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Estimating prediction error in microarray classification: Modifications of the 0.632+ bootstrap when n <p

CANADIAN JOURNAL OF STATISTICS-REVUE CANADIENNE DE STATISTIQUE 2013年第1期41卷 133-150页

作者： Jiang, Wenyu Chen, Bingshu E. Queens Univ Dept Math & Stat Kingston ON K7L 3N6 Canada Queens Univ NCIC Clin Trials Grp Kingston ON K7L 3N6 Canada Queens Univ Dept Community Hlth & Epidemiol Kingston ON K7L 3N6 Canada

We are interested in estimating prediction error for a classification model built on high dimensional genomic data when the number of genes (p) greatly exceeds the number of subjects (n). We examine a distance argument supporting the conventional 0.632+ bootstrap proposed for the $n > p$ scenario, modify it for the $n < p$ situation and develop learning curves to describe how the true prediction error varies with the number of subjects in the training set. The curves are then applied to define adjusted resampling estimates for the prediction error in order to achieve a balance in terms of bias and variability. The adjusted resampling methods are proposed as counterparts of the 0.632+ bootstrap when $n < p$, and are found to improve on the 0.632+ bootstrap and other existing methods in the microarray study scenario when the sample size is small and there is some level of differential expression. The Canadian Journal of Statistics 41: 133150;2013 (c) 2012 Statistical Society of Canada

关键词： Bootstrap 0.632+ bootstrap class prediction cross-validation feature selection learning curve microarray data prediction error

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A Positive False Discovery Rate Convergence Result

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COMMUNICATIONS IN STATISTICS-THEORY AND METHODS 2013年第23期42卷 4239-4246页

作者： Melnykov, Igor Colorado State Univ Dept Math & Phys Pueblo CO 81001 USA

The positive false discovery rate (pFDR) is the average proportion of false rejections given that the overall number of rejections is greater than zero. Assuming that the proportion of true null hypotheses, proportion of false positives, and proportion of true positives all converge pointwise, the pFDR converges to a continuous limit uniformly over all significance levels. We are showing that the uniform convergence still holds given a weaker assumption that the proportion of true positives converges in L-1.

关键词： Differentially expressed genes FDR microarray data Multiple testing pFDR

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Detecting Dense Subgraphs in Complex Networks Based on Edge Density Coefficient

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CHINESE JOURNAL OF ELECTRONICS 2013年第3期22卷 517-520页

作者： Guan Bo Zan Xiangzhen Xiao Biyu Ma Runnian Zhang Fengyue Liu Wenbin Ningbo Univ Technol Coll Electron & Informat Engn Ningbo 315016 Zhejiang Peoples R China Wenzhou Univ Dept Phys & Elect Informat Engn Wenzhou 325035 Zhejiang Peoples R China Air Force Engn Univ Telecommun Inst Xian 710077 Peoples R China Beijing Inst Technol Sch Life Sci & Technol Dept Biomed Engn Beijing 100081 Peoples R China

Densely connected patterns in biological networks can help biologists to elucidate meaningful insights. How to detect dense subgraphs effectively and quickly has been an urgent challenge in recent years. In this paper, we proposed a local measure named the edge density coefficient, which could indicate whether an edge locates a dense subgraph or not. Simulation results showed that this measure could improve both the accuracy and speed in detecting dense subgraphs. Thus, the G-N algorithm can be extended to large biological networks by this local measure. Finally, we applied this algorithm to microarray data sets of Saccharomyces cerevisiae, and performed the gene ontology analysis of the result by the GOEAST.

关键词： Complex network Dense subgraph microarray data

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PCA consistency for the power spiked model in high-dimensional settings

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JOURNAL OF MULTIVARIATE ANALYSIS 2013年 122卷 334-354页

作者： Yata, Kazuyoshi Aoshima, Makoto Univ Tsukuba Inst Math Ibaraki 3058571 Japan

In this paper, we propose a general spiked model called the power spiked model in high-dimensional settings. We derive relations among the data dimension, the sample size and the high-dimensional noise structure. We first consider asymptotic properties of the conventional estimator of eigenvalues. We show that the estimator is affected by the high-dimensional noise structure directly, so that it becomes inconsistent. In order to overcome such difficulties in a high-dimensional situation, we develop new principal component analysis (PCA) methods called the noise-reduction methodology and the cross-data-matrix methodology under the power spiked model. We show that the new PCA methods can enjoy consistency properties not only for eigenvalues but also for PC directions and PC scores in high-dimensional settings. (C) 2013 Elsevier Inc. All rights reserved.

关键词： Cross-data-matrix methodology HDLSS Large p small n microarray data Noise-reduction methodology

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Semantic Subgroup Discovery Systems and Workflows in the SDM-Toolkit

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COMPUTER JOURNAL 2013年第3期56卷 304-320页

作者： Vavpetic, Anze Lavrac, Nada Jozef Stefan Inst Ljubljana 1000 Slovenia Univ Nova Gorica Nova Gorica Slovenia

This paper addresses semantic data mining, a new data mining paradigm in which ontologies are exploited in the process of data mining and knowledge discovery. This paradigm is introduced together with new semantic subgroup discovery systems SDM-search for enriched gene sets (SEGS) and SDM-Aleph. These systems are made publicly available in the new SDM-Toolkit for semantic data mining. The toolkit is implemented in the Orange4WS data mining platform that supports knowledge discovery workflow construction from local and distributed data mining services. On the basis of the experimental evaluation of semantic subgroup discovery systems on two publicly available biomedical datasets, the paper results in a thorough quantitative and qualitative evaluation of SDM-SEGS and SDM-Aleph and their comparison with SEGS, a system for enriched gene set discovery from microarray data.

关键词： semantic data mining relational data mining inductive logic programming domain knowledge subgroup discovery ontologies microarray data

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Prediction of core cancer genes using multi-task classification framework

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JOURNAL OF THEORETICAL BIOLOGY 2013年 317卷 62-70页

作者： Gao, Shan Xu, Shuo Fang, Yaping Fang, Jianwen Univ Kansas Appl Bioinformat Lab Lawrence KS 66047 USA Inst Sci & Tech Informat China Beijing 100038 Peoples R China

Cancer is deemed as a highly heterogeneous disease specific to cell type and tissue origin. All cancers, however, share a common pathogenesis. Therefore, it is widely believed that cancers may share common mechanisms. In this study, we introduce a novel strategy based on multi-tasking learning methods to predict core cancer genes shared by multiple cancers in the hope of elucidating common cancer mechanisms. Our strategy uses two multi-tasking learning algorithms, one for feature selection and the other for validation of selected features. The combined use of two methods results in more robust classifiers and reliable selected features. The top 73 significant features, mapped to 72 genes, are selected as core cancer genes. The effectiveness of the 73 features is further demonstrated in a blind test conducted on an independent test data. The biological significance of these genes is evaluated using systems biology analyses. Extensive functional, pathway and network analysis confirms findings in previous studies and brings new insights into common cancer mechanisms. Our strategy can be used as a general method to find important genes from large gene expression datasets on the genomic level. The selected genes can be used to predict cancers. (C) 2012 Elsevier Ltd. All rights reserved.

关键词： Multi-task learning Classification Core cancer genes Gene differential expression microarray data

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MicroRNA-mRNA interaction network using TSK-type recurrent neural fuzzy network

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GENE 2013年第2期515卷 385-390页

作者： Vineetha, S. Bhat, C. Chandra Shekara Idicula, Sumam Mary Govt Engn Coll Dept Comp Sci Idukki Kerala India Natl Inst Interdisciplinary Sci & Technol Trivandrum Kerala India Cochin Univ Sci & Technol Cochin 682016 Kerala India

MicroRNAs are short non-coding RNAs that can regulate gene expression during various crucial cell processes such as differentiation, proliferation and apoptosis. Changes in expression profiles of miRNA play an important role in the development of many cancers, including CRC. Therefore, the identification of cancer related miRNAs and their target genes are important for cancer biology research. In this paper, we applied TSK-type recurrent neural fuzzy network (TRNFN) to infer miRNA-mRNA association network from paired miRNA, mRNA expression profiles of CRC patients. We demonstrated that the method we proposed achieved good performance in recovering known experimentally verified miRNA-mRNA associations. Moreover, our approach proved successful in identifying 17 validated cancer miRNAs which are directly involved in the CRC related pathways. Targeting such miRNAs may help not only to prevent the recurrence of disease but also to control the growth of advanced metastatic tumors. Our regulatory modules provide valuable insights into the pathogenesis of cancer. (C) 2012 Elsevier B.V. All rights reserved.

关键词： MicroRNA microarray data MicroRNA-mRNA Interaction Network TSK-type recurrent neural fuzzy network Fuzzy logic

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New network topology approaches reveal differential correlation patterns in breast cancer

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BMC SYSTEMS BIOLOGY 2013年第1期7卷 1-14页

作者： Bockmayr, Michael Klauschen, Frederick Gyoerffy, Balazs Denkert, Carsten Budczies, Jan Charite Inst Pathol D-10117 Berlin Germany Hungarian Acad Sci Joint Res Lab H-1083 Budapest Hungary Semmelweis Univ Semmelweis Univ Dept Pediat 1 H-1083 Budapest Hungary

Background: Analysis of genome-wide data is often carried out using standard methods such as differential expression analysis, clustering analysis and heatmaps. Beyond that, differential correlation analysis was suggested to identify changes in the correlation patterns between disease states. The detection of differential correlation is a demanding task, as the number of entries in the gene-by-gene correlation matrix is large. Currently, there is no gold standard for the detection of differential correlation and statistical validation. Results: We developed two untargeted algorithms (DCloc and DCglob) that identify differential correlation patterns by comparing the local or global topology of correlation networks. Construction of networks from correlation structures requires fixing of a correlation threshold. Instead of a single cutoff, the algorithms systematically investigate a series of correlation thresholds and permit to detect different kinds of correlation changes at the same level of significance: strong changes of a few genes and moderate changes of many genes. Comparing the correlation structure of 208 ER-breast carcinomas and 208 ER+ breast carcinomas, DCloc detected 770 differentially correlated genes with a FDR of 12.8%, while DCglob detected 630 differentially correlated genes with a FDR of 12.1%. In two-fold cross-validation, the reproducibility of the list of the top 5% differentially correlated genes in 140 ER-tumors and in 140 ER+ tumors was 49% for DCloc and 33% for DCglob. Conclusions: We developed two correlation network topology based algorithms for the detection of differential correlations in different disease states. Clusters of differentially correlated genes could be interpreted biologically and included the marker genes hydroxyprostaglandin dehydrogenase (PGDH) and acyl-CoA synthetase medium chain 1 (ACSM1) of invasive apocrine carcinomas that were differentially correlated, but not differentially expressed. Using random subsampling and c

关键词： Differential correlation microarray data Breast cancer Molecular subtypes Differential co-expression

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Finding Marker Genes from High Dimensional Expression Profiles: Divide-and-conquer Exploiting a Fuzzy Rule Based Framework

Finding Marker Genes from High Dimensional Expression Profil...

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IEEE International Conference on Fuzzy Systems (FUZZ)

作者： Huang, Sheng-Yao Chen, Yi-Cheng Chung, I-Fang Yang, Feng-Yi Su, Chun-Hung Natl Yang Ming Univ Inst Biomed Informat Taipei 112 Taiwan Natl Yang Ming Univ Dept Biomed Imaging & Radiol Sci Taipei Taiwan Biodivers Res Ctr Acad Sinica Taipei Taiwan

ISBN: (纸本)9781479900206

Previously we have developed a feature selection mechanism (Fuzzy Systems - Feature Attenuating Gates, FS-FAG), which cannot deal with very high dimensional data in an efficient manner. To address this issue, in this study we introduce a divide-and-conquer strategy for selection of marker genes for high dimensional cancer microarray data. This is a hierarchical system, which can be used with very high dimensional data. To demonstrate the effectiveness of the proposed scheme, we use two sets of microarray data under different experimental conditions. We examine the variations in the selected number of genes, the number of the final sets of marker genes, and the discriminating power of cancer subtypes of the selected marker genes. Experimental results demonstrate that proposed method indeed selects marker genes with good discriminating power for different cancer subtypes.

关键词： microarray data marker genes divide-and-conquer

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Multiple Gene Sets for Cancer Classification Using Gene Range Selection Based on Random Forest

Multiple Gene Sets for Cancer Classification Using Gene Rang...

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5th Asian Conference on Intelligent Information and database Systems (ACIIDS)

作者： Moorthy, Kohbalan Bin Mohamad, Mohd Saberi Deris, Safaai Univ Teknol Malaysia Fac Comp Sci & Informat Syst Artificial Intelligence & Bioinformat Res Grp Skudai 81310 Johor Malaysia

ISBN: (纸本)9783642365461

The advancement of microarray technology allows obtaining genetic information from cancer patients, as computational data and cancer classification through computation software, has become possible. Through gene selection, we can identify certain numbers of informative genes that can be grouped into a smaller sets or subset of genes;which are informative genes taken from the initial data for the purpose of classification. In most available methods, the amount of genes selected in gene subsets are dependent on the gene selection technique used and cannot be fine-tuned to suit the requirement for particular number of genes. Hence, a proposed technique known as gene range selection based on a random forest method allows selective subset for better classification of cancer datasets. Our results indicate that various gene sets assist in increasing the overall classification accuracy of the cancer related datasets, as the amount of genes can be further scrutinized to create the best subset of genes. Moreover, it can assist the gene-filtering technique for further analysis of the microarray data in gene network analysis, gene-gene interaction analysis and many other related fields.

关键词： Gene Selection Cancer Classification Random Forest Gene Expression microarray data

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