• 한국콘텐츠학회논문지
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• 제9권4호
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• pp.114-120
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• 2009

본 연구는 질병과 관련이 있는 단백질들은 질병 네트워크를 형성함에 있어서 매우 중요한 인자로 작용할 가능성이 있다는 아이디어에서 출발한다. 우리는 Online Medelian Inheritance in Man(OMIM)으로부터 아토피관련 43개 단백질 데이터베이스를 확보하고 이 단백질들과 상호작용하는 단백질 네트워크를 구축하였다. 아토피관련 단백질 네트워크를 바탕으로 질병 네트워크를 구축하였다. 질병 네트워크로부터 질병단백체인 CCR5, CCL11, 및 IL4R을 발굴하였는데, 이들 모두는 단백질 네트워크에서 허브 단백질로 작용하는 것들이다. 허브단백질은 세포에서 필수단백질로 작용하는 것으로 알려져 있는데, 본 연구에서는 허브단백질이면서 동시에 질병에서 매우 중요한 역할을 할 것으로 기대되는 질병단백체로 역할하고 있음을 확인하였다. 본 연구에서 소규모 아토피 관련 질병네트워크를 구축하여 분석하였지만, 여기에 제안한 질병네트워크 분석이 복잡한 인간 질병체계의 분자 기작 및 생물학적 진행과정을 이해하는데 실마리를 제공할 것으로 기대한다.

• 정보과학회 컴퓨팅의 실제 논문지
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• 제23권3호
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• pp.194-198
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• 2017

본 논문에서는 학술 문헌에서 표현된 단백질 간 상호 작용(Protein-Protein Interaction) 정보를 자동으로 추출하기 위한 확장된 형태의 Convolutional Neural Network (CNN) 모델을 제안한다. 이 모델은 기존에 관계 추출(Relation Extraction)을 위해 고안된 단순 자질 기반의 CNN 모델을 확장하여 다양한 전역 자질들을 추가적으로 적용함으로써 성능을 개선할 수 있는 장점이 있다. PPI 추출 성능 평가를 위해서 많이 활용되고 있는 준거 평가 컬렉션인 AIMed를 이용한 실험에서 F-스코어 기준으로 78.0%를 나타내어 현재까지 도출된 세계 최고 성능에 비해 8.3% 높은 성능을 나타내었다. 추가적으로 CNN 모델이 복잡한 언어 처리를 통한 자질 추출 작업을 하지 않고도 단백질간 상호 작용 추출에 높은 성능을 나타냄을 보였다.

• Genomics & Informatics
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• 제21권1호
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• pp.6.1-6.8
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• 2023

Glaucoma is the second leading cause of irreversible blindness, and primary open-angle glaucoma (POAG) is the most common type. Due to inadequate diagnosis, treatment is often not administered until symptoms occur. Hence, approaches enabling earlier prediction or diagnosis of POAG are necessary. We aimed to identify novel drugs for glaucoma through bioinformatics and network analysis. Data from 36 samples, obtained from the trabecular meshwork of healthy individuals and patients with POAG, were acquired from a dataset. Next, differentially expressed genes (DEGs) were identified to construct a protein-protein interaction (PPI) network. In both stages, the genes were enriched by studying the critical biological processes and pathways related to POAG. Finally, a drug-gene network was constructed, and novel drugs for POAG treatment were proposed. Genes with p < 0.01 and |log fold change| > 0.3 (1,350 genes) were considered DEGs and utilized to construct a PPI network. Enrichment analysis yielded several key pathways that were upregulated or downregulated. For example, extracellular matrix organization, the immune system, neutrophil degranulation, and cytokine signaling were upregulated among immune pathways, while signal transduction, the immune system, extracellular matrix organization, and receptor tyrosine kinase signaling were downregulated. Finally, novel drugs including metformin hydrochloride, ixazomib citrate, and cisplatin warrant further analysis of their potential roles in POAG treatment. The candidate drugs identified in this computational analysis require in vitro and in vivo validation to confirm their effectiveness in POAG treatment. This may pave the way for understanding life-threatening disorders such as cancer.

• 한국생물정보학회:학술대회논문집
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• 한국생물정보시스템생물학회 2003년도 제2차 연례학술대회 발표논문집
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• pp.26-51
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• 2003

Large scale protein interaction maps provide a new, global perspective with which to analyse protein function. PSIMAP, the Protein Structural Interactome Map, is a database of all the structurally observed interactions between superfamilies of protein domains with known three-dimensional structure in thePDB. PSIMAP incorporates both functional and evolutionary information into a single network. It makes it possible to age protein domains in terms of taxonomic diversity, interaction and function. One consequence of it is to predict the most important protein domain structure in evolution. We present a global analysis of PSIMAP using several distinct network measures relating to centrality, interactivity, fault-tolerance, and taxonomic diversity. We found the following results: ${\bullet}$ Centrality: we show that the center and barycenter of PSIMAP do not coincide, and that the superfamilies forming the barycenter relate to very general functions, while those constituting the center relate to enzymatic activity. ${\bullet}$ Interactivity: we identify the P-loop and immunoglobulin superfamilies as the most highly interactive. We successfully use connectivity and cluster index, which characterise the connectivity of a superfamily's neighbourhood, to discover superfamilies of complex I and II. This is particularly significant as the structure of complex I is not yet solved. ${\bullet}$ Taxonomic diversity: we found that highly interactive superfamilies are in general taxonomically very diverse and are thus amongst the oldest. This led to the prediction of the oldest and most important protein domain in evolution of lift. ${\bullet}$ Fault-tolerance: we found that the network is very robust as for the majority of superfamilies removal from the network will not break up the network. Overall, we can single out the P-loop containing nucleotide triphosphate hydrolases superfamily as it is the most highly connected and has the highest taxonomic diversity. In addition, this superfamily has the highest interaction rank, is the barycenter of the network (it has the shortest average path to every other superfamily in the network), and is an articulation vertex, whose removal will disconnect the network. More generally, we conclude that the graph-theoretic and taxonomic analysis of PSIMAP is an important step towards the understanding of protein function and could be an important tool for tracing the evolution of life at the molecular level.

• Asian Pacific Journal of Cancer Prevention
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• 제14권8호
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• pp.4621-4625
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• 2013

The purpose of this study was to construct a protein-protein interaction (PPI) network related to oral squamous cell carcinoma (OSCC). Each protein was ranked and those most associated with OSCC were mined within the network. First, OSCC-related genes were retrieved from the Online Mendelian Inheritance in Man (OMIM) database. Then they were mapped to their protein identifiers and a seed set of proteins was built. The seed proteins were expanded using the nearest neighbor expansion method to construct a PPI network through the Online Predicated Human Interaction Database (OPHID). The network was verified to be statistically significant, the score of each protein was evaluated by algorithm, then the OSCC-related proteins were ranked. 38 OSCC related seed proteins were expanded to 750 protein pairs. A protein-protein interaction nerwork was then constructed and the 30 top-ranked proteins listed. The four highest-scoring seed proteins were SMAD4, CTNNB1, HRAS, NOTCH1, and four non-seed proteins P53, EP300, SMAD3, SRC were mined using the nearest neighbor expansion method. The methods shown here may facilitate the discovery of important OSCC proteins and guide medical researchers in further pertinent studies.

• Molecules and Cells
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• 제27권3호
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• pp.271-277
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• 2009

Proteins interact with each other within a cell, and those interactions give rise to the biological function and dynamical behavior of cellular systems. Generally, the protein interactions are temporal, spatial, or condition dependent in a specific cell, where only a small part of interactions usually take place under certain conditions. Recently, although a large amount of protein interaction data have been collected by high-throughput technologies, the interactions are recorded or summarized under various or different conditions and therefore cannot be directly used to identify signaling pathways or active networks, which are believed to work in specific cells under specific conditions. However, protein interactions activated under specific conditions may give hints to the biological process underlying corresponding phenotypes. In particular, responsive functional modules consist of protein interactions activated under specific conditions can provide insight into the mechanism underlying biological systems, e.g. protein interaction subnetworks found for certain diseases rather than normal conditions may help to discover potential biomarkers. From computational viewpoint, identifying responsive functional modules can be formulated as an optimization problem. Therefore, efficient computational methods for extracting responsive functional modules are strongly demanded due to the NP-hard nature of such a combinatorial problem. In this review, we first report recent advances in development of computational methods for extracting responsive functional modules or active pathways from protein interaction network and microarray data. Then from computational aspect, we discuss remaining obstacles and perspectives for this attractive and challenging topic in the area of systems biology.

• Journal of Animal Science and Technology
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• 제60권6호
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• pp.11.1-11.7
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• 2018

After pubertal, cohort of small antral follicles enters to gonadotrophin-sensitive development, called recruited follicles. This study was aimed to identify candidate genes in follicular cyclic recruitment via analysis of protein-protein interaction (PPI) network. Differentially expressed genes (DEGs) in ovine granulosa cells of small antral follicles between follicular and luteal phases were accumulated among gene/protein symbols of the Ensembl annotation. Following directed graphs, PTPN6 and FYN have the highest indegree and outdegree, respectively. Since, these hubs being up-regulated in ovine granulosa cells of small antral follicles during the follicular phase, it represents an accumulation of blood immune cells in follicular phase in comparison with luteal phase. By contrast, the up-regulated hubs in the luteal phase including CDK1, INSRR and TOP2A which stimulated DNA replication and proliferation of granulosa cells, they known as candidate genes of the cyclic recruitment.

• 한국정보과학회논문지:소프트웨어및응용
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• 제31권12호
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• pp.1602-1610
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• 2004

다양한 유전체 프로젝트로 말미암아 엄청난 서열 데이타들이 쏟아지고, 이에 따라 핵산 및 단백질 수준의 서열 데이타 분석이 매우 중요하게 인식되고 있다. 특히 최근에는 단백질이 단순하게 개별적인 기능을 가진 독립적인 요소가 아닌 전체 단백질 상호작용 네트워크 상에서 다른 객체들과 유기적인 관계를 갖으며 나름대로의 중요한 역할을 수행하고 있다는 점에 초점을 맞추어 연구가 진행되고 있다. 특히 단백질 상호작용 관계 분석을 위해서는 실제로 상호작용이 일어나는 도메인 모듈 정보가 아주 중요하게 작용하는데, 본 논문에서는 이러한 점을 고려하여 우리가 개발한 단백질 기능 및 상호작용 분석을 위한 PIVS(Protein-protein interaction Inference and Visualization System)에 대해 소개하고 있다 PIVS는 기존의 단백질 상호작용 데이타베이스들을 합쳐서 통합 데이타베이스를 생성하고, 다양한 전처리 과정으로 통합 데이타베이스 서열의 기능 및 주석 정보를 추출하여 로컬 데이타베이스화 하였다. 그리고 특히 단백질 상호작용 관계 분석을 위해 중요하게 작용하는 도메인 모듈 정보들을 추출하여 로컬 데이터베이스를 구축하였고, 기존의 단백질 상호작용 관계 데이타를 이용하석 도메인 사이의 상호작용 관계 정보도 수집하여 분석하였다. PIVS는 단백질의 종합적인 분석 정보, 즉, 기능 및 주석, 도메인, 상호작용 관계 정보 등을 손쉽고 편리하게 얻고자 하는 사용자에게 매우 유용하게 사용될 수 있을 것이다.

• IMMUNE NETWORK
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• 제2권3호
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• pp.125-132
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• 2002

In the past, bioinformatics was often regarded as a difficult and rather remote field, practiced only by computer scientists and not a practical tool available to biologists. However, the various on-going genome projects have had a serious impact on biological sciences in various ways and now there is little doubt that bioinformatics is an essential part of the research environment, with a wealth of biological information to analyze and predict. Fully sequenced genomes made us to have additional insights into the functional properties of the encoded proteins and made it possible to develop new tools and schemes for functional biology on a proteomic scale. Among those are the yeast two-hybrid system, mass spectrometry and microarray: the technology of choice to detect protein-protein interactions. These functional insights emerge as networks of interacting proteins, also known as "pathway informatics" or "interactomics". Without exception it is no longer possible to make advances in the signaling/regulatory pathway studies without integrating information technologies with experimental technologies. In this paper, we will introduce the databases of protein interaction worldwide and discuss several challenging issues regarding the actual implementation of databases.

• BMB Reports
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• 제37권1호
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• pp.45-52
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• 2004

The goal of interaction proteomics that studies the protein-protein interactions of all expressed proteins is to understand biological processes that are strictly regulated by these interactions. The availability of entire genome sequences of many organisms and high-throughput analysis tools has led scientists to study the entire proteome (Pandey and Mann, 2000). There are various high-throughput methods for detecting protein interactions such as yeast two-hybrid approach and mass spectrometry to produce vast amounts of data that can be utilized to decipher protein functions in complicated biological networks. In this review, we discuss recent developments in analytical methods for large-scale protein interactions and the future direction of interaction proteomics.