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Large-scale integration of cancer microarray data identifies a robust common cancer signature

BMC BIOINFORMATICS 2007年第1期8卷 1-13页

作者： Xu, Lei Geman, Donald Winslow, Raimond L. Johns Hopkins Univ Inst Computat Med Baltimore MD 21218 USA Johns Hopkins Univ Ctr Cardiovasc Bioinformat & Modeling Baltimore MD 21218 USA Johns Hopkins Univ Dept Appl Math & Stat Baltimore MD 21218 USA Johns Hopkins Univ Ctr Imaging Sci Baltimore MD 21218 USA

Background: There is a continuing need to develop molecular diagnostic tools which complement histopathologic examination to increase the accuracy of cancer diagnosis. DNA microarrays provide a means for measuring gene expression signatures which can then be used as components of genomic-ased diagnostic tests to determine the presence of cancer. Results: In this study, we collect and integrate similar to 1500 microarray gene expression profiles from 26 published cancer data sets across 21 major human cancer types. We then apply a statistical method, referred to as the Top-Scoring Pair of Groups (TSPG) classifier, and a repeated random sampling strategy to the integrated training data sets and identify a common cancer signature consisting of 46 genes. These 46 genes are naturally divided into two distinct groups;those in one group are typically expressed less than those in the other group for cancer tissues. Given a new expression profile, the classifier discriminates cancer from normal tissues by ranking the expression values of the 46 genes in the cancer signature and comparing the average ranks of the two groups. This signature is then validated by applying this decision rule to independent test data. Conclusion: By combining the TSPG method and repeated random sampling, a robust common cancer signature has been identified from large-scale microarray data integration. Upon further validation, this signature may be useful as a robust and objective diagnostic test for cancer.

关键词： microarray data microarray Platform Independent Test data Prostate Cancer data Cancer microarray data

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Gene networks reconstruction and time-series prediction from microarray data using recurrent neural fuzzy networks

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IET SYSTEMS BIOLOGY 2007年第1期1卷 41-50页

作者： Maraziotis, I. A. Dragomir, A. Bezerianos, A. Univ Patras Sch Med Dept Med Phys Rion 26500 Greece

Reverse engineering problems concerning the reconstruction and identification of gene regulatory networks through gene expression data are central issues in computational molecular biology and have become the focus of much research in the last few years. An approach has been proposed for inferring the complex causal relationships among genes from microarray experimental data, which is based on a novel neural fuzzy recurrent network. The method derives information on the gene interactions in a highly interpretable form ( fuzzy rules) and takes into account the dynamical aspects of gene regulation through its recurrent structure. To determine the efficiency of the proposed approach, microarray data from two experiments relating to Saccharomyces cerevisiae and Escherichia coli have been used and experiments concerning gene expression time course prediction have been conducted. The interactions that have been retrieved among a set of genes known to be highly regulated during the yeast cell-cycle are validated by previous biological studies. The method surpasses other computational techniques, which have attempted genetic network reconstruction, by being able to recover significantly more biologically valid relationships among genes.

关键词： Saccharomyces cerevisiae microorganisms multilayer perceptrons gene regulation Neural nets Biology and medical computing Systems theory applications in biology and medicine complex causal relationships genetic network reconstruction dynamical aspects Escherichia coli Molecular biophysics Neural computing techniques highly interpretable form computational molecular biology biology computing Physics of subcellular structures microarray data yeast cell-cycle cellular biophysics time-series prediction biological studies recurrent neural fuzzy networks gene networks gene interactions fuzzy rules reverse engineering problems reverse engineering genetics fuzzy neural nets

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Mining gene-sample-time microarray data: a coherent gene cluster discovery approach

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KNOWLEDGE AND INFORMATION SYSTEMS 2007年第3期13卷 305-335页

作者： Jiang, Daxin Pei, Jian Ramanathan, Murali Lin, Chuan Tang, Chun Zhang, Aidong Nanyang Technol Univ Sch Comp Engn Singapore 639798 Singapore Simon Fraser Univ Burnaby BC V5A 1S6 Canada SUNY Buffalo Buffalo NY USA

Extensive studies have shown that mining microarray data sets is important in bioinformatics research and biomedical applications. In this paper, we explore a novel type of gene-sample-time microarray data sets that records the expression levels of various genes under a set of samples during a series of time points. In particular, we propose the mining of coherent gene clusters from such data sets. Each cluster contains a subset of genes and a subset of samples such that the genes are coherent on the samples along the time series. The coherent gene clusters may identify the samples corresponding to some phenotypes (e.g., diseases), and suggest the candidate genes correlated to the phenotypes. We present two efficient algorithms, namely the Sample-Gene Search and the Gene-Sample Search, to mine the complete set of coherent gene clusters. We empirically evaluate the performance of our approaches on both a real microarray data set and synthetic data sets. The test results have shown that our approaches are both efficient and effective to find meaningful coherent gene clusters.

关键词： bioinformatics clustering microarray data

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Phase-wise Clustering of Time Series Gene Expression data

Phase-wise Clustering of Time Series Gene Expression Data

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Int Joint Conference of 10th IEEE Int Conf on Trust, Security and Privacy in Computing and Communications (TrustCom) / 8th IEEE Int Conf on Embedded Software and Systems (ICESS) / 6th Int Conf on Frontier of Computer Science and Technology (FCST)

作者： Goyal, Poonam Karwa, Rohan Sunil Goyal, Navneet John, Matthew BITS Pilani Dept Comp Sci Pilani Rajasthan India

ISBN: (纸本)9780769546001

Extensive studies have shown that analyzing microarray time series data is important in bioinformatics research and biomedical applications. An observation in the analysis of gene expression data is that many genes have similarity in their expression patterns and therefore appear to be co-regulated. Previously, the time series gene expression data was analyzed mainly by checking the global similarities between the gene expression profiles and local similarities were overlooked. Local similarities can provide useful insight into gene behavior. In this paper, we propose a clustering algorithm for analyzing the time series gene expression data to identify the gene clusters based on the phase-wise local similarities in the cell cycle. Our approach exploits the fact that the genes which are involved in one phase of a cell cycle would have a characteristic profile for time points belonging to that phase and may not be involved in other phases. Moreover, a gene that is clustered with a set of genes in one phase might be involved with a different set of genes in other phases. In the proposed approach, we first clustered the genes at every time point of a phase and group genes with similar expression profiles, i.e., we group those genes together which remain in the same cluster at every time point within a phase. The functions of genes were obtained from Gene Ontology. In this paper, the results are presented for different phases of a cell cycle. Candidate genes are identified for these phases and their groups are analyzed. We found that the group of candidate genes had few genes which are known to be involved. Furthermore, some genes are found to be involved in more than one phase with different set of genes. Results presented show that local similarities can provide useful insight into gene behavior. Results are compared with an existing algorithm, STEM. We have used a saccharomyces cerevisiae cell cycle microarray database which is part of the Stanford microarray database

关键词： component Time-series gene expression data microarray data Clustering gene expression profiles

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Internal Evaluation Measures as Proxies for External Indices in Clustering Gene Expression data

Internal Evaluation Measures as Proxies for External Indices...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Vukicevic, Milan Delibasic, Boris Jovanovic, Milos Suknovic, Milija Obradovic, Zoran Univ Belgrade Fac Org Sci Ctr Business Decis Making Belgrade Serbia Temple Univ Ctr Data Analyt & Biomed Informat Philadelphia PA 19122 USA

ISBN: (纸本)9780769545745

Several external indices that use information not present in the dataset were shown to be useful for evaluation of representative based clustering algorithms. However, such supervised measures are not directly useful for construction of better clustering algorithms when class labels are not provided. We propose a method for identifying internal cluster evaluation measures that use only information present in the dataset and are related to given external indices. We utilize these internal measures for the construction of representative based clustering algorithms. Both identification and utilization steps of the proposed method are enabled by use of a component-based clustering algorithm design. Experiments on 432 algorithms using gene expression data sets provide evidence that some internal measures could be used as surrogates for external indices proposed in the literature. Moreover, the obtained results suggest that internal measures correlated to selected external indices can guide the algorithms toward significantly better cluster models.

关键词： component-based clustering evaluation measures microarray data

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Integration of bioinformatics tools in candidate gene prioritization of co-regulated gene sets in Saccharomyces cerevisiae

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BMC BIOINFORMATICS 2011年第7期12卷 1-2页

作者： Abedi, Vida Yeasin, Mohammed Sutter, Thomas R. Univ Memphis Dept Elect & Comp Engn Memphis TN 38152 USA Univ Memphis Dept Biol Sci Memphis TN 38152 USA Univ Memphis Coll Arts & Sci Bioinformat Program Memphis TN 38152 USA

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A Hybrid Model to Favor the Selection of High Quality Features in High Dimensional Domains

A Hybrid Model to Favor the Selection of High Quality Featur...

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12th International Conference on Intelligent data Engineering and Automated Learning (IDEAL 2011)

作者： Cannas, Laura Maria Dessi, Nicoletta Pes, Barbara Univ Cagliari Dipartimento Matemat & Informat I-09124 Cagliari Italy

ISBN: (纸本)9783642238772

Feature selection is a widely recognized challenging task in dealing with application problems with a large number of features and a limited number of training samples. Filters and wrappers are the most popular feature selection strategies, but recent literature shows the emergence of hybrid approaches aiming at combining the strengths of filters and wrappers while avoiding their drawbacks. This paper proposes a new hybrid model for feature selection that takes advantage of a filter method to weight the relevance of each feature. Top-ranked features are selected, in an incremental way, resulting in a set of nested feature spaces of relatively small size. An evolutionary wrapper further refines each space by extracting small subsets of highly predictive features. Extensive experiments on a benchmark microarray dataset state the effectiveness of the proposed approach.

关键词： Feature Selection Hybrid Methods Genetic Algorithms microarray data

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Professional Tennis Player Ranking Strategy based Monte Carlo Feature Selection

Professional Tennis Player Ranking Strategy based Monte Carl...

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IEEE International Conference on Bioinformatics and Biomedicine Workshops (BIBMW)

作者： Xie, Ruifei Han, Bin Li, Lihua Zhang, Juan Zhu, Lei Hangzhou Dianzi Univ Coll Life Informat Sci & Instrument Engn Hangzhou Zhejiang Peoples R China

ISBN: (纸本)9781457716133

Extracting significant features from high-dimensional and small sample-size microarray data is a challenging problem. Other than wrapper or filter methods, we propose a novel feature selection algorithm which integrates the ideas of professional tennis players ranking, such as seed players and dynamic ranking with Monte Carlo simulation. Seed players make the 'game' more competitive and selective, hence improve the selection efficiency. Besides, the ranks of features are dynamically updated and this ensures that it is always the current best players to take part in each competitions. The proposed algorithm is tested on widely used public datasets. Results demonstrate that the proposed method comparatively converges faster, more stable and has good performance in classification and therefore is an efficient algorithm for feature selection.

关键词： Algorithm design and analysis Classification algorithms Equations Games Heuristic algorithms Monte Carlo feature selection Monte Carlo methods Monte Carlo simulation Testing Training data handling microarray data professional tennis player ranking strateg

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How high is the level of technical noise in microarray data?

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BIOLOGY DIRECT 2007年第1期2卷 1-9页

作者： Klebanov, Lev Yakovlev, Andrei Charles Univ Prague Dept Probabil & Stat CZ-18675 Prague 8 Czech Republic Univ Rochester Dept Biostat & Computat Biol Rochester NY 14642 USA

Background: microarray gene expression data are commonly perceived as being extremely noisy because of many imperfections inherent in the current technology. A recent study conducted by the microarray Quality Control (MAQC) Consortium and published in Nature Biotechnology provides a unique opportunity to probe into the true level of technical noise in such data. Results: In the present report, the MAQC study is reanalyzed in order to quantitatively assess measurement errors inherent in high-density oligonucleotide array technology (Affymetrix platform). The level of noise is directly estimated from technical replicates of gene expression measurements in the absence of biological variability. For each probe set, the magnitude of random fluctuations across technical replicates is characterized by the standard deviation of the corresponding log-expression signal. The resultant standard deviations appear to be uniformly small and symmetrically distributed across probe sets. The observed noise level does not cause any tangible bias in estimated pair-wise correlation coefficients, the latter being particularly prone to its presence in microarray data. Conclusion: The reported analysis strongly suggests that, contrary to popular belief, the random fluctuations of gene expression signals caused by technical noise are quite low and the effect of such fluctuations on the results of statistical inference from Affymetrix GeneChip microarray data is negligibly small.

关键词： microarray data Technical Noise Affymetrix Platform microarray Quality Control Affymetrix microarray data

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A Quasi-linear Approach for microarray Missing Value Imputation

A Quasi-linear Approach for Microarray Missing Value Imputat...

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18th International Conference on Neural Information Processing (ICONIP)

作者： Cheng, Yu Wang, Lan Hu, Jinglu Waseda Univ Grad Sch Informat Prod & Syst Wakamatsu Ku Hibikino 2-7 Kitakyushu Fukuoka Japan

ISBN: (纸本)9783642249549;9783642249556

Missing value imputation for microarray data is important for gene expression analysis algorithms, such as clustering, classification and network design. A number of algorithms have been proposed to solve this problem, but most of them are only limited in linear analysis methods, such as including the estimation in the linear combination of other no-missing-value genes. It may result from the fact that microarray data. often comprises of huge size of genes with only a small number of observations, and nonlinear regression techniques are prone to overfitting. In this paper, a quasi-linear SVR model is proposed to improve the linear approaches, and it can be explained in a piecewise linear interpolation way. Two real datasets are tested and experimental results show that the quasi-linear approach for missing value imputation outperforms both the linear and nonlinear approaches.

关键词： microarray data missing value imputation quasi-linear SVR

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