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Some statistics in bioinformatics: The fifth Armitage Lecture

STATISTICS IN MEDICINE 2009年第23期28卷 2833-2856页

作者： Solomon, Patricia J. Univ Adelaide Sch Math Sci Adelaide SA 5005 Australia

The spirit and content of the 2007 Armitage Lecture are presented in this paper. To begin, two areas of Peter Armitage's early work are distinguished: his pioneering research on sequential methods intended for use in medical trials and the comparison of survival curves. Their influence on Much later work is highlighted, and motivate the proposal of several statistical 'truths' that are presented in the paper. The illustration of these truths demonstrates biology's new morphology and its dominance over statistics in this century. An overview of a recent proteomics ovarian cancer study is given as a warning of what can happen when bioinformatics meets epidemiology badly, in particular, when the study design is poor. A statistical bioinformatics success story is outlined, in which gene profiling is helping to identify novel genes and networks involved in mouse embryonic stem cell development. Some concluding thoughts are given. Copyright (C) 2009 John Wiley & Sons, Ltd.

关键词： sequential trial survival analysis some statistical truths AIDS epidemiology microarray data analysis bioinformatics serum proteomic test for ovarian cancer embryonic stem cells pluripotency gene expression gene profiling proteomic profiles for ovarian cancer

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The Five-Gene-Network data analysis with Local Causal Discovery Algorithm Using Causal Bayesian Networks

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CHALLENGES OF SYSTEMS BIOLOGY: COMMUNITY EFFORTS TO HARNESS BIOLOGICAL COMPLEXITY 2009年第1期1158卷 93-101页

作者： Yoo, Changwon Brilz, Erik M. Univ Montana Missoula MT 59812 USA

Using microarray experiments, we can model causal relationships of genes measured through mRNA expression levels. To this end, it is desirable to compare experiments of the system under complete interventions of some genes, such as by knock out of some genes, with experiments of the system under no interventions. However, it. is expensive and difficult to conduct wet lab experiments of complete interventions of genes in a biological system. Thus, it will be helpful if we can discover promising causal relationships among genes with no interventions or incomplete interventions, such as by applying a treatment. that has unknown effects to modeled genes, in order to identify promising genes to perturb in the system dial can later be verified in wet laboratories. In this paper we use causal Bayesian networks to implement, a causal discovery algorithm-the equivalence local implicit. latent variable scoring method (EquLIM)-that identifies promising causal relationships even with a small dataset generated from no or incomplete interventions. We then apply EquLIM to analyze the five-gene-network data and compare EquLIM's predictions with true causal pairwise relationships between the genes.

关键词： causal Bayesian networks causal discovery gene networks high throughput data analysis microarray data analysis

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Sparse discriminant analysis for breast cancer biomarker identification and classification

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Progress in Natural Science:Materials International 2009年第11期19卷 1635-1641页

作者： Yu Shi~(a,b),Daoqing Dai~(a,*),Chaochun Liu~(a,b),Hong Yan~(b,c) a Center for Computer Vision and Department of Mathematics,Sun Yat-Sen(Zhongshan) University,Guangzhou 510275,China b Department of Electric Engineering,City University of Hong Kong,83 Tat Chee Avenue,Kowloon,Hong Kong,China c School of Electrical and Information Engineering,University of Sydney,NSW 2006,Australia [a]Center for Computer Vision and Department of Mathematics Sun Yat-Sen (Zhongshan) University Guangzhou 510275 China [b]Department of Electric Engineering City University of Hong Kong 83 Tat Chee Avenue Kowloon Hong Kong China [c]School of Electrical and Information Engineering University of Sydney NSW 2006 Australia

Biomarker identification and cancer classification are two important procedures in microarray data *** propose a novel unified method to carry out both *** first preselect biomarker candidates by eliminating unrelated genes through the BSS/WSS ratio filter to reduce computational cost,and then use a sparse discriminant analysis method for simultaneous biomarker identification and cancer ***,we give a mathematical justification about automatic biomarker *** results show that the proposed method can identify key genes that have been verified in biochemical or biomedical research and classify the breast cancer type correctly.

关键词： Biomarker identification Cancer classification Discriminant analysis Maximum penalized likelihood microarray data analysis

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BN+1 Bayesian network expansion for identifying molecular pathway elements

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Communicative & integrative biology 2010年第6期3卷 549-54页

作者： Andrew P Hodges Peter Woolf Yongqun He Center for Computational Medicine and Bioinformatics University of Michigan Medical School University of Michigan Michigan USA.

A Bayesian network expansion algorithm called BN+1 was developed to identify undocumented gene interactions in a known pathway using microarray gene expression data. In our recent paper, the BN+1 algorithm has been successfully used to identify key regulators including uspE in the E. coli ROS pathway and biofilm formation.18 In this report, a synthetic network was designed to further evaluate this algorithm. The BN+1 method was found to identify both linear and nonlinear relationships and correctly identify variables near the starting network. Using experimentally derived data, the BN+1 method identifies the gene fdhE as a potentially new ROS regulator. Finally, a range of possible score cutoff methods are explored to identify a set of criteria for selecting BN+1 calls.

关键词： BN+1 E. coli ROS bayesian network biofilm gene interaction microarray data analysis network expansion reactive oxygen species synthetic network

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DPBC: Distance Based Possibilistic Biclustering With Application to Gene Expression analysis

DPBC: Distance Based Possibilistic Biclustering With Applica...

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3rd International Conference on Bioinformatics and Biomedical Engineering

作者： Mahfouz, Mohamed A. Ismail, Mohamed A. Univ Alexandria Fac Engn Dept Comp & Syst Engn Alexandria 21544 Egypt

ISBN: (纸本)9781424429011

Biclustering is a key step in analyzing gene expression data by identifying patterns where subset of genes are co-related based on a subset of conditions. This paper proposes a new distance based possibilistic biclustering algorithm (DPBC), in which the average distances between rows and between columns of the bicluster are minimized and at the same time the size of the bicluster is maximized by computing the zeros of the derivative of appropriate objective function. The proposed algorithm uses the possibilistic clustering paradigm similar to another existing possibilistic biclustering algorithm PBC. Whereas PBC is based on residue our approach is applicable to any accepted definition for distances between pairs of rows or columns. Experimental study on the human dataset and several artificial datasets having different noise levels shows that the DPBC algorithm can offer substantial improvements over the previously proposed algorithms.

关键词： Possibilistic biclustering fuzzy clustering biclustering bidimensional clustering microarray data analysis

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Discovering Temporal Associations among Significant Changes in Gene Expression

Discovering Temporal Associations among Significant Changes ...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBMW 2009)

作者： Rohian, Hashmat An, Aijun Zhao, Jiashu Huang, Xiangji York Univ Dept Comp Sci & Engn Toronto ON M3J 2R7 Canada

ISBN: (纸本)9780769538853

One of the most demanding problems in mining temporal data is to identify how multivariate change associations might be discovered and used to better understand data interactions and dependencies. This paper introduces a framework to mine associations among significant changes in multivariate time-series data. Building on statistical methods, we detect significant changes in time-series data and use marginal change rates to qualify the direction of change at significant change points. Furthermore, a propositional confirmation-guided rule discovery method is used to discover associations among these significant changes. We apply our approach to gene expression data measured in yeast cell cycles and demonstrate that our method can learn novel and high-quality significant change associations among different genes. Such associations can be used to cluster genes and build gene interaction networks.

关键词： Biological data Mining and Visualization microarray data analysis Change Association Mining

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Investigating the minimum required number of genes for optimum classification of myopathy microarray data.

Investigating the minimum required number of genes for optim...

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9th International Conference on Information Technology and Applications in Biomedicine

作者： Sakellariou, Argiris Sanoudou, Despina Spyrou, George Acad Athens Biomed Res Fdn Athens Greece

ISBN: (纸本)9781424453788

The investigation of potential microarray markers, which in turn will speed up the molecular analysis and provide reliable results on the benefit of patient care is of significant importance. Feature selection techniques, which aim at minimizing the dimensionality of the microarray data by keeping the most significant genes according to their expression values is a necessary component towards this goal. In the current article, we present an investigation regarding the minimum required subsets of genes, which best classify myopathy data. For this purpose, we developed a tool that facilitates the users to easily access/use multiple feature selection methods and subsequently perform classification of data. For the current study, five feature selection methods on datasets from two different myopathies have been utilized. Our findings reveal subsets of very small number of genes, which can successfully classify gene expression datasets from different patients with skeletal myopathies. In addition, we observe that similar classification results may be obtained from completely different subsets of genes. The developed tool can expedite the identification of small gene subsets with high classification accuracy that could ultimately be used in the genetics clinics for diagnostic, prognostic and pharmacogenomic purposes.

关键词： molecular diagnosis myopathy microarray data analysis feature selection

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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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EPBC: Enhanced Possibilistic Biclustering With Application to Gene Expression analysis

EPBC: Enhanced Possibilistic Biclustering With Application t...

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3rd International Conference on Bioinformatics and Biomedical Engineering

作者： Mahfouz, Mohamed A. Ismail, Mohamed A. Univ Alexandria Fac Engn Dept Comp & Syst Engn Alexandria 21544 Egypt

ISBN: (纸本)9781424429011

Biclustering is an important data mining technique that allows identifying groups of genes which behave similarly under a subset of conditions for analyzing gene expression data from microarray technology. As a gene may play more than one biological role in conjunction with distinct groups of genes, possibilistic biclustering algorithms can give much insight towards different biological processes that each gene might participate into, along with providing a degree of participation as well, and the conditions under which its participation is most effective. This paper proposes modifications to the possibilistic biclustering algorithm introduced by Maurizio Filippone, et. al, in 2004 termed as PBC in which the mean square residue is minimized and at the same time the size of a bicluster is maximized by computing the zeros of the derivative of their objective function with respect to rows and columns memberships. Their algorithm suffers from some serious drawback First in computing the derivative of their objective function they consider the residue as a constant even though changing a membership of a row or a column affects the residue of each entry in the bicluster since it changes the average of the whole bicluster and the average of each column or row respectively. Furthermore, their algorithm is strongly sensitive to its two input parameters. In this paper the derivatives are accurately computed also their objective function is modified such that only single parameter is needed which allow us to develop a procedure for approximating a range for suitable values for this parameter. Whereas the accurate computation of the derivatives slightly increases the runtime of the proposed algorithm, experimental study on Yeast and several artificial datasets with embedded constant and additive modules having different noise levels shows that our algorithm can offer substantial improvements in terms of the quality of the output biclusters over several previously proposed biclu

关键词： Possibilistic biclustering fuzzy clustering biclustering bidimensional clustering microarray data analysis gene expression data

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Inverse Langmuir method for oligonucleotide microarray analysis

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BMC BIOINFORMATICS 2009年第1期10卷 64-64页

作者： Mulders, Geert C. W. M. Barkema, Gerard T. Carlon, Enrico Katholieke Univ Leuven Inst Theoret Phys B-3000 Louvain Belgium Univ Utrecht Inst Theoret Phys NL-3584 CE Utrecht Netherlands

Background: An algorithm for the analysis of Affymetrix Genechips is presented. This algorithm, referred to as the Inverse Langmuir Method (ILM), estimates the binding of transcripts to complementary probes using DNA/RNA hybridization free energies, and the hybridization between partially complementary transcripts in solution using RNA/RNA free energies. The balance between these two competing reactions allows for the translation of background-subtracted intensities into transcript concentrations. Results: To validate the ILM, it is applied to publicly available microarray data from a multi-lab comparison study. Here, microarray experiments are performed on samples which deviate only in few genes. The log(2) fold change between these two samples, as obtained from RT-PCR experiments, agrees well with the log(2) fold change as obtained with the ILM, indicating that the ILM determines changes in the expression level accurately. We also show that the ILM allows for the identification of outlying probes, as it yields independent concentration estimates per probe. Conclusion: The ILM is robust and offers an interesting alternative to purely statistical algorithms for microarray data analysis.

关键词： Outlier Probe microarray data analysis Log2 Fold Change Median Absolute Deviation Complementary Probe

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