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第一届最优化与系统生物学国际研讨会

作者： Wai-Ki Ching Kwai-Wa Cheng Li-Min Li Nam-Kiu Tsing Alice S. Wong Advanced Modeling and Applied Computing Laboratory Department of MathematicsThe University of Hong Kong Anderson Cancer Center The University of Texas Department of Zoology The University of Hong Kong

Missing values often exist in the data of gene expression microarray experiments. A number of methods such as the Row Average (RA) method, KNNimpute algorithm and SVDimpute algorithm have been proposed to estimate the missing values. Recently, Kim et al. proposed a Local Least Squares Imputation (LLSI) method for estimating the missing values. In this paper, we propose a Weighted Local Least Square Imputation (WLLSI) method for missing values estimation. WLLSI allows training on the weighting and therefore can take advantage of both the LLSI method and the RA method. Numerical results on both synthetic data and real microarray data are given to demonstrate the effectiveness of our proposed method. The imputation methods are then applied to a breast cancer dataset.

关键词： Missing values microarray data row average method local least squares imputation method weighted local least squares imputation method.

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Two Extensions to Multi-label Correlation-Based Feature Selection: a case study in bioinformatics

Two Extensions to Multi-label Correlation-Based Feature Sele...

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IEEE International Conference on Systems, Man, and Cybernetics

作者： Suwimol Jungjit M. Michaelis Alex A. Freitas J. Cinatl School of Computing University of Kent Canterbury CT2 7NF UK School of Biosciences University of Kent Canterbury CT2 7NJ UK Institut fuer Medizinische Virologie Klinikum der Goethe-Universitaet Paul Ehrlich-Str. 40 60596 Frankfurt am Main Germany

ISBN: (纸本)9781479906505

This paper proposes two extensions to a Multi-Label Correlation Based Feature Selection Method (ML-CFS): (1) ML-CFS using the absolute value of the correlation coefficient in the equation for evaluating a candidate feature subset, and (2) ML-CFS using Mutual Information for class label weighting. These extensions are evaluated in a bioinformatics case study addressing the multi-label classification of a cancer-related DNA microarray dataset with over 20,000 features. The results show that ML-CFS with absolute value of correlation obtained a significantly better predictive accuracy (smaller hamming loss) than the original ML-CFS. On the other hand, using Mutual Information to assign weights to labels showed some positive effect when using the ML-RBF classifier, but it showed a negative effect when using the ML-kNN classifier.

关键词： multi-label feature selection multi-label classification microarray data Feature extraction Bioinformatics Absolute value correlation coefficient (r) Case studies Classifiers

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Accelerating Incremental Wrapper based Gene Selection with K-Nearest-Neighbor

Accelerating Incremental Wrapper based Gene Selection with K...

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IEEE International Conference on Bioinformatics and Biomedicine

作者： Aiguo Wang Ning An Guilin Chen Lian Li Gil Alterovitz The Gerontechnology Lab School of Computer and Information Hefei University of Technology School of Computer and Information Engineering Chuzhou University Center for Biomedical Informatics Harvard Medical School

ISBN: (纸本)9781479956708

Wrapper based gene selection methods tend to obtain better classification accuracy than filter methods, while it is much more time consuming. Accelerating this process without degrading the high accuracy is of great value for researchers to better analyze gene expression profiles. In this paper, we explore to reduce the time complexity of wrapper based gene selection method with K-Nearest-Neighbor (KNN) classifier embedded. Instead of taking KNN as a black box, we incrementally construct and maintain a classifier distance matrix to speed up the gene selection process. Experiments on six publicly available microarrays were first conducted to show the effectiveness of incremental wrapper based gene selection method with KNN. Then, to demonstrate the performance gain in time cost reduction, we analyzed the time complexity and experimentally evaluated it. Both theoretical analysis and experimental results prove that the proposed approach greatly accelerates the gene selection process without degrading the classification accuracy.

关键词： microarray data Gene selection K-nearest-neighbor Wrapper Filter

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A Integrated Computational Approach for Protein Sub-network Detection in Parkinson's Disease

A Integrated Computational Approach for Protein Sub-network ...

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2010 3rd International Conference on Computer and Electrical Engineering(ICCEE 2010)

作者： Yue Huang Yunying Huang Institute of Signal and Information Processing Department of Communication Engineering Xiamen University Department of Electronic Engineering Xiamen University

Parkinson's disease (PD) is a typical case of neurodegenerative disorder, which often impairs the sufferer's motor skills, speech, and other functions. Combination of protein-protein interaction (PPI) network analysis and gene expression studies provides a better insight of Parkinson's disease. A computational approach was developed in our work to identify protein signal network in PD study. First, a linear regression model is setup and then a network-constrain regularization analysis was applied to microarray data from transgenic mouse model with Parkinson's disease. Then protein network was detected based on an integer linear programming model by integrating microarray data and PPI database.

关键词： Parkinson’s disease microarray data linear regression model integer linear programming protein network detection

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A Novel SVM-RFE for Gene Selection

A Novel SVM-RFE for Gene Selection

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第三届最优化与系统生物学国际研讨会

作者： Jun-Yan Tan Zhi-Xia Yang Naiyang Deng College of Science China Agricultural University College of Mathematics and Systems Science Xinjiang University Academy of Mathematics and Systems Science CAS

Selecting a subset of informative genes from microarray expression data is a critical data preparation step in cancer classification and other biological function *** support vector machine recursive feature elimination(SVM-RFE) is one of the most effective feature selection method which has been successfully used in selecting informative genes for cancer classification. While,the SVM-RFE selects genes only using the gene expression data without using any other biological information of the *** on the biology information of the genes,it may be beneficial to identify the genes that are relevant to the *** propose a novel SVM-RFE method for gene selection by incorporating the Kyoto Encyclopedia of genes and genomes(KEGG) pathway information into feature selection *** results indicate that the novel SVM-RFE tends to provide better variable selection results than the SVM-RFE.

关键词： Support vector machine microarray data gene selection

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Fuzzy rule based unsupervised approach for gene saliency

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BMC BIOINFORMATICS 2009年第Sup7期10卷 1-1页

作者： Verma, Nishchal K. Agrawal, Pooja Cui, Yan Univ Tennessee Ctr Integrat & Translat Gen Dept Mol Sci Memphis TN 38163 USA

An abstract of a study related to unsupervised approach on gene saliency based on the fuzzy rule, which was conducted by Nishchal K. Verma, Pooja Agrawal, and Yan Cui, is presented.

关键词： microarray data Fuzzy Rule Classifier Performance Ranking Method Class Information

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Enhanced cancer subtyping via pan-transcriptomics data fusion, Monte-Carlo consensus clustering, and auto classifier creation 19

Enhanced cancer subtyping via pan-transcriptomics data fusio...

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Proceedings of the Tenth International Conference on Computational Systems-Biology and Bioinformatics

作者： Kristofer Linton-Reid Harry Clifford Joe Sneath Thompson Imperial College London South Kensington London United Kingdom Cambridge Cancer Genomics Cambridge United Kingdom

ISBN: (纸本)9781450372152

Subtyping of tumor transcriptome expression profiles is a routine method used to distinguish tumor heterogeneity. Unsupervised clustering techniques are often combined with survival analysis to decipher the relationship between genes and the survival times of patients. However, the reproducibility of these subtyping based studies is poor. There are multiple reports which have conflicting subtype and gene-survival time relationship results. In this study, we introduce the issues underlying the lack of reproducibility in transcriptomic subtyping studies. This problem arises from the routine analysis of small cohorts (< 100 individuals) and use of biased traditional consensus clustering techniques. Our approach carefully combines multiple RNA-sequencing and microarray datasets, followed by subtyping via Monte-Carlo Consensus Clustering and creation of deep subtyping classifiers. This paper demonstrates an improved subtyping methodology by investigating pancreatic ductal adenocarcinoma. Importantly, our methodology identifies six biologically novel pancreatic ductal adenocarcinoma subtypes. Our approach also enables a degree of reproducibility, via our pancreatic ductal adenocarcinoma classifier PDACNet, which classical subtyping studies have failed to establish.

关键词： consensus clustering cancer subtyping RNA-sequencing data microarray data pancreatic cancer gene expression

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Using partially ordered sets to represent and predict true patterns of gene response to treatments

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BMC BIOINFORMATICS 2013年第17-Sup期14卷 A20-A20页

作者： Vo, Nam S. Vinhthuy Phan Univ Memphis Dept Comp Sci Memphis TN 38152 USA

Doc number: A20

关键词： Pairwise Comparison Expression data Large Sample Size microarray data Gene Expression Level

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A systems biology approach to construct the gene regulatory network of systemic inflammation via microarray and databases mining

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BMC MEDICAL GENOMICS 2008年第1期1卷 46-46页

作者： Chen, Bor-Sen Yang, Shih-Kuang Lan, Chung-Yu Chuang, Yung-Jen Natl Tsing Hua Univ Dept Elect Engn Lab Control & Syst Biol Hsinchu 300 Taiwan Natl Tsing Hua Univ Dept Life Sci Hsinchu 300 Taiwan

Background: Inflammation is a hallmark of many human diseases. Elucidating the mechanisms underlying systemic inflammation has long been an important topic in basic and clinical research. When primary pathogenetic events remains unclear due to its immense complexity, construction and analysis of the gene regulatory network of inflammation at times becomes the best way to understand the detrimental effects of disease. However, it is difficult to recognize and evaluate relevant biological processes from the huge quantities of experimental data. It is hence appealing to find an algorithm which can generate a gene regulatory network of systemic inflammation from high-throughput genomic studies of human diseases. Such network will be essential for us to extract valuable information from the complex and chaotic network under diseased conditions. Results: In this study, we construct a gene regulatory network of inflammation using data extracted from the Ensembl and JASPAR databases. We also integrate and apply a number of systematic algorithms like cross correlation threshold, maximum likelihood estimation method and Akaike Information Criterion (AIC) on time-lapsed microarray data to refine the genome-wide transcriptional regulatory network in response to bacterial endotoxins in the context of dynamic activated genes, which are regulated by transcription factors (TFs) such as NF-kappa B. This systematic approach is used to investigate the stochastic interaction represented by the dynamic leukocyte gene expression profiles of human subject exposed to an inflammatory stimulus (bacterial endotoxin). Based on the kinetic parameters of the dynamic gene regulatory network, we identify important properties (such as susceptibility to infection) of the immune system, which may be useful for translational research. Finally, robustness of the inflammatory gene network is also inferred by analyzing the hubs and "weak ties" structures of the gene network. Conclusion: In this study, Da

关键词： microarray data Akaike Information Criterion Gene Network Gene Regulatory Network Candidate Regulator

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Identification of ESCC Potential Biomarkers using Biclustering Algorithms

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GENE REPORTS 2022年 27卷

作者： Baruah, Bikash Dutta, Manash P. Bhattacharyya, Dhruba K. NIT Arunachal Pradesh Dept Comp Sci & Engn Jote India Tezpur Univ Dept Comp Sci & Engn Tezpur Assam India

An extensive empirical study is presented in this work to identify potential biomarkers of ESCC by employing fifteen prominent biclustering algorithms on synthetic and real datasets. For systematic analyses, we implement the algorithms on a variety of synthetic datasets and evaluate the quality of biclusters using recovery and relevance scores. The biclustering algorithms showing adequate results on synthetic datasets are implemented on real ESCC microarray dataset of both normal and disease samples separately. Gene enrichment analysis has been carried out to recognize the best possible bicluster(s) of individual algorithms. Our approach exploits the set of best possible biclusters in the downstream analysis towards the identification of the potential biomarkers with reference to a set of established elite genes for ESCC. Our approach depends on Pearson correlation, conversion of floating valued correlation matrix into a binary matrix, degree analysis based on elite genes, deviation of degree in their respective mapping bicluster, significant alteration of gene expression values while transitioning from normal to disease conditions, and gene ontology and pathway analyses. Finally, we detect 9 ESCC potential biomarker genes;SH3GLB1, ARPC2, APPL1, CALM1, FTL, LPAR1, PLAU, PSMB4, and SCP2;which shows the topological as well as biological significance of ESCC elite genes.

关键词： Bicluster Biclustering algorithm Elite gene microarray data Synthetic dataset Biomarker

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