• Title/Summary/Keyword: Gene Expression

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Statistical bioinformatics for gene expression data

  • Lee, Jae-K.
    • Proceedings of the Korean Society for Bioinformatics Conference
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    • pp.103-127
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    • 2001
  • Gene expression studies require statistical experimental designs and validation before laboratory confirmation. Various clustering approaches, such as hierarchical, Kmeans, SOM are commonly used for unsupervised learning in gene expression data. Several classification methods, such as gene voting, SVM, or discriminant analysis are used for supervised lerning, where well-defined response classification is possible. Estimating gene-condition interaction effects require advanced, computationally-intensive statistical approaches.

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Statistical Tests for Time Course Microarray Experiments

  • Park, Tae-Seong;Lee, Seong-Gon;Choe, Ho-Sik;Lee, Seung-Yeon;Lee, Yong-Seong
    • Proceedings of the Korean Statistical Society Conference
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    • pp.85-90
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    • 2002
  • Microarray technology allows the monitoring of expression levels for thousands of genes simultaneously. In time-course experiments in which gene expression is monitored over time we are interested in testing gene expression profiles for different experimental groups. We propose a statistical test based on the ANOVA model to identify genes that have different gene expression profiles among experimental groups in time-course experiments. Using this test, we can detect genes that have different gene expression profiles among experimental groups. The proposed model is illustrated using cDNA microarrays of 3,840 genes obtained in an experiment to search for changes in gene expression profiles during neuronal differentiation of cortical stem cells.

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Construction of Gene Interaction Networks from Gene Expression Data Based on Evolutionary Computation (진화연산에 기반한 유전자 발현 데이터로부터의 유전자 상호작용 네트워크 구성)

  • Jung Sung Hoon;Cho Kwang-Hyun
    • Journal of Institute of Control, Robotics and Systems
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    • v.10 no.12
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    • pp.1189-1195
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    • 2004
  • This paper investigates construction of gene (interaction) networks from gene expression time-series data based on evolutionary computation. To illustrate the proposed approach in a comprehensive way, we first assume an artificial gene network and then compare it with the reconstructed network from the gene expression time-series data generated by the artificial network. Next, we employ real gene expression time-series data (Spellman's yeast data) to construct a gene network by applying the proposed approach. From these experiments, we find that the proposed approach can be used as a useful tool for discovering the structure of a gene network as well as the corresponding relations among genes. The constructed gene network can further provide biologists with information to generate/test new hypotheses and ultimately to unravel the gene functions.

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.

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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Gene Expression Signatures for Compound Response in Cancers

  • He, Ningning;Yoon, Suk-Joon
    • Genomics & Informatics
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    • v.9 no.4
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    • pp.173-180
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    • 2011
  • Recent trends in generating multiple, large-scale datasets provide new challenges to manipulating the relationship of different types of components, such as gene expression and drug response data. Integrative analysis of compound response and gene expression datasets generates an opportunity to capture the possible mechanism of compounds by using signature genes on diverse types of cancer cell lines. Here, we integrated datasets of compound response and gene expression profiles on NCI60 cell lines and constructed a network, revealing the relationship for 801 compounds and 341 gene probes. As examples, obtusol, which shows an exclusive sensitivity on a small number of colon cell lines, is related to a set of gene probes that have unique overexpression in colon cell lines. We also found that the SLC7A11 gene, a direct target of miR-26b, might be a key element in understanding the action of many diverse classes of anticancer compounds. We demonstrated that this network might be useful for studying the mechanisms of varied compound response on diverse cancer cell lines.

Deep learning for stage prediction in neuroblastoma using gene expression data

  • Park, Aron;Nam, Seungyoon
    • Genomics & Informatics
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    • v.17 no.3
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    • pp.30.1-30.4
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    • 2019
  • Neuroblastoma is a major cause of cancer death in early childhood, and its timely and correct diagnosis is critical. Gene expression datasets have recently been considered as a powerful tool for cancer diagnosis and subtype classification. However, no attempts have yet been made to apply deep learning using gene expression to neuroblastoma classification, although deep learning has been applied to cancer diagnosis using image data. Taking the International Neuroblastoma Staging System stages as multiple classes, we designed a deep neural network using the gene expression patterns and stages of neuroblastoma patients. Despite a small patient population (n = 280), stage 1 and 4 patients were well distinguished. If it is possible to replicate this approach in a larger population, deep learning could play an important role in neuroblastoma staging.

Differential gene expression by chrysotile in human bronchial epithelial cells

  • Seo, Yoo-Na;Lee, Yong-Jin;Lee, Mi-Young
    • Animal cells and systems
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    • v.16 no.2
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    • pp.95-103
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    • 2012
  • Asbestos exposure has been known to contribute to several lung diseases named asbestosis, malignant mesothelioma and lung cancer, but the disease-related molecular and cellular mechanisms are still largely unknown. To examine the effects of asbestos exposure in human bronchial epithelial cells at gene level, the global gene expression profile was analyzed following chrysotile treatment. The microarray results revealed differential gene expression in response to chrysotile treatment. The genes up- and down-regulated by chrysotile were mainly involved in processes including metabolism, signal transduction, transport, development, transcription, immune response, and other functions. The differential gene expression profiles could provide clues that might be used to understand the pathological mechanisms and therapeutic targets involved in chrysotile-related diseases.

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.

Gene Set and Pathway Analysis of Microarray Data (프마이크로어레이 데이터의 유전자 집합 및 대사 경로 분석)

  • Kim Seon-Young
    • KOGO NEWS
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    • v.6 no.1
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    • pp.29-33
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    • 2006
  • Gene set analysis is a new concept and method. to analyze and interpret microarray gene expression data and tries to extract biological meaning from gene expression data at gene set level rather than at gene level. Compared with methods which select a few tens or hundreds of genes before gene ontology and pathway analysis, gene set analysis identifies important gene ontology terms and pathways more consistently and performs well even in gene expression data sets with minimal or moderate gene expression changes. Moreover, gene set analysis is useful for comparing multiple gene expression data sets dealing with similar biological questions. This review briefly summarizes the rationale behind the gene set analysis and introduces several algorithms and tools now available for gene set analysis.

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