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IEEE International Conference on Systems, Man, and Cybernetics

作者： Natthakan Iam-On Tossapon Boongoen School of Information Technology Mae Fah Luang University Department of Mathematics and Computer Science Royal Thai Air Force Academy

ISBN: (纸本)9781467317139

Cancer has been identified as the leading cause of death. It is predicted that around 20-26 million people will be diagnosed with cancer by 2020. As a result, there is an urgent need for a more effective methodology to prevent and cure cancer. microarray technology provides a useful basis of achieving this ultimate goal. For cancer research, it has become almost routine to create gene expression profiles, which can discriminate patients into good and poor prognosis groups. This cluster analysis offers a useful basis for individualized treatment of disease. Cluster ensembles have been shown to be better than any standard clustering algorithm for such a task. This meta-learning formalism helps users to overcome the dilemma of selecting an appropriate technique and the parameters for that technique, given a set of data. Among different state-of-the-art methods, the link-based approach (LCE) provides a highly accurate clustering. This paper presents the improvement of LCE with a new link-based similarity measure being developed and engaged. Additional information that is already available in an information network is included in the similarity assessment. As such, this refinement can increase the quality of the measures, hence the resulting cluster decision. The performance of this improved LCE is evaluated on published microarray datasets, in comparison with the original LCE and several well-known cluster ensemble techniques. The findings suggest that the new model can improve the accuracy of LCE and performs better than the others investigated in the empirical study.

关键词： microarray data Clustering Cluster ensembles Link-based similarity ELOVL6 gene similar measure Information Network Cancer Cause of Death data analysis dataset Ensembles Cluster Analysis Cancer microarray Dilemma

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Support Vector Machine Ensembles Using Feature-Subset Selection for Enhancing microarray data Classification

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INTERNATIONAL JOURNAL OF APPLIED MATHEMATICS & STATISTICS 2012年第4期28卷 1-11页

作者： Ahmed, Eman El Gayar, Neamat El Azab, Iman A. Cairo Univ Fac Comp & Informat Giza 12613 Egypt

Support Vector Machines (SVMs) are known to be robust tools for classification and regression in noisy and complex domains. SVM ensembles have been widely used to improve classification accuracy in complicated pattern recognition tasks. A good example is the DNA microarray data -for tumor classification-which is usually characterized by low sample size, high dimensionality, noise and large biological variability. In this work we propose to apply an ensemble of SVMs coupled with feature-subset selection methods to alleviate the curse of dimensionality associated with expression-based classification of DNA data in order to achieve stable and reliable results. We compare the single SVM classifier to SVM ensembles applying two different feature-subset selection techniques, namely random selection and k-means clustering, and combining the base classifiers using either majority vote or SVM fusion. Two real-world datasets are used as benchmarks to evaluate and compare the performance. Experimental results show that the ensemble with k-means clustering for feature-subset selection which uses SVM base classifiers and an SVM combiner achieves the best classification accuracy, and that feature-subset-selection methods can have a considerable impact on the classification accuracy.

关键词： Support Vector Machines (SVM) Ensemble classification SVM fusion Feature subsets Feature selection microarray data

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Computational synchronization of microarray data with application to Plasmodium falciparum

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PROTEOME SCIENCE 2012年第1-Sup期10卷 S10-S10页

作者： Zhao, Wei Dauwels, Justin Niles, Jacquin C. Cao, Jianshu Singapore MIT Alliance Res & Technol Ctr Life Sci Singapore 117456 Singapore Nanyang Technol Univ Sch Elect & Elect Engn Singapore 639798 Singapore MIT Dept Biol Engn Cambridge MA 02139 USA MIT Dept Chem Cambridge MA 02139 USA

Background: microarrays are widely used to investigate the blood stage of Plasmodium falciparum infection. Starting with synchronized cells, gene expression levels are continually measured over the 48-hour intraerythrocytic cycle (IDC). However, the cell population gradually loses synchrony during the experiment. As a result, the microarray measurements are blurred. In this paper, we propose a generalized deconvolution approach to reconstruct the intrinsic expression pattern, and apply it to P. falciparum IDC microarray data. Methods: We develop a statistical model for the decay of synchrony among cells, and reconstruct the expression pattern through statistical inference. The proposed method can handle microarray measurements with noise and missing data. The original gene expression patterns become more apparent in the reconstructed profiles, making it easier to analyze and interpret the data. We hypothesize that reconstructed gene expression patterns represent better temporally resolved expression profiles that can be probabilistically modeled to match changes in expression level to IDC transitions. In particular, we identify transcriptionally regulated protein kinases putatively involved in regulating the P. falciparum IDC. Results: By analyzing publicly available microarray data sets for the P. falciparum IDC, protein kinases are ranked in terms of their likelihood to be involved in regulating transitions between the ring, trophozoite and schizont developmental stages of the P. falciparum IDC. In our theoretical framework, a few protein kinases have high probability rankings, and could potentially be involved in regulating these developmental transitions. Conclusions: This study proposes a new methodology for extracting intrinsic expression patterns from microarray data. By applying this method to P. falciparum microarray data, several protein kinases are predicted to play a significant role in the P. falciparum IDC. Earlier experiments have indeed confirmed tha

关键词： microarray data microarray Experiment Stage Parasite Normalize Life Span Infection Period

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A New Method for Preliminary Identification of Gene Regulatory Networks from Gene microarray Cancer data Using Ridge Partial Least Squares With Recursive Feature Elimination and Novel Brier and Occurrence Probability Measures

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IEEE TRANSACTIONS ON SYSTEMS MAN AND CYBERNETICS PART A-SYSTEMS AND HUMANS 2012年第6期42卷 1514-1528页

作者： Chan, S. C. Wu, H. C. Tsui, K. M. Univ Hong Kong Dept Elect & Elect Engn Pokfulam Hong Kong Peoples R China

This paper proposes a new method for preliminary identification of gene regulatory networks (GRNs) from gene microarray cancer databased on ridge partial least squares (RPLS) with recursive feature elimination (RFE) and novel Brier and occurrence probability measures. It facilitates the preliminary identification of meaningful pathways and genes for a specific disease, rather than focusing on selecting a small set of genes for classification purposes as in conventional studies. First, RFE and a novel Brier error measure are incorporated in RPLS to reduce the estimation variance using a two-nested cross validation (CV) approach. Second, novel Brier and occurrence probability-based measures are employed in ranking genes across different CV subsamples. It helps to detect different GRNs from correlated genes which consistently appear in the ranking lists. Therefore, unlike most conventional approaches that emphasize the best classification using a small gene set, the proposed approach is able to simultaneously offer good classification accuracy and identify a more comprehensive set of genes and their associated GRNs. Experimental results on the analysis of three publicly available cancer data sets, namely leukemia, colon, and prostate, show that very stable gene sets from different but relevant GRNs can be identified, and most of them are found to be of biological significance according to previous findings in biological experiments. These suggest that the proposed approach may serve as a useful tool for preliminary identification of genes and their associated GRNs of a particular disease for further biological studies using microarray or similar data.

关键词： Cancer classification gene regulatory network (GRN) logistic regression (LR) microarray data novel Brier and occurrence probability (OP) measures ridge partial least squares (RPLS) regression

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MVIAeval: a web tool for comprehensively evaluating the performance of a new missing value imputation algorithm

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BMC BIOINFORMATICS 2017年第1期18卷 1-10页

作者： Wu, Wei-Sheng Jhou, Meng-Jhun Natl Cheng Kung Univ Dept Elect Engn Tainan Taiwan

Background: Missing value imputation is important for microarray data analyses because microarray data with missing values would significantly degrade the performance of the downstream analyses. Although many microarray missing value imputation algorithms have been developed, an objective and comprehensive performance comparison framework is still lacking. To solve this problem, we previously proposed a framework which can perform a comprehensive performance comparison of different existing algorithms. Also the performance of a new algorithm can be evaluated by our performance comparison framework. However, constructing our framework is not an easy task for the interested researchers. To save researchers' time and efforts, here we present an easy-to-use web tool named MVIAeval (Missing Value Imputation Algorithm evaluator) which implements our performance comparison framework. Results: MVIAeval provides a user-friendly interface allowing users to upload the R code of their new algorithm and select (i) the test datasets among 20 benchmark microarray (time series and non-time series) datasets, (ii) the compared algorithms among 12 existing algorithms, (iii) the performance indices from three existing ones, (iv) the comprehensive performance scores from two possible choices, and (v) the number of simulation runs. The comprehensive performance comparison results are then generated and shown as both figures and tables. Conclusions: MVIAeval is a useful tool for researchers to easily conduct a comprehensive and objective performance evaluation of their newly developed missing value imputation algorithm for microarray data or any data which can be represented as a matrix form (e.g. NGS data or proteomics data). Thus, MVIAeval will greatly expedite the progress in the research of missing value imputation algorithms.

关键词： Web tool Missing value imputation microarray data Performance index Performance comparison Algorithm

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Parallel gene selection and dynamic ensemble pruning based on Affinity Propagation

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COMPUTERS IN BIOLOGY AND MEDICINE 2017年 87卷 8-21页

作者： Meng, Jun Zhang, Jing Luan, Yu-Shi He, Xin-Yu Li, Li-Shuang Zhu, Yuan-Feng Dalian Univ Technol Sch Comp Sci & Technol Dalian 116023 Peoples R China Dalian Univ Technol Sch Life Sci & Biotechnol Dalian 116023 Peoples R China BorderX Lab Inc Silicon Valley CA 94086 USA

Gene selection and sample classification based on gene expression data are important research areas in bioinformatics. Selecting important genes closely related to classification is a challenging task due to high dimensionality and small sample size of microarray data. Extended rough set based on neighborhood has been successfully applied to gene selection, as it can select attributes without redundancy and deal with numerical attributes directly. However, the computation of approximations in rough set is extremely time consuming. In this paper, in order to accelerate the process of gene selection, a parallel computation method is proposed to calculate approximations of intersection neighborhood rough set. Furthermore, a novel dynamic ensemble pruning approach based on Affinity Propagation clustering and dynamic pruning framework is proposed to reduce memory usage and computational cost. Experimental results on three Arabidopsis thaliana biotic and abiotic stress response datasets demonstrate that the proposed method can obtain better classification performance than ensemble method with gene pre-selection.

关键词： Intersection neighborhood rough set MapReduce Affinity Propagation Dynamic ensemble pruning microarray data

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Wrapper-based gene selection with Markov blanket

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COMPUTERS IN BIOLOGY AND MEDICINE 2017年 81卷 11-23页

作者： Wang, Aiguo An, Ning Yang, Jing Chen, Guilin Li, Lian Alterovitz, Gil Hefei Univ Technol Sch Comp & Informat Hefei Peoples R China Chuzhou Univ Sch Comp & Informat Engn Chuzhou Peoples R China Harvard Med Sch Ctr Biomed Informat Boston MA USA MIT Dept Elect Engn & Comp Sci Cambridge MA 02139 USA Harvard MIT Div Hlth Sci & Technol Childrens Hosp Informat Program Boston MA USA

Gene selection seeks to find a small subset of discriminant genes from the gene expression profiles. Current gene selection methods such as wrapper-based models mainly address the issue of obtaining high-quality gene subsets. However, they are considerably time consuming, due to the existence of irrelevant and redundant genes. In this study, we present an improved wrapper-based gene selection method by introducing the Markov blanket technique to reduce the required wrapper evaluation time. In addition, our method can identify targeting genes while eliminating redundant ones in an efficient way. We use ten publicly available microarray datasets to evaluate the proposed method. The results show that our method can handle gene selection effectively. Our experimental results also show that wrapper-based method combined with the Markov blanket outperforms other competing methods in terms of classification accuracy and time/space complexity.

关键词： Gene selection microarray data Symmetric uncertainty Markov blanket Wrapper methods

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A two-sample test for high-dimension, low-sample-size data under the strongly spiked eigenvalue model

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HIROSHIMA MATHEMATICAL JOURNAL 2017年第3期47卷 273-288页

作者： Ishii, Aki Tokyo Univ Sci Dept Informat Sci 2641 Yamazaki Noda Chiba 2788510 Japan

A common feature of high-dimensional data is that the data dimension is high, however, the sample size is relatively low. We call such data HDLSS data. In this paper, we consider a new two-sample test for high-dimensional data under the strongly spiked eigenvalue (SSE) model. We consider the distance-based two-sample test under the SSE model. We introduce the noise-reduction (NR) methodology and apply that to the two-sample test. Finally, we give simulation studies and demonstrate the new test procedure by using microarray data sets.

关键词： Asymptotic distribution Distance-based two-sample test HDLSS Noise-reduction methodology microarray data

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Building an intelligent integrated method of gene selection for facioscapulohumeral muscular dystrophy diagnosis

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INTERNATIONAL JOURNAL OF BIOMEDICAL ENGINEERING AND TECHNOLOGY 2017年第3期24卷 285-296页

作者： Anand, Divya Pandey, Babita Pandey, Devendra K. Lovely Profess Univ Sch Comp Sci & Engn Phagwara Punjab India Lovely Profess Univ Sch Comp Applicat Phagwara Punjab India Lovely Profess Univ Sch Biosci Phagwara Punjab India

These days, the genetic testing of neuromuscular diseases is an active area of research. The microarray technology is playing a vital role in analysing the whole genome simultaneously. In microarrays, due to the presence of large number of genes and a small number of samples, it is very difficult to find the disease specific gene subset. So, here gene selection for classification plays a major role in the genetic testing of the disease. In this paper, the gene selection is performed by deploying genetic algorithm with different number of genes wherein the fitness function is evaluated using three different classifiers namely linear discriminant analysis, quadratic discriminant analysis and k-nearest neighbour one-at-a-time for classification of facioscapulohumeral muscular dystrophy. The comparative analysis of the same is also shown in this paper. A facioscapulohumeral muscular dystrophy gene dataset consisting of 50 samples and 33,297 genes is used to evaluate the performance of integrated algorithms. The result shows that the integration of genetic algorithm with k-nearest neighbour is found to be the best for gene selection and diagnosis of facioscapulohumeral muscular dystrophy.

关键词： facioscapulohumeral muscular dystrophy genetic diagnosis genetic algorithm k-nearest neighbour linear discriminant analysis quadratic discriminant analysis muscular dystrophy microarray data neuromuscular dystrophy

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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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