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Inferring Large-Scale Gene Regulatory Networks Using a Randomized Algorithm Based on Singular Value Decomposition

IEEE-ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2019年第6期16卷 1997-2008页

作者： Fan, Anjing Wang, Haitao Xiang, Hua Zou, Xiufen Wuhan Univ Sch Math & Stat Wuhan 430072 Peoples R China

Reconstructing large-scale gene regulatory networks (GRNs) is a challenging problem in the field of computational biology. Various methods for inferring GRNs have been developed, but they fail to accurately infer GRNs with a large number of genes. Additionally, the existing evaluation indexes for evaluating the constructed networks have obvious disadvantages because GRNs in most biological systems are sparse. In this paper, we develop a new method for inferring GRNs based on randomized singular value decomposition (RSVD) and ordinary differential equation (ODE)-based optimization, denoted as IGRSVD, from large-scale time series data with noise. The three major contributions of this paper are as follows. First, the IGRSVD algorithm uses the RSVD to handle the noise and reduce the original large-scale data into small-scale problems. Second, we propose two new evaluated indexes, the expected value accuracy (EVA) and the expected value error (EVE), to evaluate the performance of inferred networks by considering the sparse features in the network. Finally, the proposed IGRSVD algorithm is compared with the existing SVD algorithm and PCA_CMI algorithm using four subsets from E. coli and datasets from DREAM challenge. The experimental results demonstrate that the IGRSVD algorithm is effective and more suitable for reconstructing large-scale networks.

关键词： Gene regulatory networks microarray data analysis reconstruction randomized singular value decomposition

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DK-means: a deterministic K-means clustering algorithm for gene expression analysis

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PATTERN analysis AND APPLICATIONS 2019年第2期22卷 649-667页

作者： Jothi, R. Mohanty, Sraban Kumar Ojha, Aparajita Pandit Deendayal Petr Univ Sch Technol Dept Comp Engn Gandhinagar India Indian Inst Informat Technol Design & Mfg Jabalpu Jabalpur Madhya Pradesh India

Clustering has been widely applied in interpreting the underlying patterns in microarray gene expression profiles, and many clustering algorithms have been devised for the same. K-means is one of the popular algorithms for gene data clustering due to its simplicity and computational efficiency. But, K-means algorithm is highly sensitive to the choice of initial cluster centers. Thus, the algorithm easily gets trapped with local optimum if the initial centers are chosen randomly. This paper proposes a deterministic initialization algorithm for K-means (DK-means) by exploring a set of probable centers through a constrained bi-partitioning approach. The proposed algorithm is compared with classical K-means with random initialization and improved K-means variants such as K-means++ and MinMax algorithms. It is also compared with three deterministic initialization methods. Experimental analysis on gene expression datasets demonstrates that DK-means achieves improved results in terms of faster and stable convergence, and better cluster quality as compared to other algorithms.

关键词： K-means clustering algorithm Initial cluster centers Gene expression clustering microarray data analysis

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Identifying Cancer Subnetwork Markers Using Game Theory Method

Identifying Cancer Subnetwork Markers Using Game Theory Meth...

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International Conference on Biomedical and Health Informatics (ICBHI)

作者： Farahmand, Saman Goliaei, Sama Kashani, Zahra Razaghi Moghadam Farahmand, Sina Univ Tehran Network Sci & Technol Dept Res Lab Computat Biol Tehran Iran Univ Tehran Life Sci Engn Dept Tehran Iran IIT Lab Neural Engn Res Biomed Engn Dept Chicago IL 60616 USA

ISBN: (纸本)9789811045059;9789811045042

In this paper, a novel game theory method is proposed to identify subnetwork markers by integrating gene expression profile and protein-protein interaction network. The proposed method has been evaluated on different cancer datasets in order to classify cancer phenotypes. To verify the performance of our approach, the identified subnetwork markers are compared with a greedy search method. The proposed method is capable of identifying robust subnetwork markers and presents higher classification performance.

关键词： Cancer subnetwork markers microarray data analysis Game theory Cancer classification

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pwrEWAS: a user-friendly tool for comprehensive power estimation for epigenome wide association studies (EWAS)

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BMC BIOINFORMATICS 2019年第1期20卷 218-218页

作者： Graw, Stefan Henn, Rosalyn Thompson, Jeffrey A. Koestler, Devin C. Univ Kansas Med Ctr Dept Biostat & Data Sci Kansas City KS 66103 USA Univ Kansas Med Ctr Dept Canc Biol Kansas City KS 66103 USA

BackgroundWhen designing an epigenome-wide association study (EWAS) to investigate the relationship between DNA methylation (DNAm) and some exposure(s) or phenotype(s), it is critically important to assess the sample size needed to detect a hypothesized difference with adequate statistical power. However, the complex and nuanced nature of DNAm data makes direct assessment of statistical power challenging. To circumvent these challenges and to address the outstanding need for a user-friendly interface for EWAS power evaluation, we have developed *** current implementation of pwrEWAS accommodates power estimation for two-group comparisons of DNAm (e.g. case vs control, exposed vs non-exposed, etc.), where methylation assessment is carried out using the Illumina Human Methylation BeadChip technology. Power is calculated using a semi-parametric simulation-based approach in which DNAm data is randomly generated from beta-distributions using CpG-specific means and variances estimated from one of several different existing DNAm data sets, chosen to cover the most common tissue-types used in EWAS. In addition to specifying the tissue type to be used for DNAm profiling, users are required to specify the sample size, number of differentially methylated CpGs, effect size(s) (), target false discovery rate (FDR) and the number of simulated data sets, and have the option of selecting from several different statistical methods to perform differential methylation analyses. pwrEWAS reports the marginal power, marginal type I error rate, marginal FDR, and false discovery cost (FDC). Here, we demonstrate how pwrEWAS can be applied in practice using a hypothetical EWAS. In addition, we report its computational efficiency across a variety of user *** under- and overpowered studies unnecessarily deplete resources and even risk failure of a study. With pwrEWAS, we provide a user-friendly tool to help researchers circumvent these risks and to assist in t

关键词： DNA methylation microarray data analysis Statistical power Sample size calculation Bioconductor package Illumina human methylation BeadChip

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Correlation feature selection based improved-Binary Particle Swarm Optimization for gene selection and cancer classification

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APPLIED SOFT COMPUTING 2018年 62卷 203-215页

作者： Jain, Indu Jain, Vinod Kumar Jain, Renu Jiwaji Univ Sch Math & Allied Sci Gwalior 474006 MP India PDPM Indian Inst Informat Technol Design & Mfg Dumna Airport RdPO Khamaria Jabalpur MP India

DNA microarray technology has emerged as a prospective tool for diagnosis of cancer and its classification. It provides better insights of many genetic mutations occurring within a cell associated with cancer. However, thousands of gene expressions measured for each biological sample using microarray pose a great challenge. Many statistical and machine learning methods have been applied to get most relevant genes prior to cancer classification. A two phase hybrid model for cancer classification is being proposed, integrating Correlation-based Feature Selection (CFS) with improved-Binary Particle Swarm Optimization (iBPSO). This model selects a low dimensional set of prognostic genes to classify biological samples of binary and multi class cancers using NaiveBayes classifier with stratified 10-fold cross-validation. The proposed iBPSO also controls the problem of early convergence to the local optimum of traditional BPSO. The proposed model has been evaluated on 11 benchmark microarray datasets of different cancer types. Experimental results are compared with seven other well known methods, and our model exhibited better results in terms of classification accuracy and the number of selected genes in most cases. In particular, it achieved up to 100% classification accuracy for seven out of eleven datasets with a very small sized prognostic gene subset (up to <1.5%) for all eleven datasets. (C) 2017 Elsevier B.V. All rights reserved.

关键词： microarray data analysis Cancer classification Improved Binary Particle Swarm Optimization (iBPSO) Hybrid model Gene selection Naive-Bayes

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NFκB pathway analysis: An approach to analyze gene co-expression networks employing feedback cycles

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COMPUTATIONAL BIOLOGY AND CHEMISTRY 2018年 72卷 62-76页

作者： Dillenburg, Fabiane Cristine Zanotto-Filho, Alfeu Fonseca Moreira, Jose Claudio Ribeiro, Leila Carro, Luigi Univ Fed Rio Grande do Sul Inst Informat Porto Alegre RS Brazil Univ Fed Santa Catarina CCB Dept Farmacol Florianopolis SC Brazil Univ Fed Rio Grande do Sul ICBS Dept Bioquim Porto Alegre RS Brazil

The genes of the NF kappa B pathway are involved in the control of a plethora of biological processes ranking from inhibition of apoptosis to metastasis in cancer. It has been described that Gliobastoma multiforme (GBM) patients carry aberrant NF kappa B activation, but the molecular mechanisms are not completely understood. Here, we present a NF kappa B pathway analysis in tumor specimens of GBM compared to nonneoplasic brain tissues, based on the different kind of cycles found among genes of a gene co-expression network constructed using quantized data obtained from the microarrays. A cycle is a closed walk with all vertices distinct (except the first and last). Thanks to this way of finding relations among genes, a more robust interpretation of gene correlations is possible, because the cycles are associated with feedback mechanisms that are very common in biological networks. In GBM samples, we could conclude that the stoichiometric relationship between genes involved in NF kappa B pathway regulation is unbalanced. This can be measured and explained by the identification of a cycle. This conclusion helps to understand more about the biology of this type of tumor. (C) 2017 Elsevier Ltd. All rights reserved.

关键词： NF kappa B microarray data analysis Gene co-expression networks Cycles Feedback mechanisms Gliobastoma multiforme

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AUCTSP: an improved biomarker gene pair class predictor

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BMC BIOINFORMATICS 2018年第1期19卷 1-13页

作者： Kagaris, Dimitri Khamesipour, Alireza Yiannoutsos, Constantin T. Southern Illinois Univ Dept Elect & Comp Engn 1230 Lincoln Dr Carbondale IL 62901 USA Indiana Univ Dept Biostat Sch Publ Hlth 410 West 10th StSuite 3000 Indianapolis IN 46202 USA

Background: The Top Scoring Pair (TSP) classifier, based on the concept of relative ranking reversals in the expressions of pairs of genes, has been proposed as a simple, accurate, and easily interpretable decision rule for classification and class prediction of gene expression profiles. The idea that differences in gene expression ranking are associated with presence or absence of disease is compelling and has strong biological plausibility. Nevertheless, the TSP formulation ignores significant available information which can improve classification accuracy and is vulnerable to selecting genes which do not have differential expression in the two conditions ("pivot" genes). Results: We introduce the AUCTSP classifier as an alternative rank-based estimator of the magnitude of the ranking reversals involved in the original TSP. The proposed estimator is based on the Area Under the Receiver Operating Characteristic (ROC) Curve (AUC) and as such, takes into account the separation of the entire distribution of gene expression levels in gene pairs under the conditions considered, as opposed to comparing gene rankings within individual subjects as in the original TSP formulation. Through extensive simulations and case studies involving classification in ovarian, leukemia, colon, breast and prostate cancers and diffuse large b-cell lymphoma, we show the superiority of the proposed approach in terms of improving classification accuracy, avoiding overfitting and being less prone to selecting non-informative (pivot) genes. Conclusions: The proposed AUCTSP is a simple yet reliable and robust rank-based classifier for gene expression classification. While the AUCTSP works by the same principle as TSP, its ability to determine the top scoring gene pair based on the relative rankings of two marker genes across all subjects as opposed to each individual subject results in significant performance gains in classification accuracy. In addition, the proposed method tends to avoid selec

关键词： microarray data analysis Gene expression Gene selection Receiver operating characteristic (ROC) curve AUC Leukemia Breast cancer Ovarian cancer Colon cancer Prostate cancer Diffuse large B-Cell lymphoma

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Gene selection for tumor classification using a novel bio-inspired multi-objective approach

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GENOMICS 2018年第1期110卷 10-17页

作者： Dashtban, M. Balafar, Mohammadali Suravajhala, Prashanth Univ Tabriz Fac Elect & Comp Engn Dept Comp Engn Tabriz Iran Birla Inst Sci Res Jaipur 302001 Rajasthan India Bioclues Org Hyderabad 500072 Telangana India

Identifying the informative genes has always been a major step in microarray data analysis. The complexity of various cancer datasets makes this issue still challenging. In this paper, a novel Bio-inspired Multi-objective algorithm is proposed for gene selection in microarray data classification specifically in the binary domain of feature selection. The presented method extends the traditional Bat Algorithm with refined formulations, effective multi-objective operators, and novel local search strategies employing social learning concepts in designing random walks. A hybrid model using the Fisher criterion is then applied to three widely-used microarray cancer datasets to explore significant biomarkers which reveal the effectiveness of the proposed method for genomic analysis. Experimental results unveil new combinations of informative biomarkers have association with other studies.

关键词： Cancer classification Gene selection Bat algorithm Feature selection Evolutionary algorithms microarray data analysis

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Fuzzy Clustering for microarray data analysis: A Review

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CURRENT BIOINFORMATICS 2011年第4期6卷 427-443页

作者： Liu, Jin Pham, Tuan D. Univ New S Wales Sch Engn & Informat Technol Canberra ACT 2600 Australia China Univ Min & Technol Sch Comp Sci & Technol Xuzhou 221116 Jiangshu Peoples R China

microarray technology is capable of providing biomedical and biological researchers with a massive amount of gene expression information to enable rapid significant discoveries in life sciences. microarray data analysis has been developing at a fast pace during the last decade and has become a popular and standard research method for gene expression studies undertaken by genomics research groups worldwide. Many computational tools have been applied to mine this data in order to discover biologically meaningful knowledge. One of the most useful analysis tools is the fuzzy clustering approach which can be modeled in many types of the continuous partitions of data and are well known for its ability to identify co-expressed genes and annotate functions for novel genes. As the computational analysis of microarray data has been developing rapidly, articles surveying its progress of research and developments are periodically needed. In this paper, we review the recent research into microarray data analysis based on fuzzy clustering algorithms and present a newly developed fuzzy clustering technique which, potentially, can be applied to perform microarray data analysis.

关键词： microarray data analysis fuzzy clustering fuzzy C-means fuzzy hyper-prototype clustering

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A novel hybrid feature selection method for microarray data analysis

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APPLIED SOFT COMPUTING 2011年第1期11卷 208-213页

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

Recently, many methods have been proposed for microarray data analysis. One of the challenges for microarray applications is to select a proper number of the most relevant genes for data analysis. In this paper, we propose a novel hybrid method for feature selection in microarray data analysis. This method first uses a genetic algorithm with dynamic parameter setting (GADP) to generate a number of subsets of genes and to rank the genes according to their occurrence frequencies in the gene subsets. Then, this method uses the chi(2)-test for homogeneity to select a proper number of the top-ranked genes for data analysis. We use the support vector machine (SVM) to verify the efficiency of the selected genes. Six different microarray datasets are used to compare the performance of the GADP method with the existing methods. The experimental results show that the GADP method is better than the existing methods in terms of the number of selected genes and the prediction accuracy. (c) 2009 Elsevier B.V. All rights reserved.

关键词： Feature selection Hybrid method Genetic algorithm chi(2)-Test for homogeneity microarray data analysis

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