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IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology

作者： Pan, Youlian Zou, Jitao Huang, Yi Liu, Ziying Phan, Sieu Famili, Fazel A. Natl Res Council Canada Inst Informat Technol Knowledge Discovery Grp 1200 Montreal Rd Ottawa ON K1A 0R6 Canada Natl Res Council Canada Inst Plant Biotechnol Saskatoon SK S7N 0W9 Canada

ISBN: (纸本)9781424427567

microarray technology has been used extensively for high throughput gene expression studies. Many bioinformatics tools are available for analysis of microarray data. In the data mining process, it is important to be goal oriented so that a set of proper tools can be assembled for the targeted knowledge discovery process. In this paper, we tackle this issue by using a microarray dataset from Brassica endosperm together with EST data to validate our process. We were most interested in which genes are highly expressed in Brassica endosperm and their variations and functions over various stages in embryo development. We also performed gene characterization based on gene ontology analysis. Our results indicate that designing a specific data mining workflow that considers both the log ratio and signal intensity enhances knowledge discovery process. Through this approach, we were able to rind the regulatory relationship between two most important transcription factors, LEC1 and WRI1 in the endosperm of Brassica napus.

关键词： microarray data analysis gene expression differentially expressed genes transcriptional regulation

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Multi-Objective Evolutionary Algorithms based Interpretable Fuzzy Models for microarray Gene Expression data analysis

Multi-Objective Evolutionary Algorithms based Interpretable ...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Wang, Zhenyu Palade, Vasile Univ Oxford Comp Lab Oxford OX1 3QD England

ISBN: (纸本)9781424483075

We believe the great interpretability of fuzzy models allow fuzzy-based methods to play a very important role in microarray gene expression data analysis, but the advantages offered by fuzzy-based techniques in this application have not yet been fully explored in the literature. In this paper, we construct Multi-Objective Evolutionary Algorithms based Interpretable Fuzzy (MOEAIF) models for microarray gene expression data analysis. Our novel fuzzy models can significantly decrease the model complexity, and automatically balance the accuracy and interpretability of the models. The experimental studies have shown that relatively simple and small fuzzy rule bases, with satisfactory classification performance, have been successful found for challenging microarray gene expression datasets.

关键词： Cancer gene expression data fuzzy rule-based systems microarray data analysis model interpretability feature selection multi-objective optimisation evolutionary algorithms

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Genome expression pathway analysis tool - analysis and visualization of microarray gene expression data under genomic, proteomic and metabolic context

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BMC BIOINFORMATICS 2007年第1期8卷 179-179页

作者： Weniger, Markus Engelmann, Julia C. Schultz, Joerg Univ Wurzburg Bioctr Dept Bioinformat D-97074 Wurzburg Germany

Background: Regulation of gene expression is relevant to many areas of biology and medicine, in the study of treatments, diseases, and developmental stages. microarrays can be used to measure the expression level of thousands of mRNAs at the same time, allowing insight into or comparison of different cellular conditions. The data derived out of microarray experiments is highly dimensional and often noisy, and interpretation of the results can get intricate. Although programs for the statistical analysis of microarray data exist, most of them lack an integration of analysis results and biological interpretation. Results: We have developed GEPAT, Genome Expression Pathway analysis Tool, offering an analysis of gene expression data under genomic, proteomic and metabolic context. We provide an integration of statistical methods for data import and data analysis together with a biological interpretation for subsets of probes or single probes on the chip. GEPAT imports various types of oligonucleotide and cDNA array data formats. Different normalization methods can be applied to the data, afterwards data annotation is performed. After import, GEPAT offers various statistical data analysis methods, as hierarchical, k-means and PCA clustering, a linear model based t-test or chromosomal profile comparison. The results of the analysis can be interpreted by enrichment of biological terms, pathway analysis or interaction networks. Different biological databases are included, to give various information for each probe on the chip. GEPAT offers no linear work flow, but allows the usage of any subset of probes and samples as a start for a new data analysis. GEPAT relies on established data analysis packages, offers a modular approach for an easy extension, and can be run on a computer grid to allow a large number of users. It is freely available under the LGPL open source license for academic and commercial users at http://***. Conclusion: GEPAT is a modular, sca

关键词： Subset Selection microarray data analysis microarray Gene Expression data Gene Interaction Network Term Enrichment analysis

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Gene ARMADA: an integrated multi-analysis platform for microarray data implemented in MATLAB

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BMC BIOINFORMATICS 2009年第1期10卷 354-354页

作者： Chatziioannou, Aristotelis Moulos, Panagiotis Kolisis, Fragiskos N. Natl Hellen Res Fdn Inst Biol Res & Biotechnol Metab Engn & Bioinformat Grp Athens 11635 Greece

Background: The microarray data analysis realm is ever growing through the development of various tools, open source and commercial. However there is absence of predefined rational algorithmic analysis workflows or batch standardized processing to incorporate all steps, from raw data import up to the derivation of significantly differentially expressed gene lists. This absence obfuscates the analytical procedure and obstructs the massive comparative processing of genomic microarray datasets. Moreover, the solutions provided, heavily depend on the programming skills of the user, whereas in the case of GUI embedded solutions, they do not provide direct support of various raw image analysis formats or a versatile and simultaneously flexible combination of signal processing methods. Results: We describe here Gene ARMADA ( Automated Robust microarray data analysis), a MATLAB implemented platform with a Graphical User Interface. This suite integrates all steps of microarray data analysis including automated data import, noise correction and filtering, normalization, statistical selection of differentially expressed genes, clustering, classification and annotation. In its current version, Gene ARMADA fully supports 2 coloured cDNA and Affymetrix oligonucleotide arrays, plus custom arrays for which experimental details are given in tabular form ( Excel spreadsheet, comma separated values, tab-delimited text formats). It also supports the analysis of already processed results through its versatile import editor. Besides being fully automated, Gene ARMADA incorporates numerous functionalities of the Statistics and Bioinformatics Toolboxes of MATLAB. In addition, it provides numerous visualization and exploration tools plus customizable export data formats for seamless integration by other analysis tools or MATLAB, for further processing. Gene ARMADA requires MATLAB 7.4 (R2007a) or higher and is also distributed as a stand-alone application with MATLAB Component Runtime. Concl

关键词： microarray dataset microarray data analysis Affymetrix Array data analysis Structure Bioinformatics Toolbox

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maGUI: A Graphical User Interface for analysis and Annotation of DNA microarray data

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The Open Bioinformatics Journal 2019年 12卷 40-44页

作者： Dhammapal Bharne Praveen Kant Vaibhav Vindal Department of Biotechnology and Bioinformatics School of Life Sciences University of Hyderabad Hyderabad India .

Summary maGUI is a graphical user interface designed to analyze microarray data produced from experiments performed on various platforms such as Affymetrix, Agilent, Illumina, and Nimblegen and so on, automatically. It follows an integrated workflow for pre-processing and analysis of the microarray data. The user may proceed from loading of microarray data to normalization, quality check, filtering, differential gene expression, principal component analysis, clustering and classification. It also provides miscellaneous applications such as gene set test and enrichment analysis for identifying gene symbols using Bioconductor packages. Further, the user can build a co-expression network for differentially expressed genes. Tables and figures generated during the analysis can be viewed and exported to local disks. The graphical user interface is very friendly especially for the biologists to perform the most microarray data analyses and annotations without much need of learning R command line programming. Availability and Implementation maGUI is an R package which can be downloaded freely from Comprehensive R Archive Network resource. It can be installed in any R environment with version 3.0.2 or above.

关键词： Graphical user interface R programming language Bioconductor Comprehensive R Archive Network microarray data analysis Gene set test analysis Gene set enrichment analysis

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Using formal concept analysis for microarray data comparison

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Journal of bioinformatics and computational biology 2008年第1期6卷 65-75页

作者： V Choi Y Huang V Lam D Potter R Laubenbacher K Duca Department of Computer Science Virginia Tech 660 McBryde Hall Blacksburg VA 24061 USA. vchoi@cs.vt.edu

microarray technologies, which can measure tens of thousands of gene expression values simultaneously in a single experiment, have become a common research method for biomedical researchers. Computational tools to analyze microarray data for biological discovery are needed. In this paper, we investigate the feasibility of using formal concept analysis (FCA) as a tool for microarray data analysis. The method of FCA builds a (concept) lattice from the experimental data together with additional biological information. For microarray data, each vertex of the lattice corresponds to a subset of genes that are grouped together according to their expression values and some biological information related to gene function. The lattice structure of these gene sets might reflect biological relationships in the dataset. Similarities and differences between experiments can then be investigated by comparing their corresponding lattices according to various graph measures. We apply our method to microarray data derived from influenza-infected mouse lung tissue and healthy controls. Our preliminary results show the promise of our method as a tool for microarray data analysis.

关键词： microarray data analysis gene expression formal concept analysis Galois lattice concept lattice

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analysis of microarray expression data

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Genome Biology 2000年第1期1卷 1-4页

作者： Paul Kellam

Detailed descriptions of the design and information content of EBI's database ArrayExpress, which is currently under development but aims to be a public repository of gene expression data, are the main feature of ... 详细信息

关键词： Gene Expression data microarray Expression microarray data analysis microarray Expression data Public Repository

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Computational analysis of microarray data ofArabidopsis thalianachallenged withAlternaria brassicicolafor identification of key genes inBrassica

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JOURNAL OF GENETIC ENGINEERING AND BIOTECHNOLOGY 2020年第1期18卷 17-17页

作者： Pathak, Rajesh Kumar Baunthiyal, Mamta Pandey, Dinesh Kumar, Anil Govind Ballabh Pant Inst Engn & Technol Dept Biotechnol Pauri Garhwal 246194 Uttarakhand India GB Pant Univ Agr & Technol Dept Mol Biol & Genet Engn Coll Basic Sci & Humanities Pantnagar 263145 Uttarakhand India Rani Lakshmi Bai Cent Agr Univ Jhansi 284003 Uttar Pradesh India

Background Alternariablight, a recalcitrant disease caused byAlternaria brassicaeandAlternaria brassicicola, has been recognized for significant losses of oilseed crops especially rapeseed-mustard throughout the world. Till date, no resistance source is available against the disease;hence, plant breeding methods cannot be used to develop disease-resistant varieties. Therefore, in the present study, efforts have been made to identify resistance and defense-related genes as well as key components ofJA-SA-ET-mediated pathway involved in resistance againstAlternaria brasscicolathrough computational analysis of microarray data and network biology approach. microarray profiling data from wild type and mutantArabidopsisplants challenged withAlternaria brassicicolaalong with control plant were obtained from the Gene Expression Omnibus (GEO) database. The data analysis, including DEGs extraction, functional enrichment, annotation, and network analysis, was used to identify genes associated with disease resistance and defense response. Results A total of 2854 genes were differentially expressed in WT9C9;among them, 1327 genes were upregulated and 1527 genes were downregulated. A total of 1159 genes were differentially expressed in JAM9C9;among them, 809 were upregulated and 350 were downregulated. A total of 2516 genes were differentially expressed in SAM9C9;among them, 1355 were upregulated and 1161 were downregulated. A total of 1567 genes were differentially expressed in ETM9C9;among them, 917 were upregulated and 650 were downregulated. Besides, a total of 2965 genes were differentially expressed in contrast WT24C24;among them, 1510 genes were upregulated and 1455 genes were downregulated. A total of 4598 genes were differentially expressed in JAM24C24;among them, 2201 were upregulated and 2397 were downregulated. A total of 3803 genes were differentially expressed in SAM24C24;among them, 1819 were upregulated and 1984 were downregulated. A total of 4164 genes were differ

关键词： microarray data analysis GO analysis Network analysis Hubs Oilseeds Brassica

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New Algorithm for Gene Selection in microarray dataanalysis Workflow

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Computer Technology and Application 2012年第2期3卷 169-174页

作者： Ahmed Moussa Brigitte Vannier Mounir Maouene LTI Laboratory National School of Applied Sciences Abdelmalek Essaadi University IPBC University of Poitiers

This paper proposes the authors" algorithm for gene selection in microarray data analysis comparing conditions with replicates. Based on background noise computation in replicated arrays, this algorithm uses the global False Discovery Rate based on "Inter" and "Intra" group comparisons of replicates to select differential expressed gene sets. This method uses two statistic types that lead to improve the selection procedure when confronted to very high background noise. Using simulated and the well known Latin square datasets, the proposed method is compared to some well known algorithms.

关键词： microarray data analysis gene selection replicates false discovery rate inter and intra group comparisons

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Significance of gene ranking for classification of microarray samples

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IEEE-ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2006年第3期3卷 312-320页

作者： Zhang, Chaolin Lu, Xuesong Zhang, Xuegong SUNY Stony Brook Cold Spring Harbor Lab Stony Brook NY 11794 USA SUNY Stony Brook Dept Biomed Engn Stony Brook NY 11794 USA Tsinghua Univ MOE Key Lab Bioinformat Dept Automat Beijing 100084 Peoples R China

Many methods for classification and gene selection with microarray data have been developed. These methods usually give a ranking of genes. Evaluating the statistical significance of the gene ranking is important for understanding the results and for further biological investigations, but this question has not been well addressed for machine learning methods in existing works. Here, we address this problem by formulating it in the framework of hypothesis testing and propose a solution based on resampling. The proposed r-test methods convert gene ranking results into position p-values to evaluate the significance of genes. The methods are tested on three real microarray data sets and three simulation data sets with support vector machines as the method of classification and gene selection. The obtained position p-values help to determine the number of genes to be selected and enable scientists to analyze selection results by sophisticated multivariate methods under the same statistical inference paradigm as for simple hypothesis testing methods.

关键词： significance of gene ranking gene selection classification microarray data analysis

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