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3rd International Conference on Swarm, Evolutionary and Memetic Computing (SEMCCO)

作者： Pati, Soumen Kumar Das, Asit Kumar St Thomas Coll Engn & Technol Dept Comp Sci Informat Technol Kolkata 23 India Bengal Engn & Sci Univ Dept Comp Sci & Technol Sibpur 03 Howrah India

ISBN: (纸本)9783642353796;9783642353802

dna gene expression profiling plays an important role in a wide range of areas in biological science for handling cancer diseases. data generated in microarray related experiments have many missing expression values which lose valuable information from the dataset. The proposed method first partitions the genes without missing values using clustering algorithm and then measures the similarity between a gene with missing values and the centroid of the clusters and finally, the missing values are estimated by the corresponding expression values of the centroid giving maximum similarity factor. The method explicitly depends on expression values to imputes missing values, completed the input dataset with low errors for data analysis and knowledge discovery. The method is compared with prominent approaches, such as zero-impute, row-average-impute and KNN-impute in terms of "Normalized Root Mean Square Error" to claim its novelty.

关键词： dna microarray data Gene expression value Clustering algorithm Similarity measurement Missing value imputation

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Improving cluster-based missing value estimation of dna microarray data

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BIOMOLECULAR ENGINEERING 2007年第2期24卷 273-282页

作者： Bras, Ligia P. Menezes, Jose C. Univ Tecn Lisboa Ctr Chem & Biol Engn IST Dept Chem & Biol Engn P-1049 Lisbon Portugal

We present a modification of the weighted K-nearest neighbours imputation method (KNNimpute) for missing values (MVs) estimation in microarray data based on the reuse of estimated data. The method was called iterative KNN imputation (IKNNimpute) as the estimation is performed iteratively using the recently estimated values. The estimation efficiency of lKNNimpme was assessed under different conditions (data type, fraction and structure of missing data) by the normalized root mean squared error (NRMSE) and the correlation coefficients between estimated and true values, and compared with that of other cluster-based estimation methods (KNNimpute and sequential KNN). We further investigated the influence of imputation on the detection of differentially expressed genes using SAM by examining the differentially expressed genes that are lost after MV estimation. The performance measures give consistent results, indicating that the iterative procedure of lKNNimpute can enhance the prediction ability of cluster-based methods in the presence of high missing rates, in non-time series experiments and in data sets comprising both time series and non-time series data, because the information of the genes having MVs is used more efficiently and the iterative procedure allows refining the MV estimates. More importantly, IKNN has a smaller detrimental effect on the detection of differentially expressed genes. (c) 2007 Elsevier B.V. All rights reserved.

关键词： missing value estimation K-nearest neighbours gene expression data dna microarray data

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A framework for microarray data-based tumor diagnostic system with improving performance incrementally

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EXPERT SYSTEMS WITH APPLICATIONS 2010年第9期37卷 6682-6688页

作者： Yu, Hualong Gu, Guochang Liu, Haibo Shen, Jing Harbin Engn Univ Coll Comp Sci & Technol Harbin 150001 Peoples R China

Gene expression data obtained from dna microarrays have shown useful in tumor classification problems. However, most existing related literatures focused on how to extract tumor-related genes and design appropriate classification strategies, but neglected effect of future unlabeled samples which are expensive to label. In this paper, we propose a novel framework to construct microarray data-based tumor diagnostic system with improving performance incrementally. Through the proposed framework, system is permitted to evaluate confidences of a new unlabeled sample in each class and opportunity of misdiagnosis decreases by returning uncertain samples to medical experts. Moreover, the system is also enabled to improve predictive accuracy by learning new experiences from incremental labeled samples constantly. The proposed framework of system has been tested on two well-known tumor microarray datasets with encouraging results and shown great potential for the developments of generic platform for tumor clinical diagnosis based on microarray data. (C) 2010 Elsevier Ltd. All rights reserved.

关键词： dna microarray data Tumor classification Tumor diagnostic system Feature gene selection

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A Network Partition Algorithm for Mining Gene Functional Modules of Colon Cancer from dna microarray data

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Genomics, Proteomics & Bioinformatics 2006年第4期4卷 245-252页

作者： Xiao-Gang Ruan Jin-Lian Wang Jian-Geng Li Institute of Artificial Intelligence and Robotics School of Electronic Information ＆ Control Engineering Beijing University of Technology Beijing 100022 China

Computational analysis is essential for transforming the masses of microarray datainto a mechanistic understanding of cancer. Here we present a method for findinggene functional modules of cancer from microarray data and have applied it tocolon cancer. First, a colon cancer gene network and a normal colon tissue genenetwork were constructed using correlations between the genes. Then the modulesthat tended to have a homogeneous functional composition were identified by split-ting up the network. Analysis of both networks revealed that they are *** of the gene functional modules for colon cancer and normal tissuesshowed that the modules’ functions changed with their structures.

关键词： dna microarray data colon cancer gene functional module GN algorithm

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Map Gene Information Into A Curve of Function and Identify the Co-Expressed Genes with APOE in Alzheimer's Disease

Map Gene Information Into A Curve of Function and Identify t...

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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,China Liang-Jie Ye Group of Gene Computation,College of Mathematics and Software Science,Sichuan Normal University,Chengdu,China Chao-Yang Pang Group of Gene Computation,Key Lab.Of Visual Computation and Virtual Reality of Sichuan Province,Chengdu,China Group of Gene Computation,College of Mathematics and Software Science,Sichuan Normal University,Chengdu,China

Alzheimer's disease(AD)is the most common type of dementia,and identifying the genes associated with it is one of key *** gene is known as the most risky factor of AD currently,and identifying the genes co-expressed with it is interesting,and these genes are associated with AD possibly. The aim of this paper is to identify the co-expressed genes with APOE and to discover new features of *** this purpose,a new kind of method is presented in this *** this method,the degrees of correlation strength between APOE and all genes are mapped into a curve of *** by the curve,the dynamic change of correlation strength between APOE and its co-expressed genes is shown clearly during the development of AD(from stage of control to incipient,moderate and severe). From the curve,the following gene feature is observed:the correlation strength between APOE and its co-expressed genes does not change significantly from control to moderate stage,but at last stage,it becomes *** feature agrees with the medical fact that,the symptom of AD is not significant in early stages and at last stage it's heavy.

关键词： Alzheimer's Disease Gene APOE dna microarray data Gene Order

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Classification of dna microarray for Cancer Diagnosis Using Complex Network

Classification of DNA Microarray for Cancer Diagnosis Using ...

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2010 2nd International Conference on Intellectual Technology in Industrial Practice (ITIP2010)

作者： Peng Wu and Zhen Liu School of Information Science and Engineering University of Jinan Jinan, P.R.China

Accurate cancer diagnosis can be achieved by performing microarray expression data classification. Various technology, including Support Vector Machines (SVMs), K Nearest Neighbor method (KNN), Neuro-Fuzzy models(NF), Neural Network (NN), etc. have been applied to analyze microarray expression data. In this paper, Complex Network based on genetic programming and particle swarm optimization is proposed for analyzing microarray expression data. We propose an automatic method for constructing and evolving our complex network model. The structure of complex network is evolved using genetic programming, and the fine tuning of the parameters encoded in the structure is accomplished using particle swarm optimization algorithm. The relative performances of our model are reported. The results are comparable to those previously obtain.

关键词： dna microarray data complex network model particle swarm optimization algorithm genetic programming

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Using Two Tools to Calculate Gene Order of Alzheimer Disease and The Comparison of Their Performance

Using Two Tools to Calculate Gene Order of Alzheimer Disease...

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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,China Gang Jiang Group of Gene Computation,College of Mathematics and Software Science,Sichuan Normal University,Chengdu,China Chao-Yang Pang Group of Gene Computation,Key Lab.Of Visual Computation and Virtual Reality of Sichuan Province,Chengdu,China Group of Gene Computation,College of Mathematics and Software Science,Sichuan Normal University,Chengdu,China

Alzheimer's disease(AD)is the most common form of dementia,the study of AD gene plays key *** study AD gene,analyzing dna microarray data becomes popular currently.A microarray data is an expression level of a gene,multiple expression levels which are generated under different experiment conditions form a vector and it contains the information of this *** two vectors hold small distance value,the two corresponding genes are *** similar genes together is an essential step of gene study,and optimal gene order is the global optimal clustering on theory. Here,optimal gene order is the shortest route of traveling salesman problem(TSP)if all genes are characterized as virtual cities and their positions are defined as the vectors consisting of multiple expression *** there are two main computation tools of gene order,which are Genetic Algorithm(GA)and Ant Colony Optimization(ACO).In this paper,GA,ACO and an improved GA(IGA)are applied to calculate AD gene order,their performance are compared. Through the comparison,the following properties are discovered:ACO fits AD gene best to calculate gene order;Different distance formulas generate different quality of gene order for both GAs(GA and IGA)and ACO.

关键词： Alzheimer's Disease Gene dna microarray data Gene Order

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A Study of Network-based Approach for Cancer Classification

A Study of Network-based Approach for Cancer Classification

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International Conference on Information Management and Engineering

作者： Jumali, R. Deris, S. Hashim, S. Z. M. Misman, M. F. Mohamad, M. S. Univ Teknol Malaysia Fac Comp Sci & Informat Syst Dept Software Engn Skudai Johor Malaysia

ISBN: (纸本)9780769535951

The advent of high-throughput techniques such as microarray data enabled researchers to elucidate process in a cell that fruitfully useful for pathological and medical. For such opportunities, microarray gene expression data have been explored and applied for various types of studies e.g. gene association, gene classification and construction of gene network. Unfortunately, since gene expression data naturally have a few of samples and thousands of genes, this leads to a biological and technical problems. Thus, the availability of artificial intelligence techniques couples with statistical methods can give promising results for addressing the problems. These approaches derive two well known methods: supervised and unsupervised. Whenever possible, these two superior methods can work well in classification and clustering in term of class discovery and class prediction. Significantly, in this paper we will review the benefit of network-based in term of interaction data for classification in identification of class cancer.

关键词： classification dna microarray data interaction gene

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Gene association study with SVM,MLP and cross-validation for the diagnosis of diseases

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Progress in Natural Science:Materials International 2008年第6期18卷 741-750页

作者： Junying Zhanga,*,Shenling Liu a,Yue Wang b aSchool of Computer Science and Engineering,Xidian University,161 Postbox,Xi’an 710071,China bDepartment of Electrical and Computer Engineering,Virginia Polytechnic Institute and State University,Alexandria,VA 22314,USA School of Computer Science and Engineering Xidian University 161 Postbox Xi’an 710071 China Department of Electrical and Computer Engineering Virginia Polytechnic Institute and State University Alexandria VA 22314 USA

Gene association study is one of the major challenges of biochip technology both for gene diagnosis where only a gene subset is responsible for some diseases,and for the treatment of the curse of dimensionality which occurs especially in dna microarray datasets where there are more than thousands of genes and only a few number of experiments(samples).This paper presents a gene selection method by training linear support vector machine(SVM)/nonlinear MLP(multilayer perceptron)classifiers and testing them with cross-validation for finding a gene subset which is optimal/suboptimal for the diagnosis of binary/multiple disease *** are selected with linear SVM classifier for the diagnosis of each binary disease types pair and tested by leave-one-out cross-validation;then,genes in the gene subset initialized by the union of them are deleted one by one by removing the gene which brings the greatest decrease of the generalization power,for samples,on the gene subset after removal,where generalization is measured by training MLPs with leave-one-out and leave-four-out *** proposed method was tested with experiments on real dna microarray MIT data and NCI *** result shows that it outperforms conventional SNR method in the separability of the data with expression levels on selected *** real dna microarray MIT/NCI data,which is composed of 7129/2308 eective genes with only 72/64 labeled sam-ples belonging to 2/4 disease classes,only 11/6 genes are selected to be diagnostic *** selected genes are tested by the classification of samples on these genes with SVM/MLP with leave-one-out/both leave-one-out and leave-four-out *** result of no misclassification indicates that the selected genes can be really considered as diagnostic genes for the diagnosis of the corresponding diseases.

关键词： dna microarray data Curse of dimensionality Gene selection Diagnostic genes SVM MLP Cross-validation

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Graph-based consensus clustering for class discovery from gene expression data

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BIOINFORMATICS 2007年第21期23卷 2888-2896页

作者： Yu, Zhiwen Wong, Hau-San Wang, Hongqiang City Univ Hong Kong Dept Comp Sci Kowloon Hong Kong Peoples R China

Motivation: Consensus clustering, also known as cluster ensemble, is one of the important techniques for microarray data analysis, and is particularly useful for class discovery from microarray data. Compared with traditional clustering algorithms, consensus clustering approaches have the ability to integrate multiple partitions from different cluster solutions to improve the robustness, stability, scalability and parallelization of the clustering algorithms. By consensus clustering, one can discover the underlying classes of the samples in gene expression data. Results: In addition to exploring a graph-based consensus clustering (GCC) algorithm to estimate the underlying classes of the samples in microarray data, we also design a new validation index to determine the number of classes in microarray data. To our knowledge, this is the first time in which GCC is applied to class discovery for microarray data. Given a pre specified maximum number of classes (denoted as K-max in this article), our algorithm can discover the true number of classes for the samples in microarray data according to a new cluster validation index called the Modified Rand Index. Experiments on gene expression data indicate that our new algorithm can (i) outperform most of the existing algorithms, (ii) identify the number of classes correctly in real cancer datasets, and (iii) discover the classes of samples with biological meaning.

关键词： dna microarray data BIO-MOLECULAR data CLASSIFICATION PREDICTION CANCER RELIABILITY STABILITY ENSEMBLES

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