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21st International Conference on Knowledge - Based and Intelligent Information and Engineering Systems (KES)

作者： Houari, Amina Ayadi, Wassim Ben Yahia, Sadok Univ Tunis El Manar Fac Sci Tunis LIPAH LR11ES14 Tunis 2092 Tunisia Univ Tunis Natl Higher Engn Sch Tunis LaTICE LR11ES04 Tunis 1008 Tunisia

A majority of existing biclustering algorithms for microarrays data focus only on extracting biclusters with positive correlations of genes. Nevertheless, biological studies show that a group of biologically significant genes may exhibit negative correlations. In this paper, we propose a new biclustering algorithm, called NBic-ARM (Negative Biclusters using Association Rule Mining). Based on Generic Association Rules, our algorithm identifies negatively-correlated genes. To assess NBic-ARM's performance, we carried out exhaustive experiments on three real-life datasets. Our results prove NBic-ARM's ability to identify statistically and biologically significant biclusters. (C) 2017 The Authors. Published by Elsevier B.V.

关键词： Biclustering Negative correlations Generic Association Rules data mining Bioinformatic dna microarray data

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Mining Negative Correlation Biclusters from Gene Expression data using Generic Association Rules

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Procedia Computer Science 2017年 112卷 278-287页

作者： Amina Houari Wassim Ayadi Sadok Ben Yahia University of Tunis El Manar Faculty of Sciences of Tunis LIPAH-LR11ES14 2092 Tunis Tunisia University of Tunis National Higher Engineering School of Tunis LaTICE-LR11ES04 1008 Tunis Tunisia

A majority of existing biclustering algorithms for microarrays data focus only on extracting biclusters with positive correlations of genes. Nevertheless, biological studies show that a group of biologically significant genes may exhibit negative correlations. In this paper, we propose a new biclustering algorithm, called NBic-ARM (Negative Biclusters using Association Rule Mining). Based on Generic Association Rules, our algorithm identifies negatively-correlated genes. To assess NBic-ARM’s performance, we carried out exhaustive experiments on three real-life datasets. Our results prove NBic-ARM’s ability to identify statistically and biologically significant biclusters.

关键词： Biclustering Negative correlations Generic Association Rules data mining Bioinformatic dna microarray data

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Gene selection for tumor classification using resilient backpropagation neural network 2

Gene selection for tumor classification using resilient back...

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2nd International Conference on Advances in Computing, Communication, and Automation (ICACCA) (Fall)

作者： Kaur, Sukhdeep Sharma, Ajay Shiv Kaur, Harpreet Singh, Karamjit Guru Nanak Dev Engn Coll Ludhiana Punjab India Bhutta Coll Engn & Technol Ludhiana Punjab India

ISBN: (纸本)9781509034802

Classification of tumor into its subtypes is a critical issue these days in tumor diagnosis and treatment. The correct classification of tumor into its subtypes leads to the appropriate treatment. Traditional methods classify cancers based on morphological appearance that leads to misclassification because of the similar appearance of sub-types of cancer. To overcome this problem of misclassification, gene expression profiles of genes are used. Gene expression profiling of genes using dna microarray data plays an important role in classification of tumor. But microarray data consists of genes expressions of thousands of genes so a method is required to reduce the dimensionality of data. In this paper, t-test scoring method is used for selecting important genes and artificial neural network is applied for the classification process. Resilient backpropagation is used as training algorithm. The experimental method employed in this paper shows that the proposed method classifies the data with more accuracy using eight genes only. So it has low computational complexity because of elimination of irrelevant features and high accuracy.

关键词： Artificial Neural Network dna microarray data Resilient Backpropagation Network t-score

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Classify Experiment Conditions of dna microarray data and Identify the Genes Associated with Alzheimer's Disease

Classify Experiment Conditions of DNA Microarray Data and Id...

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2010 First International Conference on Cellular,Molecular Biology, Biophysics and Bioengineering(CMBB 2010)

作者： Ben-Qiong Hu College of Information Management,Chengdu University of Technology,Chengdu,610059,China Shi-Peng Wang College of Mathematics and Software Science,Sichuan Normal University,Chengdu,610066,China Chao-Yang Pang Group of Gene Computation,Key Lab.Of Visual Computation and Virtual Reality of Sichuan Province,Chengdu 610066,China College of Mathematics and Software Science,Sichuan Normal University,Chengdu 610066,China

Alzheimer's disease(AD)is the most common form of *** the genes associated with it plays key role in its *** dna microarray data to identify AD gene becomes popular *** the common used method is difference expression(DE).In DE,the gene data which are generated under significantly different experiment conditions will be compared possibly and the accuracy of identification is *** evade this defect,a new method is presented in this paper:Gene data are classified into different classes so that the data included in same class are generated under similar experiment conditions and different class corresponds to significantly different *** from the centroids of all classes,upregulated(downregulated)genes are *** the extracted gene keeps invariant expression feature under all experiment conditions,it is identified as the candidate *** the method,some genes are identified and 8 genes agree with other research teams'results.

关键词： Alzheimer's disease(AD) Genes dna microarray data Ientification of Gene

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Discovering Low Overlapping Biclusters in Gene Expression data Through Generic Association Rules 5th

Discovering Low Overlapping Biclusters in Gene Expression Da...

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5th International Conference on Model Engineering and data Engineering (MEDI)

作者： Houari, Amina Ayadi, Wassim Ben Yahia, Sadok Univ Tunis El Manar Fac Siences Tunis LIPAH Tunis Tunisia Univ Tunis Higher Sch Siences & Technol Tunis LATICE Tunis Tunisia Univ Angres LERIA Angers France

ISBN: (纸本)9783319237817;9783319237800

Biclustering is a thriving and of paramount task in many biomedical applications. Indeed, the biclusters aim, among-others, the discovery of unveiling principles of cellular organizations and functions, to cite but a few. In this paper, we introduce a new algorithm called, BiARM, that aims to efficiently extract the most meaningful, low overlapping biclusters. The main originality of our algorithm stands in the fact that it relies on the extraction of generic association rules. The reduced set of association rules faithfully mimics relationships between sets of genes, proteins, or other cell members and gives important information for the analysis of diseases. The effectiveness of our method has been proved through extensive carried out experiments on real-life dna microarray data.

关键词： Biclustering Association rules mining data mining Bioinformatic dna microarray data

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An Ensemble Classifier Based on Kernel Method for Multi-situation dna microarray data

An Ensemble Classifier Based on Kernel Method for Multi-situ...

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5th International Conference on Intelligent Computing

作者： Wang, Xuesong Gu, Yangyang Cheng, Yuhu Lei, Ruhai China Univ Min & Technol Sch Informat & Elect Engn Xuzhou 221116 Jiangsu Peoples R China

ISBN: (纸本)9783642040696

In order to deal with the interaction between genes effectively, a kernel technology was adopted into a subspace method in our study. A linear subspace classifier was generalized to a nonlinear kernel subspace classifier by using a kernel principle component analysis method to constitute nonlinear feature subspaces. Because dna microarray data have characteristics of high dimension, few samples and strong nonlinearity, three types of classifiers based on kernel machine learning method were designed, i.e., support vector machine (SVM), kernel subspace classifier (KSUB-C) and kernel partial least-squares discriminant analysis (KPLS-DA). But the performances of these classifiers lie on the optimum setting of kernel functions and parameters. Therefore, to avoid the difficulty of selecting optimal kernel functions and parameters and to further improve the accuracy and generalization property of the classifiers, an ensemble classifier based on kernel method for multi-situation dna microarray data was proposed by adopting the idea of ensemble learning. The ensemble classifier combines the classification results of the SVM, KSUB-C and KPLS-DA classifiers. Experimental results involving three public dna microarray datasets indicate that the proposed ensemble classifier has high classification accuracy and perfect generalization property.

关键词： Multi-situation dna microarray data Kernel method Ensemble learning Classifier

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An algebra-based method for inferring gene regulatory networks

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BMC SYSTEMS BIOLOGY 2014年第1期8卷 37-37页

作者： Vera-Licona, Paola Jarrah, Abdul Garcia-Puente, Luis David McGee, John Laubenbacher, Reinhard Univ Connecticut Ctr Hlth Ctr Quantitat Med Farmington CT 06030 USA Univ Connecticut Ctr Hlth Dept Cell Biol Farmington CT 06030 USA Amer Univ Sharjah Dept Math & Stat Sharjah U Arab Emirates Sam Houston State Univ Dept Math & Stat Huntsville TX 77341 USA Radford Univ Dept Math & Stat Radford VA 24142 USA Jackson Lab Genom Med Farmington CT 06030 USA

Background: The inference of gene regulatory networks (GRNs) from experimental observations is at the heart of systems biology. This includes the inference of both the network topology and its dynamics. While there are many algorithms available to infer the network topology from experimental data, less emphasis has been placed on methods that infer network dynamics. Furthermore, since the network inference problem is typically underdetermined, it is essential to have the option of incorporating into the inference process, prior knowledge about the network, along with an effective description of the search space of dynamic models. Finally, it is also important to have an understanding of how a given inference method is affected by experimental and other noise in the data used. Results: This paper contains a novel inference algorithm using the algebraic framework of Boolean polynomial dynamical systems (BPDS), meeting all these requirements. The algorithm takes as input time series data, including those from network perturbations, such as knock-out mutant strains and RNAi experiments. It allows for the incorporation of prior biological knowledge while being robust to significant levels of noise in the data used for inference. It uses an evolutionary algorithm for local optimization with an encoding of the mathematical models as BPDS. The BPDS framework allows an effective representation of the search space for algebraic dynamic models that improves computational performance. The algorithm is validated with both simulated and experimental microarray expression profile data. Robustness to noise is tested using a published mathematical model of the segment polarity gene network in Drosophila melanogaster. Benchmarking of the algorithm is done by comparison with a spectrum of state-of-the-art network inference methods on data from the synthetic IRMA network to demonstrate that our method has good precision and recall for the network reconstruction task, while also predict

关键词： Reverse-engineering Network inference Boolean networks Molecular networks Gene regulatory networks Polynomial dynamical systems Algebraic dynamic models Evolutionary computation dna microarray data Time series data data noise

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A GRASP metaheuristic for microarray data analysis

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COMPUTERS & OPERATIONS RESEARCH 2013年第12期40卷 3108-3120页

作者： Cordone, Roberto Lulli, Guglielmo Univ Milan Dept Comp Sci I-20135 Milan Italy Univ Milano Bicocca Dept Informat Syst & Commun I-20122 Milan Italy

The Weighted Gene Regulatory Network (WGRN) problem consists in pruning a regulatory network obtained from dna microarray gene expression data, in order to identify a reduced set of candidate elements which can explain the expression of all other genes. Since the problem appears to be particularly hard for general-purpose solvers, we develop a Greedy Randomized Adaptive Search Procedure (GRASP) and refine it with three alternative Path Relinking procedures. For comparison purposes, we also develop a Tabu Search algorithm with a self-adapting tabu tenure. The experimental results show that GRASP performs better than Tabu Search and that Path Relinking significantly contributes to its effectiveness. (C) 2012 Elsevier Ltd. All rights reserved.

关键词： Gene regulatory networks dna microarray data GRASP Path Relinking Tabu Search

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Latent factor analysis facilitates modelling of oncogenic genes for colon adenocarcinoma

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IET SYSTEMS BIOLOGY 2013年第5期7卷 165-169页

作者： Fu, Changhe Deng, Su Song, Qiqing Jing, Ling China Agr Univ Coll Sci Beijing 100083 Peoples R China Shenyang Normal Univ Sch Math & Syst Sci Shenyang 110034 Peoples R China Guilin Univ Technol Coll Sci Guilin 541004 Peoples R China

Identification of oncogenic genes from a large sample number of genomic data is a challenge. In this study, a well-established latent factor model, Bayesian factor and regression model, are applied to predict unknown colon cancer related genes from colon adenocarcinoma genomic data. Four important latent factors were addressed by the latent factor model, focusing on characterisation of heterogeneity of expression patterns of specific oncogenic genes by using microarray data of 174 colon cancer patients. Based on the fact that variables included in the same latent factor have some common characteristics and known cancer related genes in Online Mendelian Inheritance in Man, the authors found that the four latent factors can be employed to predict unknown colon cancer related genes that were never reported in the literature. The authors validated 15 identified genes by checking their somatic mutations of the same patients from dna sequencing data.

关键词： Bayes methods biological organs cancer dna genetics genomics lab-on-a-chip medical diagnostic computing molecular biophysics physiological models regression analysis latent factor analysis oncogenic genes colon adenocarcinoma genomic data Bayesian factor colon cancer related genes heterogeneity expression patterns dna microarray data Online Mendelian Inheritance in Man somatic mutations dna sequencing data

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Metasample-Based Robust Sparse Representation for Tumor Classification

Metasample-Based Robust Sparse Representation for Tumor Clas...

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2013 International Conference on Biomedical Engineering(ICBE 2013)

作者： Bin Gan Chun-Hou Zheng Jin-Xing Liu College of Information technology and communication Qufu Normal University College of Electrical Engineering and Automation Anhui University

In this paper, based on sparse representation classification and robust thought, we propose a new classifier, named MRSRC (Metasample Based Robust Sparse Representation Classificatier), for dna microarray data classification. Firstly, we extract Metasample from trainning sample. Secondly, a weighted matrix W is added to solve an l1-regularized least square problem. Finally, the testing sample is classified according to the sparsity coefficient vector of it. The experimental results on the dna microarray data classification prove that the proposed algorithm is efficient.

关键词： dna microarray data Sparse Representation Classification MRSRC Robust

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