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gene expression data analysis of human lymphoma using support vector machines and output coding ensembles

ARTIFICIAL INTELLIGENCE IN MEDICINE 2002年第3期26卷 281-304页

作者： Valentini, G Univ Genoa Dipartimento Informat & Sci Informazione I-16146 Genoa Italy INFM I-16146 Genoa Italy

The large amount of data generated by DNA microarrays was originally analysed using unsupervised methods, such as clustering or self-organizing maps. Recently supervised methods such as decision trees, dot-product support vector machines (SVM) and multi-layer perceptrons (MLP) have been applied in order to classify normal and tumoural tissues. We propose methods based on non-linear SVM with polynomial and Gaussian kernels, and output coding (OC) ensembles of learning machines to separate normal from malignant tissues, to classify different types of lymphoma and to analyse the role of sets of coordinately expressed genes in carcinogenic processes of lymphoid tissues. Using gene expression data from "Lymphochip", a specialised DNA microarray developed at Stanford University School of Medicine, we show that SVM can correctly separate normal from tumoural tissues, and OC ensembles can be successfully used to classify different types of lymphoma. Moreover, we identify a group of coordinately expressed genes related to the separation of two distinct subgroups inside diffuse large B-cell lymphoma (DLBCL), validating a previous Alizadeh's hypothesis about the existence of two distinct diseases inside DLBCL. (C) 2002 Elsevier Science B.V. All rights reserved.

关键词： gene expression data analysis output coding ensembles of learning machines support vector machines DNA microarrays

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Exploring ant-based algorithms for gene expression data analysis

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ARTIFICIAL INTELLIGENCE IN MEDICINE 2009年第2期47卷 105-119页

作者： He, Yulan Hui, Siu Cheung Open Univ Knowledge Media Inst Milton Keynes MK7 6AA Bucks England Nanyang Technol Univ Sch Comp Engn Singapore 639798 Singapore

Objective: Recently, much research has been proposed using nature inspired algorithms to perform complex machine learning tasks. Ant colony optimization (ACO) is one such algorithm based on swarm intelligence and is derived from a model inspired by the collective foraging behavior of ants. Taking advantage of the ACO in traits such as self-organization and robustness, this paper investigates ant-based algorithms for gene expression data clustering and associative classification. Methods and material: An ant-based clustering (Ant-C) and an ant-based association rule mining (Ant-ARM) algorithms are proposed for gene expression data analysis. The proposed algorithms make use of the natural behavior of ants such as cooperation and adaptation to allow for a flexible robust search for a good candidate solution. Results: Ant-C has been tested on the three datasets selected from the Stanford Genomic Resource database and achieved relatively high accuracy compared to other classical clustering methods. Ant-ARM has been tested on the acute lymphoblastic leukemia (ALL)/acute myeloid leukemia (AML) dataset and generated about 30 classification rules with high accuracy. Conclusions: Ant-C can generate optimal number of clusters without incorporating any other algorithms such as K-means or agglomerative hierarchical clustering. For associative classification, while a few of the well-known algorithms such as Apriori, FP-growth and Magnum Opus are unable to mine any association rules from the ALL/AML dataset within a reasonable period of time, Ant-ARM is able to extract associative classification rules. (C) 2009 Elsevier B.V. All rights reserved.

关键词： gene expression data analysis Ant colony optimization Clustering Associative classification Swarm intelligence

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Incremental genetic K-means algorithm and its application in gene expression data analysis

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BMC BIOINFORMATICS 2004年第1期5卷 172-172页

作者： Lu, Y Lu, SY Fotouhi, F Deng, YP Brown, SJ Univ So Mississippi Dept Biol Sci Hattiesburg MS 39406 USA Wayne State Univ Dept Comp Sci Detroit MI 48202 USA Kansas State Univ Div Biol Manhattan KS 66506 USA

Background: In recent years, clustering algorithms have been effectively applied in molecular biology for gene expression data analysis. With the help of clustering algorithms such as K-means, hierarchical clustering, SOM, etc, genes are partitioned into groups based on the similarity between their expression profiles. In this way, functionally related genes are identified. As the amount of laboratory data in molecular biology grows exponentially each year due to advanced technologies such as Microarray, new efficient and effective methods for clustering must be developed to process this growing amount of biological data. Results: In this paper, we propose a new clustering algorithm, Incremental genetic K-means Algorithm (IGKA). IGKA is an extension to our previously proposed clustering algorithm, the Fast genetic K-means Algorithm (FGKA). IGKA outperforms FGKA when the mutation probability is small. The main idea of IGKA is to calculate the objective value Total Within-Cluster Variation (TWCV) and to cluster centroids incrementally whenever the mutation probability is small. IGKA inherits the salient feature of FGKA of always converging to the global optimum. C program is freely available at http://***/proj/FGKA/***. Conclusions: Our experiments indicate that, while the IGKA algorithm has a convergence pattern similar to FGKA, it has a better time performance when the mutation probability decreases to some point. Finally, we used IGKA to cluster a yeast dataset and found that it increased the enrichment of genes of similar function within the cluster.

关键词： Cluster Algorithm Functional Category Mutation Operator Mutation Probability gene expression data analysis

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Cluster analysis of gene expression data based on self-splitting and merging competitive learning

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IEEE TRANSACTIONS ON INFORMATION TECHNOLOGY IN BIOMEDICINE 2004年第1期8卷 5-15页

作者： Wu, SH Liew, AWC Yan, H Yang, MS City Univ Hong Kong Dept Comp Engn & Informat Technol Kowloon Hong Kong Peoples R China Univ Sydney Sch Elect & Informat Engn Sydney NSW 2006 Australia City Univ Hong Kong Dept Biol & Chem Kowloon Hong Kong Peoples R China

Cluster analysis of gene expression data from a cDNA microarray is useful for identifying biologically relevant groups of genes. However, finding the natural clusters in the data and estimating the correct number of clusters are still two largely unsolved problems. In this paper, we propose a new clustering framework that is able to address both these problems. By using the one-prototype-take-one-cluster (OPTOC) competitive learning paradigm, the proposed algorithm can find natural clusters in the input data, and the clustering solution is not sensitive to initialization. In order to estimate the number of distinct clusters in the data, we propose a cluster splitting and merging strategy. We have applied the new algorithm to simulated gene expression data for which the correct distribution of genes over clusters is known a priori. The results show that the proposed algorithm can find natural clusters and give the correct number of clusters. The algorithm has also been tested on real gene expression changes during yeast cell cycle, for which the fundamental patterns of gene expression and assignment of genes to clusters are well understood from numerous previous studies. Comparative studies with several clustering algorithms illustrate the effectiveness of our method.

关键词： cDNA microarrays cluster splitting and merging gene expression data analysis overclustering self-splitting and merging competitive learning (SSMCL)

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AnovArray: a set of SAS macros for the analysis of variance of gene expression data

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BMC BIOINFORMATICS 2005年第1期6卷 1-6页

作者： Hennequet-Antier, C Chiapello, H Piot, K Degrelle, S Hue, I Renard, JP Rodolphe, F Robin, S INRA Unite Math Informat & Genome F-78352 Jouy En Josas France INRA INA PG F-75231 Paris France INRA Unite Biol Dev & Reprod F-78352 Jouy En Josas France

Background: analysis of variance is a powerful approach to identify differentially expressed genes in a complex experimental design for microarray and macroarray data. The advantage of the anova model is the possibility to evaluate multiple sources of variation in an experiment. Results: AnovArray is a package implementing ANOVA for gene expression data using SAS(R) statistical software. The originality of the package is 1) to quantify the different sources of variation on all genes together, 2) to provide a quality control of the model, 3) to propose two models for a gene's variance estimation and to perform a correction for multiple comparisons. Conclusion: AnovArray is freely available at http://***/ stat/AnovArray and requires only SAS(R) statistical software.

关键词： gene expression data Anova Model Homogeneous Model Heterogeneous Model gene expression data analysis

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Application of Transcriptional gene Modules to analysis of Caenorhabditis elegans' gene expression data

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G3-geneS GENOMES geneTICS 2020年第10期10卷 3623-3638页

作者： Cary, Michael Podshivalova, Katie Kenyon, Cynthia Univ Calif San Francisco Program Biomed Informat San Francisco CA 94158 USA Univ Calif San Francisco Dept Biochem & Biophys San Francisco CA 94158 USA Cal Life Sci LLC 1170 Vet Blvd San Francisco CA 94080 USA

Identification of co-expressed sets of genes (gene modules) is used widely for grouping functionally related genes during transcriptomic data analysis. An organism-wide atlas of high-quality gene modules would provide a powerful tool for unbiased detection of biological signals from gene expression data. Here, using a method based on independent component analysis we call DEXICA, we have defined and optimized 209 modules that broadly represent transcriptional wiring of the key experimental organismC. elegans. These modules represent responses to changes in the environment (e.g., starvation, exposure to xenobiotics), genes regulated by transcriptions factors (e.g.,,), genes specific to tissues (e.g., neurons, muscle), genes that change during development, and other complex transcriptional responses to genetic, environmental and temporal perturbations. Interrogation of these modules reveals processes that are activated in long-lived mutants in cases where traditional analyses of differentially expressed genes fail to do so. Additionally, we show that modules can inform the strength of the association between a gene and an annotation (e.g., GO term). analysis of "module-weighted annotations" improves on several aspects of traditional annotation-enrichment tests and can aid in functional interpretation of poorly annotated genes. We provide an online interactive resource with tutorials at, in which users can find detailed information on each module, check genes for module-weighted annotations, and use both of these to analyze their own gene expression data (generated using any platform) or gene sets of interest.

关键词： gene expression data analysis microarray functional annotation of genes gene co-expression independent component analysis aging mitochondrial unfolded protein response respiration hif-1 atfs-1

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Pattern recognition techniques for the emerging field of bioinformatics: A review

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PATTERN RECOGNITION 2005年第11期38卷 2055-2073页

作者： Liew, AWC Yan, H Yang, MS Chinese Univ Hong Kong Dept Comp Sci & Engn Shatin Hong Kong Peoples R China City Univ Hong Kong Dept Comp Engn & Informat Technol Kowloon Hong Kong Peoples R China Univ Sydney Sch Elect & Informat Engn Sydney NSW 2006 Australia City Univ Hong Kong Dept Chem & Biol Kowloon Hong Kong Peoples R China

The emerging field of bioinformatics has recently created much interest in the computer science and engineering communities. With the wealth of sequence data in many public online databases and the huge amount of data generated from the Human Genome Project, computer analysis has become indispensable. This calls for novel algorithms and opens up new areas of applications for many pattern recognition techniques. In this article, we review two major avenues of research in bioinformatics, namely DNA sequence analysis and DNA microarray data analysis. In DNA sequence analysis, we focus on the topics of sequence comparison and gene recognition. For DNA microarray data analysis, we discuss key issues such as image analysis for gene expression data extraction, data pre-processing, clustering analysis for pattern discovery and gene expression time series data analysis. We describe current methods and show how computational techniques could be useful in these areas. It is our hope that this review article could demonstrate how the pattern recognition community could have an impact on the fascinating and challenging area of genomic research. (c) 2005 Pattern Recognition Society. Published by Elsevier Ltd. All rights reserved.

关键词： bioinformatics DNA sequence analysis DNA sequence comparison gene recognition DNA microarray gene expression data analysis gene expression clustering gene expression time series gene regulation

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Estimating Genome-Wide gene Networks Using Nonparametric Bayesian Network Models on Massively Parallel Computers

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IEEE-ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2011年第3期8卷 683-697页

作者： Tamada, Yoshinori Imoto, Seiya Araki, Hiromitsu Nagasaki, Masao Print, Cristin Charnock-Jones, D. Stephen Miyano, Satoru Univ Tokyo Ctr Human Genome Inst Med Sci Lab DNA Informat AnalMinato Ku Tokyo 1088639 Japan RIKEN Computat Sci Res Program Wako Saitama 3510198 Japan Univ Auckland Dept Mol Med & Pathol Sch Med Sci Fac Med & Hlth Sci Auckland 1 New Zealand Univ Cambridge Rosie Hosp Dept Obstet & Gynaecol Cambridge CB2 0SW England Biomed Res Ctr Natl Inst Hlth Res Cambridge England

We present a novel algorithm to estimate genome-wide gene networks consisting of more than 20,000 genes from gene expression data using nonparametric Bayesian networks. Due to the difficulty of learning Bayesian network structures, existing algorithms cannot be applied to more than a few thousand genes. Our algorithm overcomes this limitation by repeatedly estimating subnetworks in parallel for genes selected by neighbor node sampling. Through numerical simulation, we confirmed that our algorithm outperformed a heuristic algorithm in a shorter time. We applied our algorithm to microarray data from human umbilical vein endothelial cells (HUVECs) treated with siRNAs, to construct a human genome-wide gene network, which we compared to a small gene network estimated for the genes extracted using a traditional bioinformatics method. The results showed that our genome-wide gene network contains many features of the small network, as well as others that could not be captured during the small network estimation. The results also revealed master-regulator genes that are not in the small network but that control many of the genes in the small network. These analyses were impossible to realize without our proposed algorithm.

关键词： Biology and genetics gene networks Bayesian network structure learning gene expression data analysis

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genetic diagnosis of cancer by fuzzy-rough gene selection and the complementary hierarchical fuzzy classifier

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BIO-MEDICAL MATERIALS AND ENGINEERING 2011年第1期21卷 37-52页

作者： Shaeiri, Zahra Ghaderi, Reza Babol Univ Technol Dept Elect & Comp Engn Babol Sar Iran

gene expression data have extremely high dimensionality with respect to traditional classifiers which causes not to be used efficiently. In this paper a Fuzzy Rough gene Selection and Complementary Hierarchical Fuzzy classifier (FRGS CHF) to classify the gene expression data as a new methodology is proposed. First, some relevant genes are selected using fuzzy rough attribute selection method. After removing redundant genes, a new complementary hierarchical fuzzy classifier is proposed. The complementary learning mechanism refers to positive and negative learning which are found in the human brain hippocampus. FRGS CHF is made-up of two parallel hierarchical fuzzy systems;the first is trained with positive samples whilst the other is treated with negative samples. In contrast to many other methods such as statistical or neural networks, FRGS CHF provides greater interpretability. It does not rely on the assumption of underlying data distribution. Using complementary and hierarchical approaches, the proposed method exploits the lateral inhibition between output classes and considers the problem as a multidimensional problem. Benchmarked datasets are used to demonstrate the validity and advantages of the proposed method over the other existing methods in terms of the accuracy, better transparency, time efficiency together with fewer fuzzy rules and parameters.

关键词： gene expression data analysis fuzzy rough feature selection complementary hierarchical fuzzy classification

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gene clustering for time-series microarray with production outputs

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SOFT COMPUTING 2016年第11期20卷 4301-4312页

作者： Chira, Camelia Sedano, Javier Villar, Jose R. Camara, Monica Prieto, Carlos Tech Univ Cluj Napoca Baritiu 26-28 Cluj Napoca 400027 Romania Inst Tecnol Castilla & Leon Burgos Spain Univ Oviedo Gijon Spain Univ Salamanca Bioinformat Serv Nucleus Oviedo Spain

The identification of coexpressed genes from microarray data is a challenging problem in bioinformatics and computational biology. The objective of this study is to obtain knowledge about the most important genes and clusters related to production outputs of real-world time-series microarray data in the industrial microbiology area. Each sample in the microarray data experiment is complemented with the measurement of the corresponding production and growth values. A novel aspect of this research refers to considering the relation of coexpression patterns with the measured outputs to guide the biological interpretation of results. Shape-based clustering models are developed using the pattern of gene expression values over time and further incorporating knowledge about the correlation between the change in the gene expression level and the output value. Experiments are performed for time-series microarray of bacteria, and an analysis from a biological perspective is carried out. The obtained results confirm the existence of relationships between output variables and gene expressions. Moreover, the shape-based clustering methods show promising results, being able to guide metabolic engineering actions with the identification of potential targets.

关键词： gene clustering Time-series microarrays Feature selection gene expression data analysis

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