase-substrate relationships (KSRs). Here, we developed a new strategy, called CEASAR, based on functional protein microarrays

Müller glia (MG) are the only glial cell type produced by the neuroepithelial progenitor cells that generate the vertebrate retina. MG are required to maintain retinal homeostasis and support the survival of retinal neurons. Furthermore, in certain vertebrate classes, MG function as adult stem cells, mediating retinal regeneration in response to injury. However, the mechanisms that regulate MG development are poorly understood because there is considerable overlap in gene expression between reti

We present a network framework for analyzing multi-level regulation in higher eukaryotes based on systematic integration of various high-throughput datasets. The network, namely the integrated regulatory network, consists of three major types of regulation: TF→gene, TF→miRNA and miRNA→gene. We identified the target genes and target miRNAs for a set of TFs based on the ChIP-Seq binding profiles, the predicted targets of miRNAs using annotated 3'UTR sequences and conservation information. Making u

BACKGROUND: The sparse connectivity of protein-protein interaction data sets makes identification of functional modules challenging. The purpose of this study is to critically evaluate a novel clustering technique for clustering and detecting functional modules in protein-protein interaction networks, termed STM. RESULTS: STM selects representative proteins for each cluster and iteratively refines clusters based on a combination of the signal transduced and graph topology. STM is found to be eff

Despite the pervasiveness of networks as models for real world systems ranging from the Internet, the World Wide Web to gene regulation and scientific collaborations, only a limited number of metrics capable of characterizing these systems are available. The existing metrics for characterizing networks have broad specificity and lack the selectivity for many applications. The purpose of this paper is to identify and critically evaluate a metric, termed bridging centrality, which is highly select

Several centrality measures were introduced to identify essential components and compute components’ importance in networks. Majority of these centrality measures are dominated by components’ degree due to their nature of looking at networks’ topology. We propose a novel essential component identification model, bridging centrality, based on information flow and topological locality in scale-free networks. Bridging centrality provides an entirely new way of scrutinizing network structures and me

Patients with recent pandemic coronavirus disease 19 (COVID-19) complain of neurological abnormalities in sensory functions such as smell and taste in the early stages of infection. Determining the cellular and molecular mechanism of sensory impairment is critical to understand the pathogenesis of clinical manifestations, as well as in setting therapeutic targets for sequelae and recurrence. The absence of studies utilizing proper models of human peripheral nerve hampers an understanding of COVI

The objective of the study was to identify distinct patterns in inflammatory immune responses of COVID-19 patients and to investigate their association with clinical course and outcome. Data from hospitalized COVID-19 patients were retrieved from electronic medical record. Supervised k-means clustering of serial C-reactive protein levels (CRP), absolute neutrophil counts (ANC), and absolute lymphocyte counts (ALC) was used to assign immune responses to one of three groups. Then, relationships be

With completion of a draft sequence of the human genome, the field of genetics stands on the threshold of significant theoretical and practical advances. Crucial to furthering these investigations is a comprehensive understanding of the expression, function, and regulation of the proteins encoded by an organism. It has been observed that proteins seldom act as single isolated species in the performance of their functions; rather, proteins involved in the same cellular processes often interact wi

BACKGROUND: Quantitative characterization of the topological characteristics of protein-protein interaction (PPI) networks can enable the elucidation of biological functional modules. Here, we present a novel clustering methodology for PPI networks wherein the biological and topological influence of each protein on other proteins is modeled using the probability distribution that the series of interactions necessary to link a pair of distant proteins in the network occur within a time constant (

BACKGROUND: Regulatory regions (e.g. promoters and enhancers) play an essential role in human development and disease. Many computational approaches have been developed to predict the regulatory regions using various genomic features such as sequence motifs and evolutionary conservation. However, these DNA sequence-based approaches do not reflect the tissue-specific nature of the regulatory regions. In this work, we propose to predict regulatory regions using multiple features derived from DNA m

Understanding gene regulation is crucial to dissect the molecular basis of human development and disease. Previous studies on transcription regulatory networks often focused on their static properties. Here we used retinal development as a model system to investigate the dynamics of regulatory networks that are comprised of transcription factors, microRNAs and other protein-coding genes. We found that the active sub-networks are topologically different at early and late stages of retinal develop