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A pattern-oriented specification of gene network inference processes

COMPUTERS IN BIOLOGY AND MEDICINE 2013年第10期43卷 1415-1427页

作者： Trepode, Nestor W. de Farias, Clever R. G. Barrera, Junior Univ Sao Paulo Dept Comp Sci & Math DCM Fac Philosophy Sci & Letters Ribeirao Preto FFCLR BR-14040901 Ribeirao Preto SP Brazil

Patterns have been widely used in Computer Science. A pattern describes a generic solution to an existing problem in a more readable and accessible form. A pattern-oriented process specification consists of a generic and abstract description of a process. This paper presents a pattern-oriented specification of a genetic regulatory network inference process performed from microarray data and prior biological knowledge. The proposed specification was conceived based on prior work on gene inference networks. The adequacy of the proposed solution was then evaluated with respect to modern tendencies of the genes network inference literature. (c) 2013 Elsevier Ltd. All rights reserved.

关键词： Genetic regulatory networks Dynamical system identification Gene network inference microarray data analysis Process modeling Patterns

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RelSim: An integrated method to identify disease genes using gene expression profiles and PPIN based similarity measure

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INFORMATION SCIENCES 2017年 384卷 110-125页

作者： Maji, Pradipta Shah, Ekta Paul, Sushmita Indian Stat Inst Machine Intelligence Unit Biomed Imaging & Bioinformat Lab Kolkata India Indian Inst Technol Dept Biol Jodhpur Rajasthan India

One of the important problems in functional genomics is how to select the disease genes. In this regard, the paper presents a new gene selection algorithm, termed as RelSim, to identify disease genes. It integrates judiciously the information of gene expression profiles and protein-protein interaction networks. A new similarity measure is introduced to compute the functional similarity between two genes. It is based on the information of protein-protein interaction networks. The new similarity measure offers an efficient way to calculate the functional similarity between two genes. The proposed algorithm selects a set of genes as disease genes, considering both microarray and protein-protein interaction data, by maximizing the relevance and functional similarity of the selected genes. While gene expression profiles are used to identify differentially expressed genes, the protein protein interaction networks help to compute the functional similarity among genes. The performance of the proposed algorithm, along with a comparison with other related methods, is demonstrated on several colon cancer data sets. (C) 2016 Elsevier Inc. All rights reserved.

关键词： Disease gene identification microarray data analysis Feature selection Protein-protein interaction networks

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Identification of Differentially Expressed Genes for Time-Course microarray data Based on Modified RM ANOVA

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IEEE-ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2012年第2期9卷 451-466页

作者： ElBakry, Ola Ahmad, M. Omair Swamy, M. N. S. Concordia Univ Dept Elect & Comp Engn Ctr Signal Proc & Communi Montreal PQ H3G 1M8 Canada

The regulation of gene expression is a dynamic process, hence it is of vital interest to identify and characterize changes in gene expression over time. We present here a general statistical method for detecting changes in microarray expression over time within a single biological group and is based on repeated measures (RM) ANOVA. In this method, unlike the classical F-statistic, statistical significance is determined taking into account the time dependency of the microarray data. A correction factor for this RM F-statistic is introduced leading to a higher sensitivity as well as high specificity. We investigate the two approaches that exist in the literature for calculating the p-values using resampling techniques of gene-wise p-values and pooled p-values. It is shown that the pooled p-values method compared to the method of the gene-wise p-values is more powerful, and computationally less expensive, and hence is applied along with the introduced correction factor to various synthetic data sets and a real data set. These results show that the proposed technique outperforms the current methods. The real data set results are consistent with the existing knowledge concerning the presence of the genes. The algorithms presented are implemented in R and are freely available upon request.

关键词： ANOVA microarray data analysis permutation time-course data variance moderation

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Differential prioritization in feature selection and classifier aggregation for multiclass microarray datasets

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data MINING AND KNOWLEDGE DISCOVERY 2007年第3期14卷 329-366页

作者： Ooi, Chia Huey Chetty, Madhu Teng, Shyh Wei Monash Univ Gippsland Sch Informat Technol Churchill Vic Australia

The high dimensionality of microarray datasets endows the task of multiclass tissue classification with various difficulties-the main challenge being the selection of features deemed relevant and non-redundant to form the predictor set for classifier training. The necessity of varying the emphases on relevance and redundancy, through the use of the degree of differential prioritization (DDP) during the search for the predictor set is also of no small importance. Furthermore, there are several types of decomposition technique for the feature selection (FS) problem-all-classes-at-once, one-vs.-all (OVA) or pairwise (PW). Also, in multiclass problems, there is the need to consider the type of classifier aggregation used-whether non-aggregated (a single machine), or aggregated (OVA or PW). From here, first we propose a systematic approach to combining the distinct problems of FS and classification. Then, using eight well-known multiclass microarray datasets, we empirically demonstrate the effectiveness of the DDP in various combinations of FS decomposition types and classifier aggregation methods. Aided by the variable DDP, feature selection leads to classification performance which is better than that of rank-based or equal-priorities scoring methods and accuracies higher than previously reported for benchmark datasets with large number of classes. Finally, based on several criteria, we make general recommendations on the optimal choice of the combination of FS decomposition type and classifier aggregation method for multiclass microarray datasets.

关键词： tissue classification microarray data analysis multiclass classification feature selection classifier aggregation

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Scalable Non-Linear Graph Fusion for Prioritizing Cancer-Causing Genes

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IEEE-ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2022年第2期19卷 1130-1143页

作者： Shah, Ekta Maji, Pradipta Indian Stat Inst Machine Intelligence Unit Biomed Imaging & Bioinformat Lab Kolkata 700108 India

In the past few decades, both gene expression data and protein-protein interaction (PPI)networks have been extensively studied, due to their ability to depict important characteristics of disease-associated genes. In this regard, the paper presents a new gene prioritization algorithm to identify and prioritize cancer-causing genes, integrating judiciously the complementary information obtained from two data sources. The proposed algorithm selects disease-causing genes by maximizing the importance of selected genes and functional similarity among them. A new quantitative index is introduced to evaluate the importance of a gene. It considers whether a gene exhibits a differential expression pattern across sick and healthy individuals, and has a strong connectivity in the PPI network, which are the important characteristics of a potential biomarker. As disease-associated genes are expected to have similar expression profiles and topological structures, a scalable non-linear graph fusion technique, termed as ScaNGraF, is proposed to learn a disease-dependent functional similarity network from the co-expression and common neighbor based similarity networks. The proposed ScaNGraF, which is based on message passing algorithm, efficiently combines the shared and complementary information provided by different data sources with significantly lower computational cost. A new measure, termed as DiCoIN, is introduced to evaluate the quality of a learned affinity network. The performance of the proposed graph fusion technique and gene selection algorithm is extensively compared with that of some existing methods, using several cancer data sets.

关键词： Diseases Gene expression Proteins Protein engineering Indexes data integration Disease gene identification microarray data analysis protein-protein interaction network network fusion

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ICP: A novel approach to predict prognosis of prostate cancer with inner-class clustering of gene expression data

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COMPUTERS IN BIOLOGY AND MEDICINE 2013年第10期43卷 1363-1373页

作者： Kim, Hyunjin Ahn, Jaegyoon Park, Chihyun Yoon, Youngmi Park, Sanghyun Yonsei Univ Dept Comp Sci Seoul 120749 South Korea Gachon Univ Dept Comp Engn Inchon South Korea

Prostate cancer has heterogeneous characteristics. For that reason, even if tumors appear histologically similar to each other, there are many cases in which they are actually different, based on their gene expression levels. A single tumor may have multiple expression levels with both high-risk cancer genes and low-risk cancer genes. We can produce more useful models for stratifying prostate cancers into high-risk cancer and low-risk cancer categories by considering the range in each class through inner-class clustering. In this paper, we attempt to classify cancers into high-risk (aggressive) prostate cancer and low-risk (non-aggressive) prostate cancer using ICP (Inner-class Clustering and Prediction). Our model classified more efficiently than the models of the algorithms used for comparison. After discovering a number of genes linked to prostate cancer from the gene pairs used in our classification, we discovered that the proposed method can be used to find new unknown genes and gene pairs which distinguish between high-risk cancer and low-risk cancer. (c) 2013 Elsevier Ltd. All rights reserved.

关键词： Classification Clustering Bioinformatics microarray data analysis Prognosis Prostate cancer

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Constructing gene regulatory networks from microarray data using GA/PSO with DTW

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APPLIED SOFT COMPUTING 2012年第3期12卷 1115-1124页

作者： Lee, Chien-Pang Leu, Yungho Yang, Wei-Ning Natl Taiwan Univ Sci & Technol Dept Informat Management Taipei 106 Taiwan

Recently, many methods have been proposed for constructing gene regulatory networks (GRNs). However, most of the existing methods ignored the time delay regulatory relation in the GRN predictions. In this paper, we propose a hybrid method, termed GA/PSO with DTW, to construct GRNs from microarray datasets. The proposed method uses test of correlation coefficient and the dynamic time warping (DTW) algorithm to determine the existence of a time delay relation between two genes. In addition, it uses the particle swarm optimization (PSO) to find thresholds for discretizing the microarray dataset. Based on the discretized microarray dataset and the predicted types of regulatory relations among genes, the proposed method uses a genetic algorithm to generate a set of candidate GRNs from which the predicted GRN is constructed. Three real-life sub-networks of yeast are used to verify the performance of the proposed method. The experimental results show that the GA/PSO with DTW is better than the other existing methods in terms of predicting sensitivity and specificity. (C) 2011 Elsevier B.V. All rights reserved.

关键词： Gene regulatory networks Genetic algorithms Particle swarm optimization Dynamic time warping microarray data analysis

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On biological validity indices for soft clustering algorithms for gene expression data

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COMPUTATIONAL STATISTICS & data analysis 2011年第5期55卷 1969-1979页

作者： Wu, Han-Ming Tamkang Univ Dept Math Tamsui 25137 Taipei County Taiwan

Unsupervised clustering methods such as K-means, hierarchical clustering and fuzzy c-means have been widely applied to the analysis of gene expression data to identify biologically relevant groups of genes. Recent studies have suggested that the incorporation of biological information into validation methods to assess the quality of clustering results might be useful in facilitating biological and biomedical knowledge discoveries. In this study, we generalize two bio-validity indices, the biological homogeneity index and the biological stability index, to quantify the abilities of soft clustering algorithms such as fuzzy c-means and model-based clustering. The results of an evaluation of several existing soft clustering algorithms using simulated and real data sets indicate that the soft versions of the indices provide both better precision and better accuracy than the classical ones. The significance of the proposed indices is also discussed. (C) 2010 Elsevier B.V. All rights reserved.

关键词： Biological validity indices Fuzzy clustering Fuzzy cluster validity Fuzzy c-means Gene expression microarray data analysis Soft clustering

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Statistical properties of the quantile normalization method for density curve alignment

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MATHEMATICAL BIOSCIENCES 2013年第2期242卷 129-142页

作者： Gallon, Santiago Loubes, Jean-Michel Maza, Elie Univ Antioquia Dept Matemat & Estadist Fac Ciencias Econ Medellin 050010 Colombia Univ Toulouse 3 Inst Math Toulouse F-31062 Toulouse 9 France UMR 990 INRA INP ENSAT Genom & Biotechnol Fruit Lab Toulouse France

The article investigates the large sample properties of the quantile normalization method by Bolstad et al. (2003) [4] which has become one of the most popular methods to align density curves in microarray data analysis. We prove consistency of this method which is viewed as a particular case of the structural expectation procedure for curve alignment, which corresponds to a notion of barycenter of measures in the Wasserstein space. Moreover, we show that, this method fails in some case of mixtures, and we propose a new methodology to cope with this issue. (C) 2013 Elsevier Inc. All rights reserved.

关键词： Curve registration Manifold registration microarray data analysis Normalization Order statistics Structural expectation Wasserstein distance

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Case study of a functional genomics application for an FPGA-based coprocessor

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MICROPROCESSORS AND MICROSYSTEMS 2004年第5-6期28卷 213-222页

作者： Van Court, T Herbordt, MC Barton, RJ Boston Univ Dept Elect & Comp Engn Boston MA 02215 USA Univ Houston Dept Elect & Comp Engn Houston TX 77204 USA

Although microarrays are already having a tremendous impact on biomedical science, they still present great computational challenges. We examine a particular problem involving the computation of linear regressions on a large number of vector combinations in a high-dimensional parameter space, a problem that was found to be virtually intractable on a PC cluster. We observe that characteristics of this problem map particularly well to FPGAs and confirm this with an implementation that results in a 1600-fold speed-up over an optimized serial implementation. Some of the other contributions involve the data routing structure, the analysis of bit-width allocation, and the handling of missing data. Since this problem is representative of many in functional genomics, part of the overall significance of this work is that it points to a new area of applicability for FPGA coprocessors. (C) 2004 Elsevier B.V. All rights reserved.

关键词： computational coprocessor FPGA application bioinformatics microarray data analysis Steiner system

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