• Title, Summary, Keyword: microarray expression data

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Analysis and Subclass Classification of Microarray Gene Expression Data Using Computational Biology (전산생물학을 이용한 마이크로어레이의 유전자 발현 데이터 분석 및 유형 분류 기법)

  • Yoo, Chang-Kyoo;Lee, Min-Young;Kim, Young-Hwang;Lee, In-Beum
    • Journal of Institute of Control, Robotics and Systems
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    • v.11 no.10
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    • pp.830-836
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    • 2005
  • Application of microarray technologies which monitor simultaneously the expression pattern of thousands of individual genes in different biological systems results in a tremendous increase of the amount of available gene expression data and have provided new insights into gene expression during drug development, within disease processes, and across species. There is a great need of data mining methods allowing straightforward interpretation, visualization and analysis of the relevant information contained in gene expression profiles. Specially, classifying biological samples into known classes or phenotypes is an important practical application for microarray gene expression profiles. Gene expression profiles obtained from tissue samples of patients thus allowcancer classification. In this research, molecular classification of microarray gene expression data is applied for multi-class cancer using computational biology such gene selection, principal component analysis and fuzzy clustering. The proposed method was applied to microarray data from leukemia patients; specifically, it was used to interpret the gene expression pattern and analyze the leukemia subtype whose expression profiles correlated with four cases of acute leukemia gene expression. A basic understanding of the microarray data analysis is also introduced.

Cancer Genomics Object Model: An Object Model for Cancer Research Using Microarray

  • Park, Yu-Rang;Lee, Hye-Won;Cho, Sung-Bum;Kim, Ju-Han
    • Proceedings of the Korean Society for Bioinformatics Conference
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    • pp.29-34
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    • 2005
  • DNA microarray becomes a major tool for the investigation of global gene expression in all aspects of cancer and biomedical research. DNA microarray experiment generates enormous amounts of data and they are meaningful only in the context of a detailed description of microarrays, biomaterials, and conditions under which they were generated. MicroArray Gene Expression Data (MGED) society has established microarray standard for structured management of these diverse and large amount data. MGED MAGE-OM (MicroArray Gene Expression Object Model) is an object oriented data model, which attempts to define standard objects for gene expression. To assess the relevance of DNA microarray analysis of cancer research it is required to combine clinical and genomics data. MAGE-OM, however, does not have an appropriate structure to describe clinical information of cancer. For systematic integration of gene expression and clinical data, we create a new model, Cancer Genomics Object Model.

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Finding Informative Genes From Microarray Gene Expression Data Using FIGER-test

  • Choi, Kyoung-Oak;Chung, Hwan-Mook
    • Journal of the Korean Institute of Intelligent Systems
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    • v.17 no.5
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    • pp.707-711
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    • 2007
  • Microarray gene expression data is believed to show the functions of living organism through the gene expression values. We have studied a method to get the informative genes from the microarray gene expression data. There are several ways for this. In recent researches to get more sophisticated and detailed results, it has used the intelligence information theory like fuzzy theory. Some methods are to add fudge factors to the significance test for more refined results. In this paper, we suggest a method to get informative genes from microarray gene expression data. We combined the difference of means between two groups and the fuzzy membership degree which reflects the variance of the gene expression data. We have called our significance test the Fuzzy Information method for Gene Expression data(FIGER). The FIGER calculates FIGER variation ratio and FIGER membership degree to show how strongly each object belongs to the each group and then it results in the significance degree of each gene. The FIGER is focused on the variation and distribution of the data set to adjust the significance level. Out simulation shows that the FIGER-test is an effective and useful significance test.

Veri cation of Improving a Clustering Algorith for Microarray Data with Missing Values

  • Kim, Su-Young
    • The Korean Journal of Applied Statistics
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    • v.24 no.2
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    • pp.315-321
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    • 2011
  • Gene expression microarray data often include multiple missing values. Most gene expression analysis (including gene clustering analysis); however, require a complete data matric as an input. In ordinary clustering methods, just a single missing value makes one abandon the whole data of a gene even if the rest of data for that gene was intact. The quality of analysis may decrease seriously as the missing rate is increased. In the opposite aspect, the imputation of missing value may result in an artifact that reduces the reliability of the analysis. To clarify this contradiction in microarray clustering analysis, this paper compared the accuracy of clustering with and without imputation over several microarray data having different missing rates. This paper also tested the clustering efficiency of several imputation methods including our propose algorithm. The results showed it is worthwhile to check the clustering result in this alternative way without any imputed data for the imperfect microarray data.

Xperanto: A Web-Based Integrated System for DNA Microarray Data Management and Analysis

  • Park, Ji Yeon;Park, Yu Rang;Park, Chan Hee;Kim, Ji Hoon;Kim, Ju Ha
    • Genomics & Informatics
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    • v.3 no.1
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    • pp.39-42
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    • 2005
  • DNA microarray is a high-throughput biomedical technology that monitors gene expression for thousands of genes in parallel. The abundance and complexity of the gene expression data have given rise to a requirement for their systematic management and analysis to support many laboratories performing microarray research. On these demands, we developed Xperanto for integrated data management and analysis using user-friendly web-based interface. Xperanto provides an integrated environment for management and analysis by linking the computational tools and rich sources of biological annotation. With the growing needs of data sharing, it is designed to be compliant to MGED (Microarray Gene Expression Data) standards for microarray data annotation and exchange. Xperanto enables a fast and efficient management of vast amounts of data, and serves as a communication channel among multiple researchers within an emerging interdisciplinary field.

A modified partial least squares regression for the analysis of gene expression data with survival information

  • Lee, So-Yoon;Huh, Myung-Hoe;Park, Mira
    • Journal of the Korean Data and Information Science Society
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    • v.25 no.5
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    • pp.1151-1160
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    • 2014
  • In DNA microarray studies, the number of genes far exceeds the number of samples and the gene expression measures are highly correlated. Partial least squares regression (PLSR) is one of the popular methods for dimensional reduction and known to be useful for the classifications of microarray data by several studies. In this study, we suggest a modified version of the partial least squares regression to analyze gene expression data with survival information. The method is designed as a new gene selection method using PLSR with an iterative procedure of imputing censored survival time. Mean square error of prediction criterion is used to determine the dimension of the model. To visualize the data, plot for variables superimposed with samples are used. The method is applied to two microarray data sets, both containing survival time. The results show that the proposed method works well for interpreting gene expression microarray data.

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.

Enhancing Gene Expression Classification of Support Vector Machines with Generative Adversarial Networks

  • Huynh, Phuoc-Hai;Nguyen, Van Hoa;Do, Thanh-Nghi
    • Journal of information and communication convergence engineering
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    • v.17 no.1
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    • pp.14-20
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    • 2019
  • Currently, microarray gene expression data take advantage of the sufficient classification of cancers, which addresses the problems relating to cancer causes and treatment regimens. However, the sample size of gene expression data is often restricted, because the price of microarray technology on studies in humans is high. We propose enhancing the gene expression classification of support vector machines with generative adversarial networks (GAN-SVMs). A GAN that generates new data from original training datasets was implemented. The GAN was used in conjunction with nonlinear SVMs that efficiently classify gene expression data. Numerical test results on 20 low-sample-size and very high-dimensional microarray gene expression datasets from the Kent Ridge Biomedical and Array Expression repositories indicate that the model is more accurate than state-of-the-art classifying models.

Finding associations between genes by time-series microarray sequential patterns analysis

  • Nam, Ho-Jung;Lee, Do-Heon
    • Proceedings of the Korean Society for Bioinformatics Conference
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    • pp.161-164
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    • 2005
  • Data mining techniques can be applied to identify patterns of interest in the gene expression data. One goal in mining gene expression data is to determine how the expression of any particular gene might affect the expression of other genes. To find relationships between different genes, association rules have been applied to gene expression data set [1]. A notable limitation of association rule mining method is that only the association in a single profile experiment can be detected. It cannot be used to find rules across different condition profiles or different time point profile experiments. However, with the appearance of time-series microarray data, it became possible to analyze the temporal relationship between genes. In this paper, we analyze the time-series microarray gene expression data to extract the sequential patterns which are similar to the association rules between genes among different time points in the yeast cell cycle. The sequential patterns found in our work can catch the associations between different genes which express or repress at diverse time points. We have applied sequential pattern mining method to time-series microarray gene expression data and discovered a number of sequential patterns from two groups of genes (test, control) and more sequential patterns have been discovered from test group (same CO term group) than from the control group (different GO term group). This result can be a support for the potential of sequential patterns which is capable of catching the biologically meaningful association between genes.

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Metastasis Related Gene Exploration Using TwoStep Clustering for Medulloblastoma Microarray Data

  • Ban, Sung-Su;Park, Hee-Chang
    • 한국데이터정보과학회:학술대회논문집
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    • pp.153-159
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    • 2005
  • Microarray gene expression technology has applications that could refine diagnosis and therapeutic monitoring as well as improve disease prevention through risk assessment and early detection. Especially, microarray expression data can provide important information regarding specific genes related with metastasis through an appropriate analysis. Various methods for clustering analysis microarray data have been introduced so far. We used twostep clustering fot ascertain metastasis related gene through t-test. Through t-test between two groups for two publicly available medulloblastoma microarray data sets, we intended to find significant gene for metastasis. The paper describes the process in detail showing how the process is applied to clustering analysis and t-test for microarray datasets and how the metastasis-associated genes are explorated.

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