• Title, Summary, Keyword: pathway analysis

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HisCoM-PCA: software for hierarchical structural component analysis for pathway analysis based using principal component analysis

  • Jiang, Nan;Lee, Sungyoung;Park, Taesung
    • Genomics & Informatics
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    • v.18 no.1
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    • pp.11.1-11.3
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    • 2020
  • In genome-wide association studies, pathway-based analysis has been widely performed to enhance interpretation of single-nucleotide polymorphism association results. We proposed a novel method of hierarchical structural component model (HisCoM) for pathway analysis of common variants (HisCoM for pathway analysis of common variants [HisCoM-PCA]) which was used to identify pathways associated with traits. HisCoM-PCA is based on principal component analysis (PCA) for dimensional reduction of single nucleotide polymorphisms in each gene, and the HisCoM for pathway analysis. In this study, we developed a HisCoM-PCA software for the hierarchical pathway analysis of common variants. HisCoM-PCA software has several features. Various principle component scores selection criteria in PCA step can be specified by users who want to summarize common variants at each gene-level by different threshold values. In addition, multiple public pathway databases and customized pathway information can be used to perform pathway analysis. We expect that HisCoM-PCA software will be useful for users to perform powerful pathway analysis.

Iterative integrated imputation for missing data and pathway models with applications to breast cancer subtypes

  • Linder, Henry;Zhang, Yuping
    • Communications for Statistical Applications and Methods
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    • v.26 no.4
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    • pp.411-430
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    • 2019
  • Tumor development is driven by complex combinations of biological elements. Recent advances suggest that molecularly distinct subtypes of breast cancers may respond differently to pathway-targeted therapies. Thus, it is important to dissect pathway disturbances by integrating multiple molecular profiles, such as genetic, genomic and epigenomic data. However, missing data are often present in the -omic profiles of interest. Motivated by genomic data integration and imputation, we present a new statistical framework for pathway significance analysis. Specifically, we develop a new strategy for imputation of missing data in large-scale genomic studies, which adapts low-rank, structured matrix completion. Our iterative strategy enables us to impute missing data in complex configurations across multiple data platforms. In turn, we perform large-scale pathway analysis integrating gene expression, copy number, and methylation data. The advantages of the proposed statistical framework are demonstrated through simulations and real applications to breast cancer subtypes. We demonstrate superior power to identify pathway disturbances, compared with other imputation strategies. We also identify differential pathway activity across different breast tumor subtypes.

Integration of a Large-Scale Genetic Analysis Workbench Increases the Accessibility of a High-Performance Pathway-Based Analysis Method

  • Lee, Sungyoung;Park, Taesung
    • Genomics & Informatics
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    • v.16 no.4
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    • pp.39.1-39.3
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    • 2018
  • The rapid increase in genetic dataset volume has demanded extensive adoption of biological knowledge to reduce the computational complexity, and the biological pathway is one well-known source of such knowledge. In this regard, we have introduced a novel statistical method that enables the pathway-based association study of large-scale genetic dataset-namely, PHARAOH. However, researcher-level application of the PHARAOH method has been limited by a lack of generally used file formats and the absence of various quality control options that are essential to practical analysis. In order to overcome these limitations, we introduce our integration of the PHARAOH method into our recently developed all-in-one workbench. The proposed new PHARAOH program not only supports various de facto standard genetic data formats but also provides many quality control measures and filters based on those measures. We expect that our updated PHARAOH provides advanced accessibility of the pathway-level analysis of large-scale genetic datasets to researchers.

Studies on Gene Expression of Yukmijihwang-tang using High-throughput Gene Expression Analysis Techniques (대규모 유전자 분석 기법을 이용한 육미지황원의 유전자 발현 연구)

  • Kang, Bong-Joo;Kim, Yun-Taik;Cho, Dong-Wuk
    • Korean Journal of Oriental Medicine
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    • v.8 no.2
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    • pp.95-107
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    • 2002
  • Yukmijihwang-tang(YM) is a noted herbal prescription in Chinese and Korean traditional medicines, and it has been known to reinforce the vital essence and has been widely used for a variety of disease such as stroke, osteoporosis, anti-tumor, and hypothyrodism. Regarding its traditional use, YM has been known to reinforce the Yin (vital essence) of liver and kidney. Also it has been known to reinforce nutrition and biological function in brain. Recently, studies suggested that YM increase antioxidant activities and exert the protective effect against oxidant-induced liver cell injury. We investigated the high-throughput gene expression analysis on the Yukmijihwang-tang administrated in SD rats. Microarray data were validated on a limited number of genes by semiquantitative RT-PCR and Western blot analyses. The recent availability of microarrays provides an attractive strategy for elaborating an unbiased molecular profile of large number of genes in drug discovery This experimental approach offers the potential to identify molecules or cellular pathways not previously associated with herbal medicine. Total RNA from normal control brain and Yukmijihwang-tang administrated brain were hybridized to microarrays containing 10,000 rat genes. The 52 genes were found to be up-regulated(twice or more) excluding EST gene. The nine genes were found to be down-regulated(twice or more) excluding EST gene. Gene array technology was used to identify for the first time many genes expression pathway analysis that arecell cycle pathway, apoptosis pathway, electron transport chain pathway, cytoplasmic ribosomal protein pathway, fatty acid degradation pathway, and TGF-beta signaling pathway. These differentially expressed genes pathway analysis have not previously been iavestigated in the context of herbal medicine efficacy and represent novel factors for further study of the mechanism of herbal medicine efficacy.

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Pathway Retrieval for Transcriptome Analysis using Fuzzy Filtering Technique andWeb Service

  • Lee, Kyung-Mi;Lee, Keon-Myung
    • International Journal of Fuzzy Logic and Intelligent Systems
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    • v.12 no.2
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    • pp.167-172
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    • 2012
  • In biology the advent of the high-throughput technology for sequencing, probing, or screening has produced huge volume of data which could not be manually handled. Biologists have resorted to software tools in order to effectively handle them. This paper introduces a bioinformatics tool to help biologists find potentially interesting pathway maps from a transcriptome data set in which the expression levels of genes are described for both case and control samples. The tool accepts a transcriptome data set, and then selects and categorizes some of genes into four classes using a fuzzy filtering technique where classes are defined by membership functions. It collects and edits the pathway maps related to those selected genes without analyst' intervention. It invokes a sequence of web service functions from KEGG, which an online pathway database system, in order to retrieve related information, locate pathway maps, and manipulate them. It maintains all retrieved pathway maps in a local database and presents them to the analysts with graphical user interface. The tool has been successfully used in identifying target genes for further analysis in transcriptome study of human cytomegalovirous. The tool is very helpful in that it can considerably save analysts' time and efforts by collecting and presenting the pathway maps that contain some interesting genes, once a transcriptome data set is just given.

Dynamical Analysis of Cellular Signal Transduction Pathways with Nonlinear Systems Perspectives (비선형시스템 관점으로부터 세포 신호전달경로의 동역학 분석)

  • Kim Hyun-Woo;Cho Kwang-Hyun
    • Journal of Institute of Control, Robotics and Systems
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    • v.10 no.12
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    • pp.1155-1163
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    • 2004
  • Extracellular signal-regulated kinase (ERK) signaling pathway is one of the mitogen-activated protein kinase (MAPK) signal transduction pathways. This pathway is known as pivotal in many signaling networks that govern proliferation, differentiation and cell survival. The ERK signaling pathway comprises positive and negative feedback loops, depending on whether the terminal kinase stimulates or inhibits the activation of the initial level. In this paper, we attempt to model the ERK pathway by considering both of the positive and negative feedback mechanisms based on Michaelis-Menten kinetics. In addition, we propose a fraction ratio model based on the mass action law. We first develop a mathematical model of the ERK pathway with fraction ratios. Secondly, we analyze the dynamical properties of the fraction ratio model based on simulation studies. Furthermore, we propose a concept of an inhibitor, catalyst, and substrate (ICS) controller which regulates the inhibitor, catalyst, and substrate concentrations of the ERK signal transduction pathway. The ICS controller can be designed through dynamical analysis of the ERK signaling transduction pathway within limited concentration ranges.

Biological Pathway Extension Using Microarray Gene Expression Data

  • Chung, Tae-Su;Kim, Ji-Hun;Kim, Kee-Won;Kim, Ju-Han
    • Genomics & Informatics
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    • v.6 no.4
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    • pp.202-209
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    • 2008
  • Biological pathways are known as collections of knowledge of certain biological processes. Although knowledge about a pathway is quite significant to further analysis, it covers only tiny portion of genes that exists. In this paper, we suggest a model to extend each individual pathway using a microarray expression data based on the known knowledge about the pathway. We take the Rosetta compendium dataset to extend pathways of Saccharomyces cerevisiae obtained from KEGG (Kyoto Encyclopedia of genes and genomes) database. Before applying our model, we verify the underlying assumption that microarray data reflect the interactive knowledge from pathway, and we evaluate our scoring system by introducing performance function. In the last step, we validate proposed candidates with the help of another type of biological information. We introduced a pathway extending model using its intrinsic structure and microarray expression data. The model provides the suitable candidate genes for each single biological pathway to extend it.

Development of Multidimensional Analysis System for Bio-pathways (바이오 패스웨이 다차원 분석 시스템 개발)

  • Seo, Dongmin;Choi, Yunsoo;Jeon, Sun-Hee;Lee, Min-Ho
    • The Journal of the Korea Contents Association
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    • v.14 no.11
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    • pp.467-475
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    • 2014
  • With the development of genomics, wearable device and IT/NT, a vast amount of bio-medical data are generated recently. Also, healthcare industries based on big-data are booming and big-data technology based on bio-medical data is rising rapidly as a core technology for improving the national health and aged society. A pathway is the biological deep knowledge that represents the relations of dynamics and interaction among proteins, genes and cells by a network. A pathway is wildly being used as an important part of a bio-medical big-data analysis. However, a pathway analysis requires a lot of time and effort because a pathway is very diverse and high volume. Also, multidimensional analysis systems for various pathways are nonexistent even now. In this paper, we proposed a pathway analysis system that collects user interest pathways from KEGG pathway database that supports the most widely used pathways, constructs a network based on a hierarchy structure of pathways and analyzes the relations of dynamics and interaction among pathways by clustering and selecting core pathways from the network. Finally, to verify the superiority of our pathway analysis system, we evaluate the performance of our system in various experiments.

HisCoM-PAGE: software for hierarchical structural component models for pathway analysis of gene expression data

  • Mok, Lydia;Park, Taesung
    • Genomics & Informatics
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    • v.17 no.4
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    • pp.45.1-45.3
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    • 2019
  • To identify pathways associated with survival phenotypes using gene expression data, we recently proposed the hierarchical structural component model for pathway analysis of gene expression data (HisCoM-PAGE) method. The HisCoM-PAGE software can consider hierarchical structural relationships between genes and pathways and analyze multiple pathways simultaneously. It can be applied to various types of gene expression data, such as microarray data or RNA sequencing data. We expect that the HisCoM-PAGE software will make our method more easily accessible to researchers who want to perform pathway analysis for survival times.

Pathway Analysis of Metabolic Syndrome Using a Genome-Wide Association Study of Korea Associated Resource (KARE) Cohorts

  • Shim, Unjin;Kim, Han-Na;Sung, Yeon-Ah;Kim, Hyung-Lae
    • Genomics & Informatics
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    • v.12 no.4
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    • pp.195-202
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    • 2014
  • Metabolic syndrome (MetS) is a complex disorder related to insulin resistance, obesity, and inflammation. Genetic and environmental factors also contribute to the development of MetS, and through genome-wide association studies (GWASs), important susceptibility loci have been identified. However, GWASs focus more on individual single-nucleotide polymorphisms (SNPs), explaining only a small portion of genetic heritability. To overcome this limitation, pathway analyses are being applied to GWAS datasets. The aim of this study is to elucidate the biological pathways involved in the pathogenesis of MetS through pathway analysis. Cohort data from the Korea Associated Resource (KARE) was used for analysis, which include 8,842 individuals (age, $52.2{\pm}8.9years$ ; body mass index, $24.6{\pm}3.2kg/m^2$). A total of 312,121 autosomal SNPs were obtained after quality control. Pathway analysis was conducted using Meta-analysis Gene-Set Enrichment of Variant Associations (MAGENTA) to discover the biological pathways associated with MetS. In the discovery phase, SNPs from chromosome 12, including rs11066280, rs2074356, and rs12229654, were associated with MetS (p < $5{\times}10^{-6}$), and rs11066280 satisfied the Bonferroni-corrected cutoff (unadjusted p < $1.38{\times}10^{-7}$, Bonferroni-adjusted p < 0.05). Through pathway analysis, biological pathways, including electron carrier activity, signaling by platelet-derived growth factor (PDGF), the mitogen-activated protein kinase kinase kinase cascade, PDGF binding, peroxisome proliferator-activated receptor (PPAR) signaling, and DNA repair, were associated with MetS. Through pathway analysis of MetS, pathways related with PDGF, mitogen-activated protein kinase, and PPAR signaling, as well as nucleic acid binding, protein secretion, and DNA repair, were identified. Further studies will be needed to clarify the genetic pathogenesis leading to MetS.