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    Share your research findings and results with us.

    Welcome experts and scholars in the fields of Bioinformatics from all over the world.

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    Submit your papers or abstracts to ICBRA 2019.

    You're welcome to submit research papers or abstracts for presentation and publication.

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    Extend communication and cooperation in Seoul.

    ICBRA 2019 which will be held in Seoul, South during December 19-21, 2019 provides platform for communication and cooperation.

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Keynote Speakers

Prof. Taesung Park
Seoul National University, South Korea

Prof. Taesung Park received his B.S. and M.S. degrees in Statistics from Seoul National University (SNU), Korea in 1984 and 1986, respectively and received his Ph.D. degree in Biostatistics from the University of Michigan in 1990. From Aug. 1991 to Aug. 1992, he worked as a visiting scientist at the NIH, USA. From Sep. 2002 to Aug. 2003, he was a visiting professor at the University of Pittsburgh. From Sep. 2009 to Aug. 2010, he was a visiting professor in Department of Biostatistics at the University of Washington. From Sep. 1999 to Sep. 2001, he worked as an associate professor in Department of Statistics at SNU. Since Oct. 2001 he worked as a professor and currently the Director of the Bioinformatics and Biostatistics Lab. at SNU. He served as the chair of the bioinformatics Program from Apr. 2005 to Mar. 2008, and the chair of Department of Statistics of SNU from Sep. 2007 and Aug. 2009. He has served editorial board members and associate editors for the international journals including Genetic Epidemiology, Computational Statistics and Data Analysis, Biometrical Journal, and International journal of Data Mining and Bioinformatics. His research areas include microarray data analysis, GWAS, gene-gene interaction analysis, and statistical genetics.

Speech Title: "Hierarchical Component Analysis for Microbiome Data Using Taxonomy Information"

Abstract: The recent advent of high-throughput sequencing technology has enabled us to study the associations between human microbiome and diseases. The DNA sequences of microbiome samples are clustered as operational taxonomic units (OTUs) according to their similarity. The OTU table containing counts of OTUs present in each sample is used to measure correlations between OTUs and disease status and find key microbes for prediction of the disease status. Various statistical methods have been proposed for such microbiome data analysis. However, none of these methods have used hierarchical structure of taxonomy information that is biologically meaningful. In this paper, we propose a hierarchical structural component model for microbiome data (HisCoM-microb) using taxonomy information as well as OTU table data. The proposed HisCoM-microb consists of two layers: one for OTUs grouped at the lowest taxonomy level and the other for OTUs grouped at the higher taxonomy level. Then we calculate simultaneously coefficient estimates of OTUs of all layers inserted in the hierarchical model. Through this analysis, we can infer the association between OTUs and disease status, considering the impact of taxonomic structure on disease status. Both simulation study and real microbiome data analysis show that our method provides a new testing approach for microbiome data which clearly reveal the relations between each taxon and disease status at the same time as finding the key microbiota of the disease.

Prof. Hans-Uwe Dahms
Kaohsiung Medical University, Taiwan

Dr. Hans-Uwe Dahms is a professor at Kaohsiung Medical University. He is interested in stress responses in general and within aquatic systems in particular. He, his colleagues and students integratively study pollution and the toxicology of stressors from physical, chemical, and biological sources. He is equally interested in climate change, the spread of diseases, antibiotic-resistance, food and drink safety from water sources, and integra-tive approaches in environmental and public health monitoring, risk assessment and management. He advised more than 25 Ph.D. students in their research and published more than 275 papers in scientific journals. He served as a reviewer for more than 70 SCI journals, as editorial board member of 12 reputed scientific journals, academic editor of PLosONE, and as editor in chief of FRONTIERS in Marine Pollution.

Speech Title: "Evaluation of In silico Toxicity Predictions"

Abstract: Chemoinformatics represents a search for chemical information resources where data are typically transformed into information and this into technologies that allow to make decisions better and faster. Such in silico approaches refer to computer applications or computer simulations. In silico approaches in pollution studies can best be understood as chemoinformatics using informational techniques applied to a range of problems in the field of chemistry related to toxicology and the effects of pollutants. To provide an example for the evaluation of in silico approaches, we will introduce to food safety issues related to food preservatives, plasticizers, and artificial sweeteners. For such assessments SMILES of the above food additives will be taken from the PubChem database. By using MarvinSketch all chemicals presented here are based on structural data retrieved from PubChem. In silico predictive models generally provide fast and economic screening tools for compound properties. They allow a high throughput and a constant optimization. They are less expensive, less time consuming, have a high reproducibility, and reduce experimental efforts. Computational approaches can also prioritize chemicals for their toxicological evaluation in order to reduce the amount of costly in vitro and ethically problematic in vivo toxicological screenings, and provide early alerts for newly developed substances. Limitations include that ADME aspects (absorption, distribution, metabolism, and excretion – which are basic pharmacokinetic descriptors) are not taken into account. There can be a lack of quality and transparency of the training set of experimental data. The programs, descriptors, and applicabilities are sometimes not clear. In addition are carcinogenicity predictions not possible based on non-genotoxic compounds.

Prof. Sun Kim
Seoul National University, South Korea

Sun Kim is Professor in the School of Computer Science and Engineering, Director of Bioinformatics Institute, and an affiliated faculty for the Interdisciplinary Program in Bioinformatics at Seoul National University. Before joining SNU, he was Chair of Faculty Division C; Director of Center for Bioinformatics Research, an Associate Professor in School of Informatics and Computing; and an Adjunct Associate Professor of Cellular and Integrative Physiology, Medical Sciences Program at Indiana University (IU) Bloomington. Prior to joining IU in 2001, he worked at DuPont Central Research from 1998 to 2001, and at the University of Illinois at Urbana-Champaign from 1997 to 1998. Sun Kim received B.S and M.S and Ph.D in Computer Science from Seoul National University, KAIST and the University of Iowa, respectively.

Speech Title: "Measuring Intra-Tumor Heterogeneity from Bulk Cell Sequencing"

Abstract: Intratumor heterogeneity (ITH) represents various phenotypic diversity among subclones that constitute a cancer tissue. ITH is now considered as an important clinical factor related to the aggressiveness, drug resistance, recurrence, and metastasis of cancer. Since cancer is a disease of the genome, the ITH level and cancer subclonal structure are inferred based on the genomic profile (e.g. somatic mutations and copy number variations). However, recent studies have suggested that the ITH can be identified at multi-omics level. Recently, our group developed ITH inference methods for methylome, transcriptome, and spliceome bulk-tumor sequencing data. The first method (Scientific Report 2016) that we developed was a transcriptomic ITH (tITH) model that measured entropy of biological network states. We developed another information theoretic method for measuring spliceomic ITH (sITH) in cancer cells, SpliceHetero (PLoS ONE 2019). Splicing patterns in cancer are very complicated, including wide spread retention of intron sequences in transcripts. The last one, PRISM (ISMB/Bioinformatics 2019), is a tool for inferring the composition of epigenetically distinct subclones of a tumor solely from methylation patterns obtained by reduced representation bisulfite sequencing.

Prof. Michael Greenacre
Universitat Pompeu Fabra, Spain

Michael Greenacre is Professor of Statistics at the Universitat Pompeu Fabra in Barcelona. His whole career has been devoted to research in multivariate analysis and he has written six books on correspondence analysis and data visualization and co-edited four more with Prof. Jörg Blasius (Bonn University). He has over 100 published articles in peer-reviewed journals and books, and has given short courses in 15 countries to statisticians, biologists and social scientists, in Europe, North and South America, Africa and Australia. For more than 30 years he has been working in projects related to Arctic ecology, based in north Norway. And for almost 20 years he has become interested in compositional data analysis, collaborating with biochemists, geochemists and recently with researchers in the analysis of microbiome data.

Speech Title: "The Analysis of High-Dimensional Microbiome Data: It's A Question of Coherence!"

Abstract: The standard structure of a microbiome data set is: (1) high-dimensional (hundreds or thousands of variables, in the form of operational taxonomics units, or OTUs); (2) relatively small sample (tens or hundreds); (3) basic data are counts of OTUs in each sampling unit; (4) many zeros (50-90% of the data set are zeros); and (5) the totals in each sampling unit are irrelevant, it is the relative counts that are important. To try to understand these data and extract some meaning from them, the problem might either be (a) to identify the OTUs that are driving the overall structure, which means equivalently removing those OTUs that can be considered random and uninteresting; (b) when there is some specific objective such as to explain a response variable or distinguish between pre-defined groups, to identify the OTUs that are relevant to this objective. In either case we have the challenge of variable selection. In this talk I will describe the approach to such data known as compositional data analysis. The basic principle of this approach is that the analytical procedure be subcompositionally coherent, which dictates that ratios of OTUs be used rather than the OTUs themselves. The problem with this approach is that zero values are not permitted, so there are various strategies to cope with this situation. One way is to replace the zeros by some small positive values, while a pragmatic solution is to use an alternative approach for which zeros need no replacement, while deviating as little as possible from the ideal requirement of subcompositional coherence.


Invited Speakers

Name: Chuhsing Kate Hsiao

Affiliation: National Taiwan University, Taiwan

Title: Network Analysis for Prioritizing Regulation Association of Hub Gene Nodes


Name: Tzu-Pin Lu

Affiliation: National Taiwan University, Taiwan

Title: A Novel Algorithm to Identify Regulating ceRNAs using the Integration of miRNA and Gene Expression Profiles


Name: Seungyoon Nam

Affiliation: Gachon University, South Korea

Title: Systems Biology in Early Drug Discovery


Name: Sungho Won

Affiliation: Seoul National University, South Korea

Title: Phylogenetic Tree-based Microbiome Association Test


Name: Yujin Chung

Affiliation: Kyonggi University, South Korea

Title: Inference of Isolation-with-Migration Models from Genomic Data


Name: Minsun Song

Affiliation: Sookmyung Women's University, South Korea

Titlle :  Goodness of Fit Test at Extreme of Disease Risk Distribution


Name: Wonil Chung

Affiliation: Soongsil University, South Korea  

Title: Efficient Penalized Regression Approaches Improve Polygenic Prediction in Biobank Data



Latest News

October 05, 2019


Welcome to join in the short course delivered by Professor Michael Greenacre on December 18-19, 2019.

September 30, 2019


With the request of many authors, the submission deadline for ICBRA 2019 has been extended to October 25.


July 25, 2019


Dr. Tae-Hyuk Ahn from Saint Louis University, USA and so on joined in ICBRA 2019 as Technical Committee.

Important Dates
Before October 25, 2019
On November 10, 2019
Before November 20, 2019


On December 19-21, 2019