Category Archives: Publications

Baeksoo Kim, Jihoon Jo, Jonghyun Han, Chungoo Park* and Hyunju Lee* (2017) In silico re-identification of properties of drug target proteins. BMC Bioinformatics, 31 May 2017;18(Suppl 7):248. (IF: 2.448) (JCR 2016: 10/57, 17.5%, Oncology) ($: co-corresponding authors). (Presented at DTMBIO 2016 in conjuction with CIKM, Indianapolis, USA)

In silico re-identification of properties of drug target proteins.

  • Author : Baeksoo Kim, Jihoon Jo, Jonghyun Han,  Chungoo Park and Hyunju Lee
  • Published Date : 2016
  • Category : Bioinformatics
  • Place of publication : BMC Bioinformatics

 

Abstract

Computational approaches in the identification of drug targets are expected to reduce time and effort in drug development. Advances in genomics and proteomics provide the opportunity to uncover properties of druggable genomes. Although several studies have been conducted for distinguishing drug targets from non-drug targets, they mainly focus on the sequences and functional roles of proteins. Many other
properties of proteins have not been fully investigated. In this study, we first confirm previously known properties of drug targets with a higher statistical power by analyzing larger sets of drugs and targets. We then suggest new properties, such as gene essentiality, gene expression levels, tissue specificity, and solvent accessibility. We predict drug targets based on these features using a support vector machine and
a random forest method. We believe that our study will provide a new aspect in inferring drug-target interactions.

Jonghyun Han and Hyunju Lee* (2016) Characterizing the interests of social media users: Refinement of a topic model for incorporating heterogeneous media. Information Sciences, 2016 September 01; 358-359:112-128 (IF: 3.364) (JCR 2015: 8/144, 5.56%, COMPUTER SCIENCE, INFORMATION SYSTEMS)

Characterizing the interests of social media users: Refinement of a topic model for incorporating heterogeneous media.

  • Author : Jonghyun Han and Hyunju Lee
  • Published Date : 2016
  • Category : Mining in Social Network
  • Place of publication : Information Sciences

 

Abstract

Recent research has focused on extracting personal interest data from social media. Although many methods have been developed, accurately estimating users’ interests is often difficult because messages on social media are short and are not classified into any predefined categories. We propose a new method to overcome this problem by incorporating heterogeneous media, such as news. In our method, we first extract explicit features and implicit topics of categories using news media, where implicit topics are determined using a refined topic model. Next, we describe social media messages using these features and topics to estimate users’ interests. Compared with several other approaches, our approach provides more accurate estimations of users’ interests. We also demonstrate that the accuracy of friend recommendations is increased using the users’ interests estimated by our method. Thus, we expect that the proposed approach could be helpful for enhancing the personalization of social media services.

Wangin Kim, Sangbin Park, Chanhun Choi, Youg Ran Kim, Inkyu Park, Changseob Seo, Daehwan Youn, Wook Shin, Yumi Lee, Donghee Choi, Mirae Kim, Hyunju Lee, Seonjong Kim, and Changsu Na (2016) Evaluation of Anti-Inflammatory Potential of the New Ganghwaljetongyeum on Adjuvant-Induced Inflammatory Arthritis in Rats. Evidence-Based Complementary and Alternative Medicine, 2016 June 13; 2016:1230294 (IF 1.931) (JCR 2015: 7/24, 29.2%, INTEGRATIVE & COMPLEMENTARY MEDICINE).

Evaluation of Anti-Inflammatory Potential of the New Ganghwaljetongyeum on Adjuvant-Induced Inflammatory Arthritis in Rats.

  • Author :Wangin Kim, Sangbin Park, Chanhun Choi, Youg Ran Kim, Inkyu Park, Changseob Seo, Daehwan Youn, Wook Shin, Yumi Lee, Donghee Choi, Mirae Kim, Hyunju Lee, Seonjong Kim, and Changsu Na
  • Published Date : 2016
  • Category : Bioinformatics and Text Mining 
  • Place of publication : Evidence-Based Complementary and Alternative Medicine

 

Abstract

Ganghwaljetongyeum (GHJTY) has been used as a standard treatment for arthritis for approximately 15 years at the Korean Medicine Hospital of Dongshin University. GHJTY is composed of 18 medicinal herbs, of which five primary herbs were selected and named new Ganghwaljetongyeum (N-GHJTY). The purpose of the present study was to observe the effect of N-GHJTY on arthritis and to determine its mechanism of action. After confirming arthritis induction using complete Freund’s adjuvant (CFA) in rats, N-GHJTY (62.5, 125, and 250 mg/kg/day) was administered once a day for 10 days. In order to determine pathological changes, edema of the paws and weight were measured before and for 10 days after N-GHJTY administration. Cytokine (TNF-α, IL-1β, and IL-6) levels and histopathological lesions in the knee joint were also examined. Edema in the paw and knee joint of N-GHJTY-treated rats was significantly decreased at 6, 8, and 10 days after administration, compared to that in the CFA-control group, while weight consistently increased. Rats in N-GHJTY-treated groups also recovered from the CFA-induced pathological changes and showed a significant decline in cytokine levels. Taken together, our results showed that N-GHJTY administration was effective in inhibiting CFA-induced arthritis via anti-inflammatory effects while promoting cartilage recovery by controlling cytokine levels.

Ho Jang, Youngmi Hur and Hyunju Lee. Identification of cancer-driver genes in focal genomic alterations from whole genome sequencing data. Scientific Reports, 2016 May 09; 6:25582 (IF: 5.578) (JCR: 5/57, 8.8%, MULTIDISCIPLINARY SCIENCES).

Identification of cancer-driver genes in focal genomic alterations from whole genome sequencing data

  • Author : Jang Ho, Youngmi Hur,and Hyunju Lee
  • Published Date : 2016
  • Category : Bioinformatics and Text Mining 
  • Place of publication : Scientific Reports

 

Abstract

DNA copy number alterations (CNAs) are the main genomic events that occur during the initiation and development of cancer. Distinguishing driver aberrant regions from passenger regions, which might contain candidate target genes for cancer therapies, is an important issue. Several methods for identifying cancer-driver genes from multiple cancer patients have been developed for single nucleotide polymorphism (SNP) arrays. However, for NGS data, methods for the SNP array cannot be directly applied because of different characteristics of NGS such as higher resolutions of data without predefined probes and incorrectly mapped reads to reference genomes. In this study, we developed a wavelet-based method for identification of focal genomic alterations for sequencing data (WIFA-Seq). We applied WIFA-Seq to whole genome sequencing data from glioblastoma multiforme, ovarian serous cystadenocarcinoma and lung adenocarcinoma, and identified focal genomic alterations, which contain candidate cancer-related genes as well as previously known cancer-driver genes.

 

Wonjun Choi, Chan-Hun Choi, Young Ran Kim, Seon-Jong Kim, Chang-Su Na and Hyunju Lee. HerDing: herb recommendation system to treat diseases using genes and chemicals. Database (Oxford), 2016 March 15; 2016:baw011 (IF: 3.372) (JCR: 7/57, 12.3%, MATHEMATICAL & COMPUTATIONAL BIOLOGY).

HerDing: herb recommendation system to treat diseases using genes and chemicals.

  • Author :Wonjun Choi, Chan-Hun Choi, Young Ran Kim, Seon-Jong Kim, Chang-Su Na and Hyunju Lee
  • Published Date : 2016
  • Category : Bioinformatics and Text Mining 
  • Place of publication : Database-Oxford

 

Abstract

In recent years, herbs have been researched for new drug candidates because they have a long empirical history of treating diseases and are relatively free from side effects. Studies to scientifically prove the medical efficacy of herbs for target diseases often spend a considerable amount of time and effort in choosing candidate herbs and in performing experiments to measure changes of marker genes when treating herbs. A computational approach to recommend herbs for treating diseases might be helpful to promote efficiency in the early stage of such studies. Although several databases related to traditional Chinese medicine have been already developed, there is no specialized Web tool yet recommending herbs to treat diseases based on disease-related genes. Therefore, we developed a novel search engine, HerDing, focused on retrieving candidate herb-related information with user search terms (a list of genes, a disease name, a chemical name or an herb name). HerDing was built by integrating public databases and by applying a text-mining method. The HerDing website is free and open to all users, and there is no login requirement.

Database URL: http://combio.gist.ac.kr/herding

Daeyong Jin and Hyunju Lee (2015) A Computational Approach to Identifying Gene-microRNA Modules in Cancer. PLoS Computational Biology, 2015 Jan 22; 11(1):e1004042. (IF: 4.829) (JCR: 3/52, 5.8%, MATHEMATICAL & COMPUTATIONAL BIOLOGY).

A Computational Approach to Identifying Gene-microRNA Modules in Cancer.

  • Author : Daeyong Jin and Hyunju Lee
  • Published Date : 2015
  • Category : Bioinformatics and Text Mining 
  • Place of publication : PLoS Computational Biology

 

Abstract

MicroRNAs (miRNAs) play key roles in the initiation and progression of various cancers by regulating genes. Regulatory interactions between genes and miRNAs are complex, as multiple miRNAs can regulate multiple genes. In addtion, these interactions vary from patient to patient and even among patients with the same cancer type, as cancer development is a heterogeneous process. These relationships are more complicated because transcription factors and other regulatory molecules can also regulate miRNAs and genes. Hence, it is important to identify the complex relationships between genes and miRNAs in cancer. In this study, we propose a computational approach to constructing modules that represent these relationships by integrating the expression data of genes and miRNAs with gene-gene interaction data. First, we used a biclustering algorithm to construct modules consisting of a subset of genes and a subset of samples to incorporate the heterogeneity of cancer cells. Second, we combined gene-gene interactions to include genes that play important roles in cancer-related pathways. Then, we selected miRNAs that are closely associated with genes in the modules based on a Gaussian Bayesian network and Bayesian Information Criteria. When we applied our approach to ovarian cancer and glioblastoma (GBM) data sets, 33 and 54 modules were constructed, respectively. In these modules, 91% and 94% of ovarian cancer and GBM modules, respectively, were explained either by direct regulation between genes and miRNAs or by indirect relationships via transcription factors. In addition, 48.4% and 74.0% of modules from ovarian cancer and GBM, respectively, were enriched with cancer-related pathways, and 51.7% and 71.7% of miRNAs in modules were ovarian cancer-related miRNAs and GBM-related miRNAs, respectively. Finally, we extensively analyzed significant modules and showed that most genes in these modules were related to ovarian cancer and GBM.

 

Jonghyun Han and Hyunju Lee (2015) Adaptive Landmark Recommendations for Travel Planning: Personalizing and Clustering Landmarks using Geo-Tagged Social Media. Pervasive and Mobile Computing. 18:4-17 (IF: 2.079) (COMPUTER SCIENCE, NFORMATION SYSTEMS: 20/139)

Adaptive Landmark Recommendations for Travel Planning: Personalizing and Clustering Landmarks using Geo-Tagged Social Media. Pervasive and Mobile Computing.

  • Author : Jonghyun Han and Hyunju Lee
  • Published Date : 2014
  • Category : Mining in Social Network
  • Place of publication : Pervasive and Mobile Computing

 

Abstract

When travelers plan their trips, landmark recommendation systems considering the properties of their trips will be convenient to help travelers determine locations they will visit. Because interesting content may vary according to travelers and their situations, it is important to recommend personalized landmarks by considering them and their trips. In this paper, we propose an approach that adaptively recommends clusters of landmarks using geo-tagged social media. We first examine the impact of spatial and temporal properties of a trip on the distribution of popular places through large-scale data analysis. Our approach is to compute the significance of landmarks for travelers according to the spatial and temporal properties of their trips. Then, we generate clusters of recommended landmarks, which have similar theme or are contiguous, by utilizing histories of travels’ trajectories. Performances of recommended landmarks by our approach are evaluated against several baseline approaches, showing increased accuracy and satisfaction, compared to the baselines. Through a user study, we also verify that it is applicable to lesser-known places and reflective of local events and seasonal changes. Thus, we expect that the approach is helpful in developing personalized recommendations.

Bayabaatar Amgalan and Hyunju Lee (2014) WMAXC: a weighted maximum clique method for identifying condition-specific sub-network. PLoS One, 2014 Aug 22; 9(8): e104993 (IF: 3.534)

WMAXC: a weighted maximum clique method for identifying condition-specific sub-network.

 

Abstract

Sub-networks can expose complex patterns in an entire bio-molecular network by extracting interactions that depend on temporal or condition-specific contexts. When genes interact with each other during cellular processes, they may form differential co-expression patterns with other genes across different cell states. The identification of condition-specific sub-networks is of great importance in investigating how a living cell adapts to environmental changes. In this work, we propose the weighted MAXimum clique (WMAXC) method to identify a condition-specific sub-network. WMAXC first proposes scoring functions that jointly measure condition-specific changes to both individual genes and gene-gene co-expressions. It then employs a weaker formula of a general maximum clique problem and relates the maximum scored clique of a weighted graph to the optimization of a quadratic objective function under sparsity constraints. We combine a continuous genetic algorithm and a projection procedure to obtain a single optimal sub-network that maximizes the objective function (scoring function) over the standard simplex (sparsity constraints). We applied the WMAXC method to both simulated data and real data sets of ovarian and prostate cancer. Compared with previous methods, WMAXC selected a large fraction of cancer-related genes, which were enriched in cancer-related pathways. The results demonstrated that our method efficiently captured a subset of genes relevant under the investigated condition.

 

Hee-Jin Lee, Tien Cuong Dang, Hyunju Lee, and Jong C. Park (2014) OncoSearch: Cancer Gene Search Engine with Literature Evidence Nucleic Acids Research (9 May 2014) (IF: 8.278).

OncoSearch: Cancer Gene Search Engine with Literature Evidence  Nucleic Acids Research.

  • Author : Heejin Lee, Tien Cuong Dang, Hyunju Lee, and Jong C. Park
  • Published Date : 2014
  • Category : Bioinformatics and Text Mining 
  • Place of publication : Nucleic acids research

 

Abstract

In order to identify genes that are involved in oncogenesis and to understand how such genes affect cancers, abnormal gene expressions in cancers are actively studied. For an efficient access to the results of such studies that are reported in biomedical literature, the relevant information is accumulated via text-mining tools and made available through the Web. However, current Web tools are not yet tailored enough to allow queries that specify how a cancer changes along with the change in gene expression level, which is an important piece of information to understand an involved gene’s role in cancer progression or regression. OncoSearch is a Web-based engine that searches Medline abstracts for sentences that mention gene expression changes in cancers, with queries that specify (i) whether a gene expression level is up-regulated or down-regulated, (ii) whether a certain type of cancer progresses or regresses along with such gene expression change and (iii) the expected role of the gene in the cancer. OncoSearch is available through http://oncosearch.biopathway.org

Paper :   link 

Website :   link

Media covered :  전자신문 (Electronic Times) (2014. 05. 22) 국제신문 (2014.05. 22)뉴스1 (News1) (2014.05.22)

Song, B. and Lee, H. (2012) Prioritizing Disease Genes by Integrating Domain Interactions and Disease Mutations in a Protein-Protein Interaction Network,IJICIC, 8(2), 1327-1338

Prioritizing Disease Genes by Integrating Domain Interactions and Disease Mutations in a Protein-Protein Interaction Network

Abstract

Complex diseases such as cancer are involved in inter-relationship amongseveral genes, with protein-protein interaction networks being extensively studied in at-tempts to reveal the relationship between genes and diseases. Although these studies haveshown promising results for identifying disease genes, it is not systemically studied that aprotein functions differently depending on its interaction partners in the network since aprotein can have multiple functions. In this study, domains are considered as functionalunits of proteins and we investigate how disease-related mutations in domains can be usedto identify other disease genes in a domain-domain interaction network. We subsequentlypropose a computational method to predict disease genes based on the following two as-sumptions. The first assumption is that proteins closely interacting with known diseaseproteins in a protein interaction network are likely to be involved in the same disease.Second, although two proteins are in the same distance from known disease genes in aprotein interaction network, the protein interacting with known disease genes through adomain with mutation is more likely to be related to the disease than other proteins thatinteract through domains with no mutation. As a result, when the proposed approach isapplied to five diseases, it highly ranks disease-related genes compared to a model usingonly a protein interaction data set.