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IEEE International Conference on bioinformatics and Biomedicine Workshops (BIBMW)

作者： Chang Bum Hong Young Jin Kim Sanghoon Moon Young-Ah Shin Yoon Shin Cho Jong-Young Lee Division of Structural and functional Genomics Center for Genome Science National Institute of Health Seoul South Korea Interdisciplinary Program in bioinformatics Seoul National University Seoul South Korea

International HapMap Project and the Human Genome Diversity Project (HGDP) provide plentiful resources on human genome information for the public. However, these kinds of information available from HapMap and HGDP are limited mainly due to the small size of samples in both databases. Recently we conducted genome-wide association study (GWAS) with 8,842 Korean subjects (Cho et al., Nature Genetics 2009). In an effort to build a publicly available browsing system for genome data resulted from our large scale GWAS, we developed the browser, namely Korea Association REsource (KARE) browser. This browser provides users with a large scale of single nucleotide polymorphisms (SNP) information comprising 1.5 million SNPs from population-based cohorts of 8,842 samples. KAREBrowser was based on the generic genome browser (GBrowse) that is a web-based application tool developed for users to navigate and visualize the genomic features and annotations in an interactive manner. All SNP information and related functions are available at the web site http://***/karebrowser/.

关键词： bioinformatics Genomics Databases Browsers Couplings Humans

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BioSMACK: A linux Live CD for analysis of genome-wide association

BioSMACK: A linux Live CD for analysis of genome-wide associ...

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IEEE International Conference on bioinformatics and Biomedicine Workshops (BIBMW)

作者： Chang Bum Hong Young Jin Kim Sanghoon Moon Young-Ah Shin Min Jin Go Jong-Young Lee Yoon Shin Cho Division of Structural and functional Genomics Center for Genome Science National Institute of Health Seoul South Korea Interdisciplinary Program in bioinformatics Seoul National University Seoul South Korea

Recent advancement of high-throughput genotyping technologies has enabled us to carry out a genome-wide association study (GWAS) in a large cohort. However, analyzing millions of single nucleotide polymorphisms (SNP) is still a troublesome task for researchers to encounter in conducting GWAS. Several difficulties in using analysis tools are one of the major challenges for researchers such as compatibility and dependencies during installation process of softwares. This is a huge obstacle to research institute without computing facilities and specialists. Thus proper research environment is an urgent demand for researchers working on GWAS. We developed BioSMACK to provide research environment for GWAS with no configuration and easy usage. BioSMACK is based on Ubuntu Live CD that offers complete linux-based operating system environment without installation. Moreover, we provide users with GWAS manual that is consist of series of guidelines for GWAS and useful examples. BioSMACK is freely available at http://***/biosmack.

关键词： bioinformatics Genomics Linux Metals Operating systems Manuals

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cuGWAM: Genome-wide association multifactor dimensionality reduction using CUDA-enabled high-performance graphics processing unit

cuGWAM: Genome-wide association multifactor dimensionality r...

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IEEE International Conference on bioinformatics and Biomedicine Workshops (BIBMW)

作者： Min-Seok Kwon Kyunga Kim Sungyoung Lee Taesung Park Interdisciplinary program in Bioinformatics Seoul National University South Korea Department of Statistics Seoul National University South Korea Department of statistics Seoul National University South Korea

Multifactor dimensionality reduction (MDR) method has been widely applied to detect gene-gene interactions that are well recognized as playing an important role in understanding complex traits, such as disease susceptibility. However, because of an exhaustive analysis of MDR, the current MDR software has some limitations to be extended to the genome-wide association studies (GWAS) with a large number of genetic markers up to ~1 million. To overcome this computational problem, we developed CUDA based genome-wide association MDR (cuGWAM) software using efficient hardware accelerators. Not only cuGWAM has better performance than CPU-based MDR methods (original MDR and parallel MDR) and GPU-based other methods (MDRGPU), but also initial construction cost is also less expensive. Furthermore, cuGWAM provides various performance measures to evaluate MDR classifiers, including tau-b, likelihood ratio, normalized mutual information as well as balanced accuracy. Also, cuGWAM provided three methods for handling missing genotypes: complete, available and missing category. Executable cuGWAM are freely available at http://***/cugwam for system with CUDA-enabled GPU devices.

关键词： Graphics processing unit bioinformatics Genomics Performance evaluation Classification algorithms Graphics Instruction sets

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Genome-wide analysis of haplotype interaction for the data from the North American Rheumatoid Arthritis Consortium

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BMC proceedings 2009年第7期3 Suppl 7卷 S34页

作者： Jungsun Park Junghyun Namkung Mina Jhun Taesung Park Interdisciplinary Program in Bioinformatics Seoul National University San56-l Shin Lim-Dong Kwan Ak-Ku Seoul 151-747 Republic of Korea. pj518@snu.ac.kr.

Recent genome-wide association studies on several complex diseases have focused on individual single-nucleotide polymorphism (SNP) analysis; however, not many studies have reported interactions among genes perhaps because the gene-gene and gene-environment interaction analysis could be infeasible due to heavy computing requirements. In this study we propose a new strategy for exploring the interactions among haplotypes. The proposed method consists of two steps. Step 1 tests the single-SNP association of whole genome with multiple testing corrections and finds the haplotype blocks of the significant SNPs. Step 2 performs interaction analysis of haplotypes within blocks. Our proposed method is applied to the rheumatoid arthritis data for Genetic Analysis Workshop 16.

关键词： Haplotype Block Significant SNPs Codominant Model North American Rheumatoid Arthritis Consortium Haplotype Association Analysis

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AnomiGAN: Generative adversarial networks for anonymizing private medical data

arXiv

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arXiv 2019年

作者： Bae, Ho Jung, Dahuin Yoon, Sungroh Interdisciplinary Program in Bioinformatics Seoul National University Seoul08826 Electrical and Computer Engineering Seoul National University Seoul08826 Biological Sciences Seoul National University Seoul08826

Typical personal medical data contains sensitive information about individuals. Storing or sharing the personal medical data is thus often risky. For example, a short DNA sequence can provide information that can not only identify an individual, but also his or her relatives. Nonetheless, most countries and researchers agree on the necessity of collecting personal medical data. This stems from the fact that medical data, including genomic data, are an indispensable resource for further research and development regarding disease prevention and treatment. To prevent personal medical data from being misused, techniques to reliably preserve sensitive information should be developed for real world application. In this paper, we propose a framework called anonymized generative adversarial networks (AnomiGAN), to improve the maintenance of privacy of personal medical data, while also maintaining high prediction performance. We compared our method to state-of-the-art techniques and observed that our method preserves the same level of privacy as differential privacy (DP), but had better prediction results. We also observed that there is a trade-off between privacy and performance results depending on the degree of preservation of the original data. Here, we provide a mathematical overview of our proposed model and demonstrate its validation using UCI machine learning repository datasets in order to highlight its utility in practice. Experimentally, our approach delivers a better performance compared to that of the DP approach. Availability: Implementation is available at https://***/hobae/AnomiGAN/. Copyright © 2019, The Authors. All rights reserved.

关键词： Generative adversarial networks

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S100A4 released from highly bone-metastatic breast cancer cells plays a critical role in osteolysis

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Bone Research 2019年第4期7卷 424-436页

作者： Haemin Kim Bongjun Kim Sang Il Kim Hyung Joon Kim Brian YRyu Junho Chung Zang Hee Lee Hong-Hee Kim Department of Cell and Developmental Biology BK21 Program and DRISeoul National UniversitySeoulKorea Department of Biochemistry and Molecular Biology Seoul National University College of MedicineSeoulKorea Cancer Research Institute Seoul National University College of MedicineSeoulKorea Department of Cancer Biology Seoul National University College of MedicineSeoulKorea Department of Oral Physiology BK21 PLUS Projectand Dental and Life Science InstituteSchool of DentistryPusan National UniversityYangsanKorea Interdisciplinary Program in Bioinformatics Seoul National University College of Natural SciencesSeoulKorea Department of Biomedical Sciences Seoul National University Graduate SchoolSeoulKorea

Bone destruction induced by breast cancer metastasis causes severe complications,including death,in breast cancer *** between cancer cells and skeletal cells in metastatic bone microenvironments is a principal element that drives tumor progression and ***-derived factors play fundamental roles in this form of *** identify soluble factors released from cancer cells in bone metastasis,we established a highly bone-metastatic subline of MDA-MB-231 breast cancer *** subline(mtMDA)showed a markedly elevated ability to secrete S100A4 protein,which directly stimulated osteoclast formation via surface receptor *** S100A4 stimulated osteoclastogenesis in vitro and bone loss in *** medium from mtMDA cells in which S100A4 was knocked down had a reduced ability to stimulate ***,the S100A4 knockdown cells elicited less bone destruction in mice than the control knockdown *** addition,administration of an anti-S100A4 monoclonal antibody(mAb)that we developed attenuated the stimulation of osteoclastogenesis and bone loss by mtMDA in *** together,our results suggest that S100A4 released from breast cancer cells is an important player in the osteolysis caused by breast cancer bone metastasis.

关键词： S100A4 breast metastatic

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A probabilistic model for pathway-guided gene set selection

A probabilistic model for pathway-guided gene set selection

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2021 IEEE International Conference on bioinformatics and Biomedicine, BIBM 2021

作者： Kim, Inyoung Lee, Sangseon Kim, Youngkuk Namkoong, Hugh Kim, Sun Artificial Intelligence Institute Seoul National University Seoul Korea Republic of Institute of Computer Technology Seoul National University Seoul Korea Republic of Seoul National University Department of Computer Science and Engineering Seoul Korea Republic of Institute of Engineering Research Seoul National University Seoul Korea Republic of Interdisciplinary Program in Bioinformatics Seoul National University Seoul Korea Republic of

ISBN: (纸本)9781665401265

Breast cancer is classified into five intrinsic subtypes, with differing treatment methods and prognoses. Therefore, accurate identification of subtypes from patient transcriptome data is essential. Many gene signatures, including PAM50, have been developed to classify breast cancer subtypes. However, existing gene selection methods do not utilize biological pathways. Gene signature selection using biological pathways can explain signature genes in terms of biological functions. Thus, we propose a probabilistic model for pathway-guided gene set selection using gene expression data. First, we defined gene and pathway factors based on gene expression and pathway activation levels, and calculated the posterior probability. Second, we adopted the prediction strength to guide gene set selection. Third, the gene set was selected using the posterior probability and prediction strength values. Finally, on evaluating the selected gene set, it was experimentally confirmed that our gene set performed better on classification tasks than the PAM50 gene set, a gene set produced by the XGBoost classifier, and a random gene set. Among the genes selected by our method, it was confirmed that the genes included in the cell cycle and circadian rhythm pathways showed different expression patterns for each breast cancer subtype. Our selected gene set exhibited biological significance in terms of pathway activation. © 2021 IEEE.

关键词： Gene expression

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Exploring the mutation tendency of a specific region in the virus taxonomic groups using data mining techniques

Exploring the mutation tendency of a specific region in the ...

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2022 IEEE International Conference on Big Data, Big Data 2022

作者： Je, Mikyung Cho, Myeongji Kim, Yoon Jung Son, Hyeon S. Seoul National University Interdisciplinary Graduate Program in Bioinformatics College of Natural Science Seoul Korea Republic of Honam National Institute of Biological Resources Division of Bio It Mokpo Korea Republic of Seoul National University Lab. of Computational Virology and Viroinformatics Graduate School of Public Health Seoul Korea Republic of Seoul National University Institute of Health and Environment Seoul Korea Republic of

ISBN: (纸本)9781665480451

The GDNQ motif is mainly found in nonsegmented (-)ssRNA viruses and plays a role in the catalytic and polymerase activity pathways. However, in some recently reported dsRNA mycoviruses, the GDNQ motif of the (-)ssRNA virus group is found instead of the GDD motif commonly found in the dsRNA virus group. Therefore, in this study, data mining techniques were used to explore the mutation tendency of a specific region in the virus taxonomic groups. Viral protein sequences were obtained from the NCBI Virus data repository, and the performance of several known string-searching algorithms (BF, KMP, and BM) was evaluated to establish an optimal strategy for extracting useful information from sequence big data. Then, the pattern-matching performance of the algorithm was tested to detect whether the conserved region sequence was a common motif type or a mutated motif type. As a result of the analysis, it was confirmed that the BF method produced the fastest results. However, the BM method recorded higher accuracy. Through the virus mutation tendency derived from the analysis results, it was confirmed that 7.1% of dsRNA viruses had a mutated motif type based on the total data analyzed and that it was relatively recent that ssRNA virus motif variants began to be discovered in dsRNA viruses. It is expected that additional in-depth analysis of the host range and infection route of viruses with the mutation will help understand the evolution of dsRNA and ssRNA mycoviruses. © 2022 IEEE.

关键词： Big data

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Enhancing Label Consistency on Document-level Named Entity Recognition

arXiv

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arXiv 2022年

作者： Jeong, Minbyul Kang, Jaewoo Department of Computer Science and Engineering Korea University Seoul02841 Korea Republic of Interdisciplinary Graduate Program in Bioinformatics Korea University Seoul Korea Republic of AIGEN Sciences Seoul04778 Korea Republic of

Summary: Named entity recognition (NER) is a fundamental part of extracting information from documents in biomedical applications. A notable advantage of NER is its consistency in extracting biomedical entities in a document context. Although existing document NER models show consistent predictions, they still do not meet our expectations. We investigated whether the adjectives and prepositions within an entity cause a low label consistency, which results in inconsistent predictions. In this paper, we present our method, ConNER, which enhances the label dependency of modifiers (e.g., adjectives and prepositions) to achieve higher label agreement. ConNER refines the draft labels of the modifiers to improve the output representations of biomedical entities. The effectiveness of our method is demonstrated on four popular biomedical NER datasets;in particular, its efficacy is proved on two datasets with 7.5–8.6% absolute improvements in the F1 score. We interpret that our ConNER method is effective on datasets that have intrinsically low label consistency. In the qualitative analysis, we demonstrate how our approach makes the NER model generate consistent predictions. © 2022, CC BY-SA.

关键词： Forecasting

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Two-way interaction analysis of obesity trait from Korean population using generalized MDR

Two-way interaction analysis of obesity trait from Korean po...

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IEEE International Conference on bioinformatics and Biomedicine Workshops (BIBMW)

作者： Sungyoung Lee Sohee Oh Min-Seok Kwon Seungyeoun Lee Taesung Park Interdisciplinary Program in Bioinformatics Seoul National University Seoul South Korea Department of Statistics Seoul National University Seoul South Korea Department of Applied Mathematics Sejong University Seoul South Korea

Most common complex traits such as obesity, hypertension, diabetes, and cancers are known to be associated with multiple genes, environmental factors, and epistasis. Recently, the development of advanced genotyping technologies allows us to perform the genome-wide association studies (GWAS). For detecting the effects of multiple genes on complex traits, many approaches have been proposed for GWAS. Multifactor dimensionality reduction (MDR) proposed by Ritchie et al. (2001) is one of the powerful methods for detecting epistasis, which detects high order interactions among genes. However, MDR is computationally intensive due to its requirement of testing all possible n-way combinations. Thus, it is not practically feasible to apply MDR directly to analyzing large-scale GWAS data. We propose an efficient strategy to perform MDR analysis for GWAS data. First, select a subset of genetic factors such as SNPs with some marginal significance. Second, perform MDR analysis only for the selected genetic factors. Third, provide MDR summary results through network graph. Even for the MDR analysis for the selected SNPs, a fast computing system is required. We developed our own GPU based system for the large-scale GWAS data. We applied our strategy to Korean Association REsource (KARE) (8,838 individuals with 101,837 SNPs) for detecting two-way interactions of genetic factors associated with body mass index (BMI). We identified several genes and pathways which have been known to be associated with obesity.

关键词： Obesity bioinformatics Genomics Graphics processing unit Diabetes

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