Author Archives: Combio

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

Bayarbaatar Amgalan and Hyunju Lee (2015) DEOD: Uncovering dominant effects of cancer-driver genes based on a partial covariance selection method. Bioinformatics, 2015 Aug 1;31(15):2452-60. (IF: 4.981) (JCR: 4/52, 7.7%, MATHEMATICAL & COMPUTATIONAL BIOLOGY).

DEOD: Uncovering dominant effects of cancer-driver genes based on a partial covariance selection method.

 

Abstract

Motivation: The generation of a large volume of cancer genomes has allowed us to identify disease-related alterations more accurately, which is expected to enhance our understanding regarding the mechanism of cancer development. With genomic alterations detected, one challenge is to pinpoint cancer-driver genes that cause functional abnormalities.

Results: Here, we propose a method for uncovering the dominant effects of cancer-driver genes (DEOD) based on a partial covariance selection approach. Inspired by a convex optimization technique, it estimates the dominant effects of candidate cancer-driver genes on the expression level changes of their target genes. It constructs a gene network as a directed-weighted graph by integrating DNA copy numbers, single nucleotide mutations, and gene expressions from matched tumor samples, and estimates partial covariances between driver genes and their target genes. Then, a scoring function to measure the cancer-driver score for each gene is applied. To test the performance of DEOD, a novel scheme is designed for simulating conditional multivariate normal variables (targets and free genes) given a group of variables (driver genes). When we applied the DEOD method to both the simulated data and breast cancer data, DEOD successfully uncovered driver variables in the simulation data, and identified well-known oncogenes in breast cancer. In addition, two highly ranked genes by DEOD were related to survival time. The copy number amplifications of MYC (8q24.21) and TRPS1 (8q23.3) were closely related to the survival time with p-values = 0.00246 and 0.00092, respectively. The results demonstrate that DEOD can efficiently uncover cancer-driver genes.

Availability: DEOD was implemented in Matlab, and source codes and data are available at http://combio.gist.ac.kr/softwares/

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.

 

Workshop on November 2014

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DigSee was presented at Microsoft Research Asia Faculty Summit 2014.

 

DigSee (Disease gene search engine) was presented at Microsoft Research Asia Faculty Summit 2014, which was held at Beijing, China on October 30-31, 2014 (http://research.microsoft.com/en-us/events/asiafacsum2014/). Find more information about the session ‘computing in Science’ (http://research.microsoft.com/en-US/events/asiafacsum2014/abstracts.aspx) and  DemoFest (http://research.microsoft.com/en-US/events/asiafacsum2014/demofest.aspx).

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.

 

‘Bioinformatics Story’ by Prof. Hyunju Lee (Science column, 2014 GIST magazine Vol. 19 No. 1)

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Professor Hyunju Lee was invited to Microsoft Research Faculty Summit 2014 to present her text mining research.

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The fifteenth annual Microsoft Research Faculty summit was held on July 14-15, 2014 (http://research.microsoft.com/en-US/events/fs2014/default.aspx).  Leading academic researchers and educators joined Microsoft researchers and engineers to explore future technology trends that will define the twenty-first century (http://research.microsoft.com/en-US/events/fs2014/speakers.aspx).

In the “Science in the Cloud” session at its second day, Professor Hyunju Lee presented her research about “Disease gene search engine (DigSee): Text mining diseae-gene-biological event relationships”. This session illustrates work by academic researchers who have been awarded “Microsoft Azure for Research” cloud awards. Out of the 190 projects awarded, four projects including Prof. Lee’s DigSee project were highlighted (http://research.microsoft.com/en-US/events/fs2014/abstracts.aspx#cloud), and a presentation slide is uploaded (http://research.microsoft.com/en-us/events/fs2014/agenda.aspx). In the “DemoFest”, demo for DigSee was presented (http://research.microsoft.com/en-US/events/fs2014/demonstrations.aspx).

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Workshop on January 27th, 2014

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