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A Modified Ant Colony Optimization Algorithm for Tumor Marker Gene Selection

Genomics, Proteomics & Bioinformatics 2009年第4期7卷 200-208页

作者： Hualong Yu Guochang Gu Haibo Liu Jing Shen Jing Zhao College of Computer Science and Technology Harbin Engineering University Harbin 150001 China.

microarray data are often extremely asymmetric in dimensionality, such as thousands or even tens of thousands of genes but only a few hundreds of samples or less. Such extreme asymmetry between the dimensionality of genes and samples can lead to inaccurate diagnosis of disease in clinic. Therefore, it has been shown that selecting a small set of marker genes can lead to improved classification accuracy. In this paper, a simple modified ant colony optimization （ACO） algorithm is proposed to select tumorelated marker genes, and support vector machine （SVM） is used as classifier to evaluate the performance of the extracted gene subset. Experimental results on several benchmark tumor microarray datasets showed that the proposed approach produces better recognition with fewer marker genes than many other methods. It has been demonstrated that the modified ACO is a useful tool for selecting marker genes and mining high dimension data

关键词： microarray data ant colony optimization marker gene selection support vector machine

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Learning undirected graphical models from multiple datasets with the generalized non-rejection rate

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INTERNATIONAL JOURNAL OF APPROXIMATE REASONING 2012年第9期53卷 1326-1335页

作者： Roverato, Alberto Castelo, Robert Univ Bologna Bologna Italy Univ Pompeu Fabra Barcelona Spain

Learning graphical models from multiple datasets constitutes an appealing approach to learn transcriptional regulatory interactions from microarray data in the field of molecular biology. This has been approached both in a model based learning approach and in a model free learning approach where, in the latter, it is common practice to pool datasets produced under different experimental conditions. In this paper, we introduce a quantity called the generalized non-rejection rate which extends the non-rejection rate, introduced by Castelo and Roverato [3] so as to explicitly keep into account the different graphical models representing distinct experimental conditions involved in the structure of the dataset produced in multiple experimental batches. We show that the generalized non-rejection rate allows one to learn the common edges occurring throughout all graphical models, making it specially suited to identify robust transcriptional interactions which are common to all the considered experiments. The generalized non-rejection rate is then applied to both synthetic and real data and shown to provide competitive performance with respect to other widely used methods. (C) 2012 Elsevier Inc. All rights reserved.

关键词： Graphical model microarray data Non-rejection rate Partial correlation Small-sample inference Multiple data sets

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Inferring gene regulatory networks using differential evolution with local search heuristics

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IEEE-ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2007年第4期4卷 634-647页

作者： Noman, Nasimul Iba, Hitoshi Univ Tokyo Grad Sch Frontier Sci Iba Lab Tokyo Japan Univ Dhaka Dept Comp Sci & Engn Dhaka 1000 Bangladesh

We present a memetic algorithm for evolving the structure of biomolecular interactions and inferring the effective kinetic parameters from the time-series data of gene expression using the decoupled S-system formalism. We propose an Information Criteria-based fitness evaluation for gene network model selection instead of the conventional Mean Squared Error (MSE)-based fitness evaluation. A hill-climbing local-search method has been incorporated in our evolutionary algorithm for efficiently attaining the skeletal architecture that is most frequently observed in biological networks. The suitability of the method is tested in gene circuit reconstruction experiments, varying the network dimension and/or characteristics, the amount of gene expression data used for inference, and the noise level present in expression profiles. The reconstruction method inferred the network topology and the regulatory parameters with high accuracy. Nevertheless, the performance is limited to the amount of expression data used and the noise level present in the data. The proposed fitness function has been found to be more suitable for identifying the correct network topology and for estimating the accurate parameter values compared to the existing ones. Finally, we applied the methodology for analyzing the cell-cycle gene expression data of budding yeast and reconstructed the network of some key regulators.

关键词： biology and genetics inverse problems global optimization medicine and science microarray data gene regulatory system transcriptional regulation memetic algorithm

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MODEL-BASED CLUSTERING WITH GENE RANKING USING PENALIZED MIXTURES OF HEAVY-TAILED DISTRIBUTIONS

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JOURNAL OF BIOINFORMATICS AND COMPUTATIONAL BIOLOGY 2013年第3期11卷 1-24页

作者： Cozzini, Alberto Jasra, Ajay Montana, Giovanni Univ London Imperial Coll Sci Technol & Med Dept Math London SW7 2AZ England Natl Univ Singapore Dept Stat & Appl Probabil Singapore 117546 Singapore

Cluster analysis of biological samples using gene expression measurements is a common task which aids the discovery of heterogeneous biological sub-populations having distinct mRNA profiles. Several model-based clustering algorithms have been proposed in which the distribution of gene expression values within each sub-group is assumed to be Gaussian. In the presence of noise and extreme observations, a mixture of Gaussian densities may over-fit and overestimate the true number of clusters. Moreover, commonly used model-based clustering algorithms do not generally provide a mechanism to quantify the relative contribution of each gene to the final partitioning of the data. We propose a penalized mixture of Student's t distributions for model-based clustering and gene ranking. Together with a resampling procedure, the proposed approach provides a means for ranking genes according to their contributions to the clustering process. Experimental results show that the algorithm performs well comparably to traditional Gaussian mixtures in the presence of outliers and longer tailed distributions. The algorithm also identifies the true informative genes with high sensitivity, and achieves improved model selection. An illustrative application to breast cancer data is also presented which confirms established tumor sub-classes.

关键词： Clustering mixture models skewed distributions microarray data gene selection

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Comparison of Binary Classification Based on Signed Distance Functions with Support Vector Machines

Comparison of Binary Classification Based on Signed Distance...

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4th Annual Ohio Collaborative Conference on Bioinformatics

作者： Boczko, Erik M. Xie, Minhui Wu, Di Young, Todd Ohio Univ Dept Math Athens OH 45701 USA Vanderbilt Univ Med Ctr Dept Biomed Informat Nashville TN 37235 USA Vanderbilt Univ Dept EE & CS Nashville TN USA

We compare methods based on the Signed Distance Function (SDF) a new tool for binary classification with standard Support Vector Machine (SVM) methods. We demonstrate on several sets of micro-array data that the perfo... 详细信息

ISBN: (纸本)9780769536859

关键词： Machine Learning microarray data

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Analyzing Genetic Factors Involved in Recombinant Protein Expression Enhancement

Analyzing Genetic Factors Involved in Recombinant Protein Ex...

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IEEE International Conference on Bioinformatics and Biomedicine Workshops (BIBMW)

作者： Johnson, Daniel Teoh, Keat Ashby, Cody Hood, Elizabeth Huang, Xiuzhen Arkansas State Univ Jonesboro AR 72467 USA

ISBN: (纸本)9781424483044

Understanding the genetic factors that promote recombinant protein accumulation in transgenic plants will provide insightful strategies for protein biofactory efficiency. Through combining biological and bioinformatics analysis, our work is to determine genetic and biological factors affecting increased protein accumulation of a bacterial cellulase enzyme in transgenic maize. microarray experiments were performed on maize near-isogenic lines that exhibit high and low accumulation of the enzyme expressed from a transgene in seeds. Through microarray data analysis, two thousand three hundred thirteen genes were identified which exhibited at least a 1.5-fold change in expression level. One hundred sixty-one genes from the data set are shown to be statistically valid. Of these, eighty-two genes are upregulated while seventy-nine genes are down-regulated. Preliminary functional analysis of the genes was conducted and several pathways of biological importance were tentatively identified. These genes code for four categories of proteins: known proteins, zein proteins, putative proteins and unknown proteins. Further functional clustering and annotation analysis for these genes will help construct and define networks of interaction as well as predict important metabolic pathways to understand the controlling mechanisms that lead to the hyper-accumulation phenomenon.

关键词： gene expression microarray data recombinant protein accumulation

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Extraction of essential features by quantum density

Extraction of essential features by quantum density

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Conference on Photonics Applications in Astronomy, Communications, Industry, and High-Energy Physics Experiments

作者： Wilinski, Artur Warsaw Univ Life Sci SGGW Nowoursynowska 159 PL-02776 Warsaw Poland

ISBN: (数字)9781510604865

ISBN: (纸本)9781510604858

In this paper we consider the problem of feature extraction, as an essential and important search of dataset. This problem describe the real ownership of the signals and images. Searches features are often difficult to identify because of data complexity and their redundancy. Here is shown a method of finding an essential features groups, according to the defined issues. To find the hidden attributes we use a special algorithm DQAL with the quantum density for the j-th features from original data, that indicates the important set of attributes. Finally, they have been generated small sets of attributes for subsets with different properties of features. They can be used to the construction of a small set of essential features. All figures were made in Matlab(6).

关键词： feature selection pattern recognition data reduction microarray data quantum density relevant set

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Review on Feature Selection Methods for Gene Expression data Classification 4th

Review on Feature Selection Methods for Gene Expression Data...

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4th International Conference of Reliable Information and Communication Technology (IRICT)

作者： Almutiri, Talal Saeed, Faisal Taibah Univ Coll Comp Sci & Engn Medina Saudi Arabia

ISBN: (纸本)9783030335823;9783030335816

microarray technology makes it easier for scientists to rapidly measure thousands of gene's expression levels. By analyzing these data, we can find the altered genes, thereby facilitating easy diagnosis and classification of the genetic-related diseases. However, predicting and identifying cancer types is a great challenge in the medical field. Gene expression microarray contains information that can help in this regard, but microarray data have high dimensionality problem which means a large number of genes or features and a small number of samples, also there are redundant and irrelevant features that increase the challenge of microarray analysis. This study reviewed recent studies about methods, algorithms, and limitations of feature selection for microarray gene expression classification. This study compared and focused on four aspects for each related study: datasets, feature selection methods, classifiers, and accuracy results. Feature selection methods are considered as a pre-processing step which plays a vital role in the effectiveness of a classification. This paper showed that applying filter methods such as t-Test, Pearson's Correlation Coefficient (PCC), and Bhattacharyya distance eliminate irrelevant features that help to increase classification performance and accuracy. Therefore, applying wrapper or embedded methods such as Genetic Algorithm (GA) without applying filter methods in advance could affect the effectiveness of a classification negatively.

关键词： Cancer classification Gene expression Feature selection microarray data

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Gene Clustering with Partition Around Mediods Algorithm Based on Weighted and Normalized Mahalanobis Distance 2

Gene Clustering with Partition Around Mediods Algorithm Base...

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2nd International Conference on Intelligent Informatics and Biomedical Sciences (ICIIBMS)

作者： Najat, Naween Abdulazeez, Adnan Mohsin Univ Zakho Comp Sci Dept Zakho Iraq Duhok Polytech Univ Duhok Iraq

ISBN: (纸本)9781509066643

The partition around medoids (PAM) algorithm is a robust and flexible unsupervised learning algorithm that depends on the underlying distance and default distance metric is Euclidean distance. The PAM algorithm is more efficient than K-Mean since medoids assess a minimum distance from the other objects. In this study, we have integrated the Mahalanobis distance with PAM algorithm, since Mahalanobis distance has been defined in cluster analysis for different applications and is used to overcome the problem of scaling and correlation with Euclidean distance. However, the performance of PAM algorithm based on Mahalanobis distance was found to be inadequate when employed on selected microarray expression datasets, which were pre-processed prior to the analyses. We proposed an enhanced PAM algorithm based on the weighted and normalized Mahalanobis distance, and the results obtained using our proposed algorithm reveal an ultimate cluster solution for selected microarray datasets. The algorithms were evaluated using the Dunn's validity index.

关键词： microarray data Partition Around Medoids Mahalanobis distance Normalised Weighted Mahalanobis distance validity index pre-processing number of clusters

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Computational Approach to Detect the Culprit Genes Responsible for a Disease

Computational Approach to Detect the Culprit Genes Responsib...

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International Symposium on Advanced Computing and Communication (ISACC)

作者： War, Daphne Kordor Saha, Goutam North Eastern Hill Univ NEHU IT Dept Shillong Meghalaya India

ISBN: (纸本)9781467367080

Computational exploration of the identity of genes which may be responsible for a particular disease is a very exhaustive study nowadays. This uses the datasets available in different websites which store a tremendous amount of genetic information in the form of microarray data. In this paper, we have presented a reliable and effective approach for unveiling the smallest possible set of genes which is associated with even a quite poor prognosis disease like Alzheimer Disease. The dataset used here has been collected from the official website of NCBI. We have used rough set theory, random forest, and principal component analysis or their collective form for the said purpose. The maximum accuracy achievable here for the purpose of diagnosis is quite satisfactory. Further, we have verified our result from DAVID ontological website where it has been found that most of the genes extracted computationally are really associated with Alzheimer's disease.

关键词： Alzheimer's disease data mining microarray data principal component analysis randomforest rough set theory

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