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Hasun Yu, Ph.D.

  CONTACT INFORMATION  

Translational Genome Informatics Laboratory

Severance Biomedical Science Institute

Yonsei University College of Medicine

ABMRC #511, 50-1 Yonsei-ro, Seodaemun-gu,

Seoul 03722, South Korea.

Office: +82-2-2228-0914

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  RESEARCH AREAS  

Drug effect prediction, NGS analysis, Network biology, and Machine learning

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  EDUCATION  

Korea Advanced Institute of Science and Technology                Daejeon, Korea

  • ​Ph.D., Bioinformatics                                                            2011.09-2017.02 

  • M.S., Bioinformatics                                                              2010.02-2011.08

 

Hanyang university                                                                            Seoul, Korea

  • B.S., Biomedical engineering                                               2006.03-2010.02

 

 

  RESEARCH EXPERIENCES  

Developing novel network-based methods to predict drug effects on diseases [Sep. 2013 – Present]

  • PDOD: Predict drugs having opposite effects on diseases states with ‘effect type’ such as ‘activation’ or ‘inhibition’ in biological networks and altered states of gene expressions in diseases (Using KEGG pathways, DrugBank, CTD, GEO).

  • CODA: Construct a tissue-specific network with protein expression data and literature and predict effects of drugs on diseases with the network. Distinct from other previous tissue-specific networks, our constructed network includes GO terms and diseases with anatomical context and intercellular associations (Using KEGG pathways, BioGRID, TRANSFAC, GO, EndoNet, CTD, PubMed, PhenoGO, HPA).

  • HIDEEP: Identify hormones affecting drugs with hormone-receptor relations, drug-target interactions, and protein-protein interactions (Using KEGG, BioGRID, DrugBank, EndoNet).

 

Identifying somatic mutations with sequencing data [Jan. 2017 – Present]

  • Calling somatic mutations: Develop a pipeline to identify putative somatic mutations from brain samples without matched control.

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eQTL analysis with SNP chip data and RNA sequencing data [Jan. 2012 – Jan.2013]

  • eQTL analysis: Identify associations between mutations and gene expressions by eQTL analysis in schizophrenia patients (Stanley database).

 

Analyze transcriptome in whole brain regions [Sep. 2010 – Aug. 2011]

  • Microarray data analysis: Analyze transcriptome patterns among whole brain regions using gene expression data (Allen Brain Atlas). ​

 

  PUBLICATIONS  

JOURNAL (Peer Reviewed)

  1. Yu, H.*, Jung, J.*, S. Yoon, M. Kwon, S. Bae, S. Yim, J. Lee, S. Kim, Y. Kang and D. Lee (2017), " CODA: Integrating multi-level context-oriented directed associations for analysis of drug effects." Scientific Reports 7, 7519.

  2. Yu, H., S. Choo, J. Park, J. Jung, Y. Kang and D. Lee (2016). "Prediction of drugs having opposite effects on disease genes in a directed network." BMC Systems Biology 10(1): 17-25.

  3. Yoon, S., J. Jung, H. Yu, M. Kwon, S. Choo, K. Park, D. Jang, S. Kim and D. Lee (2015). "Context-based resolution of semantic conflicts in biological pathways." BMC Med Inform Decis Mak 15 Suppl 1: S3.

  4. Yu, H., J. Bang, Y. Jo and D. Lee (2012). "Combining Neuroinformatics Databases for Multi-Level Analysis of Brain Disorders." IBC 4: 7.

 

International conferences

  1. Yim, S., H. Yu, D. Jang and D. Lee, "Annotating activation/inhibition relationships to protein-protein interactions using gene ontology", 25th Annual International Conference on Intelligent Systems for Molecular Biology/16th European Conference on Computational Biology (ISMB/ECCB), Prague, Czech Republic, Jul. 21-25, 2017.

  2. Yu, H., S. Choo, J. Park, J. Jung, Y. Kang and D. Lee, "PDOD: Prediction of Drugs Having Opposite Effects on Disease Genes in a Directed Network", The fourteenth Asia Pacific Bioinformatics Conference (APBC), San Francisco Bay Area, United States, Jan. 11-13, 2016.

  3. Yu, H., J. Jung, S. Yoon, M. Kwon, Y. Kang, S. Bae and D. Lee, "Development of a Framework for Constructing a Virtual Physiological Human with the Integration of COntext Specific Directed Associations (CODA)", 8th International Workshop on Data and Text Mining in Bioinformatics (DTMBIO), Melbourne, Australia, Oct. 23, 2015.

  4. Yu, H., S. Choo, J. Park, J. Jung, Y. Kang and D. Lee, "Prediction of drugs having opposite effects on disease genes", 23rd Annual International Conference on Intelligent Systems for Molecular Biology/14th European Conference on Computational Biology (ISMB/ECCB), Dublin, Ireland, Jul. 10-14, 2015.

  5. Jung, J., Yu, H., S. Yoon, M. Kwon, S. Choo, S. Kim and D. Lee, "Construction of Multi-level Networks Incorporating Molecule, Cell, Organ and Phenotype Properties for Drug-induced Phenotype Prediction"", 7th International Workshop on Data and Text Mining in Bioinformatics (DTMBIO), Shanghai, China, Nov. 7, 2014.

  6. Yoon, S., J. Jung, H. Yu, M. Kwon, S. Choo, K. Park, D. Jang, S. Kim and D. Lee, "Systematic Identification of Context-dependent Conflicting Information in Biological Pathways", 7th International Workshop on Data and Text Mining in Bioinformatics (DTMBIO), Shanghai, China, Nov. 7, 2014.

  7. Yu, H. and D. Lee, "Functional Analysis of Human Whole Brain Regions Based on Gene Expression", 5th ACM International Workshop on Data and Text Mining in Biomedical Informatics (DTMBIO), Glasgow, Scotland, Oct. 24, 2011.

 

 

  ONGOING RESEARCH  

  1. Kwon, M., J. Jung, H. Yu, and D. Lee, "HIDEEP: a systems approach to predict hormone impacts on drug efficacy based on effect paths." (Accepted in Scientific Reports)

  2. Yim, S., H. Yu, D Jang, and D. Lee, "Annotating activation/inhibition relationships of protein-protein interactions using gene ontology" (Accepted in APBC 2018/BMC Systems Biology)

  3. Jung, J., Y. Kang, H. Paik, M. Kwon, H. Yu, D. Jang and D. Lee, "Analysis of cancer cell growth associated gene expression signatures under chemical compound treated conditions for identifying therapeutic targets." (To be submitted at Nature Communications)

  4. Image-based drug effect prediction using deep learning [Lee, S*., Yu, H.*, J. Lee and D. Lee. In progress]

 

 

  PATENTS  

Lee, D., H. Yu and S. Choo, "Method for predicting drugs having opposite effects on disease genes in a directed network and apparatus thereof”, Application No. 1020160073969 (Korea).

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  SKILLS  

Handling biological databases:

KEGG pathways, BioGRID, TRANSFAC, CTD, DrugBank, HPA, GEO, GO, PhenoGO, HPA, PubMed, EndoNet, and Allen Brain Atlas

Network analysis:

Shortest path analysis, closest path analysis, Petri Net, and Random walk

Calling germline/somatic mutations and analyzing them:

samtools, GATK HaplotypeCaller, UnifiedGenotyper, MuTect, MosaicHunter, IGV, snpEff, and UCSC genome browser

Handling expression data:

GetGEO, Tophat, limma, and edgeR

Programming skills:

Proficient in R, python, and bash programming

Experiences with c, c++, MATLAB, and TensorFlow programming

Statistical analysis and machine learning:

Experiences with various statistical tests, regression, clustering algorithms, SVM, and deep learning

 

 

Last Update: Nov. 20, 2017

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