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Evaluation of Time Series microarray data for Dynamic Gene Regulatory Network Inference 14th

14th Mediterranean Conference on Medical and Biological Engineering and Computing (MEDICON)

作者： Xenitidis, P. Seimenis, I. Kakolyris, S. Adamopoulos, A. Democritus Univ Thrace Sch Med Med Phys Lab Alexandroupolis Greece Democritus Univ Thrace Sch Med Dept Med Oncol Alexandroupolis Greece

ISBN: (纸本)9783319327037;9783319327013

microarray data are primarily used by gene regulatory network inference algorithms. We used dynamic artificial gene regulatory networks to evaluate the adequacy of time course microarray data to support the inference process. We evaluated the effect of different ways that genes can be triggered on the performance of an inference algorithm. We evaluated the effect of sparseness of a network on the inference performance. Finally we evaluated the effect of noise in microarray data on the inference process.

关键词： Gene regulatory networks inference microarray data

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Distributed feature selection: An application to microarray data classification

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APPLIED SOFT COMPUTING 2015年 30卷 136-150页

作者： Bolon-Canedo, V. Sanchez-Marono, N. Alonso-Betanzos, A. Univ A Coruna Dept Comp Sci Lab Res & Dev Artificial Intelligence LIDIA La Coruna 15071 Spain

Feature selection is often required as a preliminary step for many pattern recognition problems. However, most of the existing algorithms only work in a centralized fashion, i.e. using the whole dataset at once. In this research a new method for distributing the feature selection process is proposed. It distributes the data by features, i.e. according to a vertical distribution, and then performs a merging procedure which updates the feature subset according to improvements in the classification accuracy. The effectiveness of our proposal is tested on microarray data, which has brought a difficult challenge for researchers due to the high number of gene expression contained and the small samples size. The results on eight microarray datasets show that the execution time is considerably shortened whereas the performance is maintained or even improved compared to the standard algorithms applied to the non-partitioned datasets. (C) 2015 Elsevier B.V. All rights reserved.

关键词： Feature selection Distributed learning microarray data

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A novel aggregate gene selection method for microarray data classification

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PATTERN RECOGNITION LETTERS 2015年 60-61卷 16-23页

作者： Thanh Nguyen Khosravi, Abbas Creighton, Douglas Nahavandi, Saeid Deakin Univ Ctr Intelligent Syst Res Geelong Vic 3216 Australia

This paper introduces a novel method for gene selection based on a modification of analytic hierarchy process (AHP). The modified AHP (MAHP) is able to deal with quantitative factors that are statistics of five individual gene ranking methods: two-sample t-test, entropy test, receiver operating characteristic curve, Wilcoxon test, and signal to noise ratio. The most prominent discriminant genes serve as inputs to a range of classifiers including linear discriminant analysis, k-nearest neighbors, probabilistic neural network, support vector machine, and multilayer perceptron. Gene subsets selected by MAHP are compared with those of four competing approaches: information gain, symmetrical uncertainty, Bhattacharyya distance and ReliefF. Four benchmark microarray datasets: diffuse large B-cell lymphoma, leukemia cancer, prostate and colon are utilized for experiments. As the number of samples in microarray data datasets are limited, the leave one out cross validation strategy is applied rather than the traditional cross validation. Experimental results demonstrate the significant dominance of the proposed MAHP against the competing methods in terms of both accuracy and stability. With a benefit of inexpensive computational cost, MAHP is useful for cancer diagnosis using DNA gene expression profiles in the real clinical practice. (C) 2015 Elsevier B.V. All rights reserved.

关键词： Gene selection Analytic hierarchy process Classification Gene expression profiles microarray data

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A Novel Information Theoretic Approach to Gene Selection for Cancer Classification Using microarray data

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CURRENT BIOINFORMATICS 2015年第4期10卷 431-440页

作者： Naseem, Imran Togneri, Roberto Bennamoun, Mohammed Karachi Inst Econ & Technol Coll Engn Karachi Pakistan Univ Western Australia Sch Elect Elect & Comp Engn Crawley WA 6009 Australia Univ Western Australia Sch Comp Sci & Software Engn Crawley WA 6009 Australia

In this research an efficient gene selection method called Discriminant Mutual Information (DMI) algorithm is proposed. The DMI algorithm sequentially induces discrimination and relevance to identify the most significant genes for tumor classification. In particular, in the first step the entire gene population is decorrelated by the formation of gene-sets such that the genes with similar characteristics form a single gene-set. The mutual information criterion is further employed to identify the most representative gene of each gene-set. Extensive experiments have been conducted on six publicly available databases where the proposed DMI algorithm has shown good results compared to a number of state-of-the-art approaches. Extensive computational analysis clearly reflects the computational efficiency of the proposed approach, typically it requires only a few seconds for experimentation on standard microarray datasets.

关键词： Gene selection microarray data tumor classification

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Approaches for Cancer Diagnosis Based on microarray data

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JOURNAL OF COMPUTATIONAL AND THEORETICAL NANOSCIENCE 2015年第9期12卷 2566-2573页

作者： Zeng Zhiqiang Hua Shi Xiamen Univ Technol Coll Comp & Informat Engn Xiamen 361005 Peoples R China

Development on microarray technology may lead to opportunities in bioinformatics and makes it possible to diagnose cancer on the level of gene expression. Many adverse factors, such as small number of samples with high-dimensional characteristics and data class imbalances, pose challenges to traditional machine learning methods. Numerous researchers had worked on these problems and obtained significant achievements. This paper describes the data sets used in study, summarizes the approaches for cancer diagnosis based on microarray data, and provides outlook on future research direction.

关键词： microarray data Classification Dimensionality Reduction Cancer Diagnosis

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A hybrid ensemble method based on double disturbance for classifying microarray data

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BIO-MEDICAL MATERIALS AND ENGINEERING 2015年第Sup1期26卷 S1961-S1968页

作者： Chen, Tao Xue, Huifeng Hong, Zenglin Cui, Man Zhao, Hui Northwestern Polytech Univ Sch Automat Xian 710072 Shaanxi Peoples R China Shaanxi Univ Technol Sch Math & Comp Sci Hanzhong 723000 Shaanxi Peoples R China

microarray data has small samples and high dimension, and it contains a significant amount of irrelevant and redundant genes. This paper proposes a hybrid ensemble method based on double disturbance to improve classification performance. Firstly, original genes are ranked through reliefF algorithm and part of the genes are selected from the original genes set, and then a new training set is generated from the original training set according to the previously selected genes. Secondly, D bootstrap training subsets are produced from the previously generated training set by bootstrap technology. Thirdly, an attribute reduction method based on neighborhood mutual information with a different radius is used to reduce genes on each bootstrap training subset to produce new training subsets. Each new training subset is applied to train a base classifier. Finally, a part of the base classifiers are selected based on the teaching-learning-based optimization to build an ensemble by weighted voting. Experimental results on six benchmark cancer microarray datasets showed proposed method decreased ensemble size and obtained higher classification performance compared with Bagging, AdaBoost, and Random Forest.

关键词： microarray data bagging reliefF neighborhood mutual information teaching-learning-based optimization

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Reduction Combination Determination for Efficient microarray data Classification with Three Stage Dimensionality Reduction Approach 1

Reduction Combination Determination for Efficient Microarray...

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1st International Conference on Computational Intelligence in data Mining (ICCIDM)

作者： Dash, Rasmita Misra, B. B. Siksha O Anusandhan Univ Dept Comp Sci & Informat Technol Inst Tech Educ & Res Bhubaneswar Orissa India Silicon Inst Technol Dept Comp Sci & Engn Bhubaneswar 751024 Odisha India

ISBN: (纸本)9788132222088;9788132222071

Classification of microarray data with high dimension and small sample size is a complex task. This work explores the optimal search space appropriate for classification. Here the crush of dimensionality is handled with a three stages dimension reduction technique. At the first stage, statistical measures are used to remove genes that do not contribute for classification. In the second stage, more noisy genes are removed by considering signal to noise ratio (SNR). In the third stage, principal component analysis (PCA) method is used to further reduce the dimension. Further, how much to reduce at each stage is crucial to develop an efficient classifier. Combination of different proportion of reduction at each stage is considered in this study to find appropriate combination for each dataset which maximizes the classifier performance. Help of naive Bayes classifier is taken here to find appropriate combination of reduction.

关键词： microarray data data classification Feature selection

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A Hybrid Possibilistic Algorithm for Biclustering: Application to microarray data Analysis 26

A Hybrid Possibilistic Algorithm for Biclustering: Applicati...

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26th International Workshop on database and Expert Systems Applications (DEXA)

作者： Ben Saber, Haifa Elloumi, Mourad Univ Tunis Lab Technol Informat Commun & Elect Engn LaTICE Tunis Tunisia Time Univ Tunis Tunisia Univ Tunis El Manar Lab Technol Informat Commun & Elect Engn LaTICE Tunis Tunisia

ISBN: (纸本)9781467375818

A attractive way to perform biclustering of genes and conditions is to adopt the notion of fuzzy sets, which is useful for discovering overlapping biclusters. Fuzzy clustering is well known as a robust and efficient way to reduce computation cost to obtain the better results. However, this approach is not explored very well. In this paper, we propose a new algorithm called, RefineBicluster for biclustering of microarray data using the fuzzy approach. This algorithm adopts the strategy of one bicluster at a time, assigning to each data matrix element, i.e. each gene and for each condition, a membership to bicluster. The biclustering problem, in where one would maximize the size of the bicluster and minimize the residual, is faced as the optimization of a proper functional. Applied on continuous synthetic datasets, our algorithm outperforms other biclustering algorithms for microarray data.

关键词： biclustering fuzzy strategy microarray data

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A Comparision Between Methods for Generating Differentially Expressed Genes from microarray data for Prediction of Disease 3

A Comparision Between Methods for Generating Differentially ...

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THIRD INTERNATIONAL CONFERENCE ON COMPUTER, COMMUNICATION, CONTROL AND INFORMATION TECHNOLOGY (C3IT)

作者： Dasgupta, Srirupa Saha, Goutam Mondal, Ritwik Pal, Rajat Kumar Chanda, Amitabha Govt Coll Engn & Leather Technol Dept Informat Technol Kolkata 91 India Govt Coll Engn & Ceram Technol Dept Informat Technol Kolkata 10 India Univ Calcutta Dept Comp Sci & Engn Kolkata 9 India

ISBN: (纸本)9781479944453

Feature selection from microarray data has become an ever evolving area of research. Numerous techniques have widely been applied for extraction of genes which are expressed differentially in microarray data. Some of these comprise of studies related to fold-change approach, classical t-statistics and modified t-statistics. It has been found that the gene lists returned by these methods are dissimilar. In this work we compare the outputs of two different feature selection methods using three classifiers based on different algorithms namely the Random Forest Ensemble based method, the Support vector machine (SVM) and the KNN methods, using the prediction accuracy of the test datasets.

关键词： microarray data differential expression fold change t-test false detection ratio random forest SVM KNN classification signature gene-ontology

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MLP Network for Lung Cancer Presence Prediction Based on microarray data 3

MLP Network for Lung Cancer Presence Prediction Based on Mic...

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3rd IEEE World Conference on Complex Systems (WCCS)

作者： Rafii, Fadoua Kbir, M'hamed Ait Hassani, Badr Dine Rossi UAE LIST Lab Tangier Morocco UAE LABIPHABE Lab Tangier Morocco

ISBN: (纸本)9781467396691

The appearance of the microarray technology has attracted the scientific community and industry;with its ability of measuring simultaneously the activity and interactions of thousands of genes. This advanced technology was applied for enormous issues such as drug discovery, gene discovery, diagnosis and prognosis of disease and toxicological research. Despite the fact that microarray applications have known birth in many biological studies, the handling and analysis of the data obtained are not trivial tasks. For these reasons, it has been focused on the present paper on the PCA classification technique and Neural Network for microarray data;in the object of reducing the large data and producing informative results. The methodology proposes an approach based on MLP neural network to resolve the problem of lung cancer classification based on microarray data. The approach consists on data reduction by using the PCA Technique, followed by a classification based on MLP network, feed-forward neural network known by its stable learning. The effectiveness of the implemented method was evaluated by measuring the correct classification rate performed on lung cancer gene expression dataset and compared to results obtained by other methods that use the same data.

关键词： microarray PCA MLP microarray data Neural Network

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