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Identification of human papillomavirus status specific biomarker in head and neck cancer

HEAD AND NECK-JOURNAL FOR THE SCIENCES AND SPECIALTIES OF THE HEAD AND NECK 2015年第9期37卷 1310-1318页

作者： Kim, Ki-Yeol Zhang, Xianglan Cha, In-Ho Yonsei Univ Coll Dent Oral Canc Res Inst Seoul 120752 South Korea Yanbian Univ Dept Pathol Coll Med Yanji Jilin Province Peoples R China Yonsei Univ Dept Oral & Maxillofacial Surg Coll Dent Seoul 120752 South Korea

BackgroundIn an attempt to improve the prognosis of patients with head and neck cancer, it could be necessary to identify biomarkers in homogeneous subgroups of patients with head and neck cancer. MethodsWe explored the necessity of human papillomavirus (HPV) status-specific biomarkers in patients with head and neck cancer and identified an HPV status-specific gene set. The identified gene set was validated with the external microarray datasets. For this purpose, we used 4 microarray datasets obtained from a public database. One dataset was used for identifying the HPV status specific gene set, and the other datasets were used for validation. ResultsThe identified gene set for distinguishing head and neck cancer from head and neck normal, did not accurately discriminate the HPV specific patients with head and neck cancer from the head and neck normal group. Also, it was demonstrated that some HPV-positive (HPV+) head and neck cancer-specific genes may not be significant in HPV-negative (HPV-) head and neck cancer patients. The identified HPV specific gene sets showed good performance in validation datasets. The HPV+ head and neck cancer-specific gene set included various metabolism-related pathways, and the HPV- head and neck cancer-specific gene set included several pathways;fibroblast, collagen degradation, actin-binding, and actin cytoskeleton. ConclusionThe identification of HPV status-specific significant gene set was necessary. It is expected that HPV status-specific biomarkers can be helpful in deciding therapeutic methods for patients with head and neck cancer, after biological validation and large prospective trials. (c) 2014 Wiley Periodicals, Inc. Head Neck 37: 1310-1318, 2015

关键词： head and neck cancer human papillomavirus human papillomavirus (HPV) status-specific gene set prognosis microarray data

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A new method for mining disjunctive emerging patterns in high-dimensional datasets using hypergraphs

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INFORMATION SYSTEMS 2014年 40卷 1-10页

作者： Vimieiro, Renato Moscato, Pablo Univ Newcastle Ctr Bioinformat Biomarker Discovery & Informat Ba Callaghan NSW 2308 Australia Hunter Med Res Inst New Lambton Hts NSW 2305 Australia

We investigate in this paper the problem of mining disjunctive emerging patterns in high-dimensional biomedical datasets. Disjunctive emerging patterns are sets of features that are very frequent among samples of a target class, cases in a case-control study, for example, and are very rare among all other samples. We, for the very first time, demonstrate that this problem can be solved using minimal transversals in a hypergraph. We propose a new divide-and-conquer algorithm that enables us to efficiently compute disjunctive emerging patterns in parallel and distributed environments. We conducted experiments using real-world microarray gene expression datasets to assess the performance of our approach. Our results show that our approach is more efficient than the state-of-the-art solution available in the literature. In this sense, we contribute to the area of bioinformatics and data mining by providing another useful alternative to identify patterns distinguishing samples with different class labels, such as those in case-control studies, for example. (C) 2013 Elsevier Ltd. All rights reserved.

关键词： Emerging patterns Contrast pattern mining Associative classifier Minimal transversals Hypergraphs microarray data

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Learning Phenotype Structure Using Sequence Model

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IEEE TRANSACTIONS ON KNOWLEDGE AND data ENGINEERING 2014年第3期26卷 667-681页

作者： Zhao, Yuhai Wang, Guoren Zhang, Xiang Yu, Jeffrey Xu Wang, Zhanghui Northeastern Univ Sch Informat Sci & Engn Shenyang 110819 Liaoning Peoples R China Case Western Reserve Univ Dept Elect Engn & Comp Sci Cleveland OH 44106 USA Chinese Univ Hong Kong Dept Syst Engn & Engn Management Hong Kong Hong Kong Peoples R China

Advanced microarray technologies have enabled to simultaneously monitor the expression levels of all genes. An important problem in microarray data analysis is to discover phenotype structures. The goal is to 1) find groups of samples corresponding to different phenotypes (such as disease or normal), and 2) for each group of samples, find the representative expression pattern or signature that distinguishes this group from others. Some methods have been proposed for this issue, however, a common drawback is that the identified signatures often include a large number of genes but with low discriminative power. In this paper, we propose a g*-sequence model to address this limitation, where the ordered expression values among genes are profitably utilized. Compared with the existing methods, the proposed sequence model is more robust to noise and allows to discover the signatures with more discriminative power using fewer genes. This is important for the subsequent analysis by the biologists. We prove that the problem of phenotype structure discovery is NP-complete. An efficient algorithm, FINDER, is developed, which includes three steps: 1) trivial g*-sequences identifying, 2) phenotype structure discovery, and 3) refinement. Effective pruning strategies are developed to further improve the efficiency. We evaluate the performance of FINDER and the existing methods using both synthetic and real gene expression data sets. Extensive experimental results show that FINDER dramatically improves the accuracy of the phenotype structures discovered (in terms of both statistical and biological significance) and detects signatures with high discriminative power. Moreover, it is orders of magnitude faster than other alternatives.

关键词： data mining bioinformatics microarray data

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Disclosed: An efficient depth-first, top-down algorithm for mining disjunctive closed itemsets in high-dimensional data

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INFORMATION SCIENCES 2014年 280卷 171-187页

作者： Vimieiro, Renato Moscato, Pablo Univ Newcastle Ctr Bioinformat Biomarker Discovery & Informat Ba Callaghan NSW 2308 Australia Hunter Med Res Inst New Lambton Hts NSW 2305 Australia

We focus, in this paper, on the computational challenges of identifying disjunctive Boolean patterns in high-dimensional data. We conduct our analysis focusing particularly in microarray gene expression data, since this is one of the most stereotypical examples of high-dimensional data. We devised a novel algorithm that takes advantage of the scarcity of samples in microarray data sets, allowing us to efficiently find disjunctive closed patterns. Our algorithm, Disclosed, mines disjunctive closed itemsets by exploring the search space in a depth-first, top-down manner. We evaluated the performance of our algorithm to execute such a task using real microarray gene expression data sets publicly available on the Internet. Our experiments revealed under what situations, the characteristics of a data set, our method obtain a good, bad or average performance. We also compared the performance of our method with the state of the art algorithms for finding disjunctive closed patterns and disjunctive minimal generators. We observed that our approach is two orders of magnitude more efficient, both in terms of time and memory. (C) 2014 Elsevier Inc. All rights reserved.

关键词： Disjunctive itemset Frequent itemset mining Closed itemset Rare itemset Formal concept analysis microarray data

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Semantic subgroup explanations

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JOURNAL OF INTELLIGENT INFORMATION SYSTEMS 2014年第2期42卷 233-254页

作者： Vavpetic, Anze Podpecan, Vid Lavrac, Nada Jozef Stefan Inst Dept Knowledge Technol Ljubljana Slovenia Jozef Stefan Int Postgrad Sch Ljubljana Slovenia Univ Nova Gorica Nova Gorica Slovenia

Subgroup discovery (SD) methods can be used to find interesting subsets of objects of a given class. While subgroup describing rules are themselves good explanations of the subgroups, domain ontologies can provide additional descriptions to data and alternative explanations of the constructed rules. Such explanations in terms of higher level ontology concepts have the potential of providing new insights into the domain of investigation. We show that this additional explanatory power can be ensured by using recently developed semantic SD methods. We present a new approach to explaining subgroups through ontologies and demonstrate its utility on a motivational use case and on a gene expression profiling use case where groups of patients, identified through SD in terms of gene expression, are further explained through concepts from the Gene Ontology and KEGG orthology. We qualitatively compare the methodology with the supporting factors technique for characterizing subgroups. The developed tools are implemented within a new browser-based data mining platform ClowdFlows.

关键词： data mining Semantic data mining Subgroup discovery Ontologies microarray data

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Unlabeling data can improve classification accuracy

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PATTERN RECOGNITION LETTERS 2014年第1期37卷 15-23页

作者： Lausser, Ludwig Schmid, Florian Schmid, Matthias Kestler, Hans A. Univ Ulm Inst Neural Informat Proc Res Grp Bioinformat & Syst Biol D-89069 Ulm Germany Univ Munich Dept Stat D-80539 Munich Germany

In this study we focus on the effects of sample limitations on partially supervised learning algorithms. We analyze the performance of these types of learning algorithms on small datasets under varying trade-offs between labeled and unlabeled samples. In contrast to the typical settings for partially supervised learning algorithms, the number of available unlabeled samples is also restricted. We utilize gene expression datasets, which are typical examples of data collections of small sample size. DNA microarrays are used to generate these profiles by measuring thousands of mRNA values simultaneously. These profiles are increasingly used for tumor categorization. Partially labeled microarray datasets occur naturally in the diagnostic setting if the corresponding labeling process is time consuming or expensive (i.e., "early relapse" vs. "late relapse"). Surprisingly, the best classification results in our study were not always achieved for a maximal proportion of labeled samples. This is unexpected as asymptotical results for an unlimited amount of samples suggest that a labeled sample is of an exponentially higher value than an unlabeled one. Our analysis shows that in the case of finite sample sizes a more balanced trade-off between labeled and unlabeled samples is optimal. This trade-off was not unique over all experiments. It could be shown that the optimal trade-off between unlabeled and labeled samples is mainly dependent on the chosen learning algorithm. (C) 2013 Elsevier B.V. All rights reserved.

关键词： Partially supervised learning Transductive learning Semi-supervised learning Classification microarray data

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Hybrid Ant Bee Algorithm for Fuzzy Expert System Based Sample Classification

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IEEE-ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2014年第2期11卷 347-360页

作者： GaneshKumar, Pugalendhi Rani, Chellasamy Devaraj, Durairaj Victoire, T. Aruldoss Albert Anna Univ Dept Informat Technol Reg Ctr Coimbatore Tamil Nadu India Govt Coll Engn Dept Comp Sci & Engn Salem India Kalasalingam Univ Dept Elect & Elect Engn Krishnankoil India Anna Univ Dept Elect & Elect Engn Reg Ctr Coimbatore Tamil Nadu India

Accuracy maximization and complexity minimization are the two main goals of a fuzzy expert system based microarray data classification. Our previous Genetic Swarm Algorithm (GSA) approach has improved the classification accuracy of the fuzzy expert system at the cost of their interpretability. The if-then rules produced by the GSA are lengthy and complex which is difficult for the physician to understand. To address this interpretability-accuracy tradeoff, the rule set is represented using integer numbers and the task of rule generation is treated as a combinatorial optimization task. Ant colony optimization (ACO) with local and global pheromone updations are applied to find out the fuzzy partition based on the gene expression values for generating simpler rule set. In order to address the formless and continuous expression values of a gene, this paper employs artificial bee colony (ABC) algorithm to evolve the points of membership function. Mutual Information is used for idenfication of informative genes. The performance of the proposed hybrid Ant Bee Algorithm (ABA) is evaluated using six gene expression data sets. From the simulation study, it is found that the proposed approach generated an accurate fuzzy system with highly interpretable and compact rules for all the data sets when compared with other approaches.

关键词： microarray data fuzzy expert system ant colony optimization artificial bee colony mutual information

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Estimation of variances and covariances for high-dimensional data: a selective review

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WILEY INTERDISCIPLINARY REVIEWS-COMPUTATIONAL STATISTICS 2014年第4期6卷 255-264页

作者： Tong, Tiejun Wang, Cheng Wang, Yuedong Hong Kong Baptist Univ Dept Math Hong Kong Hong Kong Peoples R China Univ Calif Santa Barbara Dept Stat & Appl Probabil Santa Barbara CA 93106 USA

Estimation of variances and covariances is required for many statistical methods such as t-test, principal component analysis and linear discriminant analysis. High-dimensional data such as gene expression microarray data and financial data pose challenges to traditional statistical and computational methods. In this paper, we review some recent developments in the estimation of variances, covariance matrix, and precision matrix, with emphasis on the applications to microarray data analysis. (C) 2014 Wiley Periodicals, Inc.

关键词： covariance matrix high-dimensional data microarray data precision matrix shrinkage estimation sparse covariance matrix

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TriGen: A genetic algorithm to mine triclusters in temporal gene expression data

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NEUROCOMPUTING 2014年 132卷 42-53页

作者： Gutierrez-Aviles, D. Rubio-Escudero, C. Martinez-Alvarez, F. Riquelme, J. C. Univ Seville Dept Comp Sci Seville Spain Pablo de Olavide Univ Seville Dept Comp Sci Seville Spain

Analyzing microarray data represents a computational challenge due to the characteristics of these data. Clustering techniques are widely applied to create groups of genes that exhibit a similar behavior under the conditions tested. Biclustering emerges as an improvement of classical clustering since it relaxes the constraints for grouping genes to be evaluated only under a subset of the conditions and not under all of them. However, this technique is not appropriate for the analysis of longitudinal experiments in which the genes are evaluated under certain conditions at several time points. We present the TriGen algorithm, a genetic algorithm that finds triclusters of gene expression that take into account the experimental conditions and the time points simultaneously. We have used TriGen to mine datasets related to synthetic data, yeast (Saccharomyces cerevisiae) cell cycle and human inflammation and host response to injury experiments. TriGen has proved to be capable of extracting groups of genes with similar patterns in subsets of conditions and times, and these groups have shown to be related in terms of their functional annotations extracted from the Gene Ontology. (C) 2013 Elsevier B.V. All rights reserved.

关键词： Tricluster Genetic algorithms microarray data Time series

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data classification using an ensemble of filters

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NEUROCOMPUTING 2014年 135卷 13-20页

作者： Bolon-Canedo, V. Sanchez-Marono, N. Alonso-Betanzos, A. Univ A Coruna Dept Comp Sci Lab Res & Dev Artificial Intelligence LIDIA La Coruna 15071 Spain

Ensemble learning has been the focus of much attention, based on the assumption that combining the output of multiple experts is better than the output of any single expert. Many methods have been proposed of which bagging and boosting were the most popular. In this research, the idea of ensembling is adapted for feature selection. We propose an ensemble of filters for classification, aimed at achieving a good classification performance together with a reduction in the input dimensionality. With this approach, we try to overcome the problem of selecting an appropriate method for each problem at hand, as it is overly dependent on the characteristics of the datasets. The adequacy of using an ensemble of filters rather than a single filter was demonstrated on synthetic and real data, paving the way for its final application over a challenging scenario such as DNA microarray classification. (C) 2014 Elsevier B.V. All rights reserved.

关键词： Ensemble learning Feature selection Classification microarray data

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