• Proceedings of the Korea Information Processing Society Conference
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• 2011.11a
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• pp.1115-1118
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• 2011

단백질 사이의 상호작용 네트워크(PPI network: Protein-Protein Interaction network)를 이용하여 단백질 기능을 예측 하는 것은 단백질 기능 예측 기법들 중에서 중요한 작용을 한다. 하지만 PPI를 이용한 단백질 기능 예측은 기능의 복잡도와 다양성으로 인해 제한적인 결과를 나타내 왔다. 따라서 본 논문에서는 기존의 연구들 보다 높은 정확도로 단백질 기능을 예측하기 위해 기능 예측을 하려는 단백질과 상호작용 하는 단백질들에 그래프 마이닝 기법을 적용하여 빈발 2-노드 상호작용 패턴을 찾고, 그 패턴을 이용하여 단백질 기능을 예측하는 접근법을 제안하였다. 실험데이터로 DIP(Database of Interacting Proteins)에서 제공하는 단백질 상호작용 데이터를 사용하였으며, 다른 기존의 단백질 기능 예측 기법들보다 높은 정확도를 보여주었다.

• Proceedings of the Korea Contents Association Conference
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• 2009.05a
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• pp.306-311
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• 2009

We initially obtained human diseases-related proteins dataset from the OMIM and the SWISS PROT and then constructed disease-related protein-protein interaction network. The protein network contains 40 hub proteins such as CALM1, ACTB and ABL2. The protein network can be derived the map of the relationship between different disease proteins, denoted disease interaction network. We demonstrate that the associations between diseases are directly correlated to their underlying protein-protein interaction networks. From constructed the disease-protein bipartite network, we derived 38 diseasomal proteins, including APP, ABL1 and STAT1. We previously demonstrated that hub proteins in the network tend to be diseasomal proteins in the disease-related protein sub-networks. However, we found that 18% hubs are only diseasomal proteins in the whole disease network. At this point, we could not elucidate difference in the hub-diseasomal proteins tendency between sub0network and whole network. In spite of we still have unsolved problems, our results elucidate that the discovery of protein interaction networks assigned by diseases will provide insight into the underlying molecular mechanisms and biological processes in complex human disease system.

• Asian Pacific Journal of Cancer Prevention
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• v.15 no.16
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• pp.6899-6904
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• 2014

NGAL (neutrophil gelatinase-associated lipocalin) is a novel cancer-related protein involves multiple functions in many cancers and other diseases. We previously overexpressed NGAL to analyze its role in esophageal squamous cell carcinoma (ESCC). In this study, a protein-protein interaction (PPI) was constructed and the shortest paths from NGAL to transcription factors in the network were analyzed. We found 28 shortest paths from NGAL to RELA, most of them obeying the principle of extracellular to cytoplasm, then nucleus. These shortest paths were also prioritized according to their normalized intensity from the microarray by the order of interaction cascades. A systems approach was developed in this study by linking differentially expressed genes with publicly available PPI data, Gene Ontology and subcellular localizaton for the integrated analyses. These shortest paths from NGAL to DEG transcription factors or other transcription factors in the PPI network provide important clues for future experimental identification of new pathways.

• Korean Journal of Cognitive Science
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• v.28 no.4
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• pp.299-314
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• 2017

Predicting compound-protein interactions in-silico is significant for the drug discovery. In this paper, we propose an scalable machine learning model to predict compound-protein interaction. The key idea of this scalable machine learning model is the architecture of pairwise neural network model and feature embedding method from the raw data, especially for protein. This method automatically extracts the features without additional knowledge of compound and protein. Also, the pairwise architecture elevate the expressiveness and compact dimension of feature by preventing biased learning from occurring due to the dimension and type of features. Through the 5-fold cross validation results on large scale database show that pairwise neural network improves the performance of predicting compound-protein interaction compared to previous prediction models.

• Genomics & Informatics
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• v.11 no.4
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• pp.200-210
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• 2013

Studying biological networks, such as protein-protein interactions, is key to understanding complex biological activities. Various types of large-scale biological datasets have been collected and analyzed with high-throughput technologies, including DNA microarray, next-generation sequencing, and the two-hybrid screening system, for this purpose. In this review, we focus on network-based approaches that help in understanding biological systems and identifying biological functions. Accordingly, this paper covers two major topics in network biology: reconstruction of gene regulatory networks and network-based applications, including protein function prediction, disease gene prioritization, and network-based genome-wide association study.

• Proceedings of the Korea Contents Association Conference
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• 2004.11a
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• pp.511-515
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• 2004

In the post-genomic era, researches on proteins as well as genes have been increasingly required. Particularly, work on protein-protein interaction and protein network construction have been recently establishing. Most biologists publish their research results through papers or other media. However, biologists do not use the information effectively, since the published research results are very large. As the growth of internet, it becomes easy to access very large research results. It is significantly important to extract information with a biological meaning from varisous media. Therefore, in this research, we efficiently extract protein-protein interaction information from many open papers or other media and construct the database of the extracted information. We build a protein network from the established database and then design and implement various pathway analysis algorithms which find biological meaning from the protein network.

• Asian Pacific Journal of Cancer Prevention
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• v.15 no.5
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• pp.2345-2351
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• 2014

Lysyl oxidase-like 2 (LOXL2), a member of the lysyl oxidase (LOX) family, is a copper-dependent enzyme that catalyzes oxidative deamination of lysine residues on protein substrates. LOXL2 was found to be overexpressed in esophageal squamous cell carcinoma (ESCC) in our previous research. We later identified a LOXL2 splicing variant LOXL2-delta72 and we overexpressed LOXL2-delta72 and its wild type counterpart in ESCC cells following microarray analyses. First, the differentially expressed genes (DEGs) of LOXL2 and LOXL2-delta72 compared to empty plasmid were applied to generate protein-protein interaction (PPI) sub-networks. Comparison of these two sub-networks showed hundreds of different proteins. To reveal the potential specific roles of LOXL2- delta72 compared to its wild type, the DEGs of LOXL2-delta72 vs LOXL2 were also applied to construct a PPI sub-network which was annotated by Gene Ontology. The functional annotation map indicated the third PPI sub-network involved hundreds of GO terms, such as "cell cycle arrest", "G1/S transition of mitotic cell cycle", "interphase", "cell-matrix adhesion" and "cell-substrate adhesion", as well as significant "immunity" related terms, such as "innate immune response", "regulation of defense response" and "Toll signaling pathway". These results provide important clues for experimental identification of the specific biological roles and molecular mechanisms of LOXL2-delta72. This study also provided a work flow to test the different roles of a splicing variant with high-throughput data.

• Korean Journal of Veterinary Service
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• v.42 no.2
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• pp.73-83
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• 2019

Foot-and-Mouth Disease (FMD) is a highly contagious trans-boundary viral disease caused by FMD virus, which causes huge economic losses. FMDV infects cloven hoofed (two-toed) mammals such as cattle, sheep, goats, pigs and various wildlife species. To control the FMDV, it is necessary to understand the life cycle and the pathogenesis of FMDV in host. Especially, the protein-protein interaction between FMDV and host will help to understand the survival cycle of viruses in host cell and establish new therapeutic strategies. However, the computational approach for protein-protein interaction between FMDV and pig hosts have not been applied to studies of the onset mechanism of FMDV. In the present work, we have performed the prediction of the pig's proteins which interact with FMDV based on RNA-Seq data, protein sequence, and structure information. After identifying the virus-host interaction, we looked for meaningful pathways and anticipated changes in the host caused by infection with FMDV. A total of 78 proteins of pig were predicted as interacting with FMDV. The 156 interactions include 94 interactions predicted by sequence-based method and the 62 interactions predicted by structure-based method using domain information. The protein interaction network contained integrin as well as STYK1, VTCN1, IDO1, CDH3, SLA-DQB1, FER, and FGFR2 which were related to the up-regulation of inflammation and the down-regulation of cell adhesion and host defense systems such as macrophage and leukocytes. These results provide clues to the knowledge and mechanism of how FMDV affects the host cell.

• IMMUNE NETWORK
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• v.2 no.3
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• pp.137-141
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• 2002

Among the members of the inhibitor of apoptosis (IAP) protein family, only Livin and survivin have been reported to have variant forms. We have found a variant form of c-IAP2 through the interaction with the X protein of HBV using the yeast two-hybrid system. In contrast to the wild-type c-IAP2, the variant form has two stretches of sequence in the RING domain that are repeated in the C-terminus that would disrupt the RING domain. We demonstrate that the variant form has an inhibitory effect on TNF-mediated $NF-{\kappa}B$ activation unlike the wild-type c-IAP2, which increases TNFmediated $NF-{\kappa}B$ activation. These results suggest that this variant form has different activities from the wild-type and the RING domain may be involved in the regulation of TNF-induced $NF-{\kappa}B$ activation.

• BMB Reports
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• v.50 no.9
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• pp.478-483
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• 2017

Budding yeast has dozens of prions, which are mutually dependent on each other for the de novo prion formation. In addition to the interactions among prions, transmissions of prions are strictly dependent on two chaperone systems: the Hsp104 and the Hsp70/Hsp40 (J-protein) systems, both of which cooperatively remodel the prion aggregates to ensure the multiplication of prion entities. Since it has been postulated that prions and the remodeling factors constitute complex networks in cells, a quantitative approach to describe the interactions in live cells would be required. Here, the researchers applied dual-color fluorescence cross-correlation spectroscopy to investigate the molecular network of interaction in single live cells. The findings demonstrate that yeast prions and remodeling factors constitute a network through heterogeneous protein-protein interactions.