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Weighted ensemble learning of Bayesian network for gene regulatory networks

NEUROCOMPUTING 2015年第PB期150卷 404-416页

作者： Njah, Hasna Jamoussi, Salma Univ Sfax MIRACL Multimedia InfoRmat Syst & Adv Comp Lab Sfax Tunisia

Gene Regulatory Network (GRN) is known as the most adequate representation of genes' interactions based on microarray datasets. One of the most performing modeling tools that enable the inference of these networks is a Bayesian network (BN). When preceded by an efficient pre-processing step, BN learning can unveil possible relationships between key disease genes and allows biologists to analyze these interactions and to exploit them. However, the layout of microarray data is different from classic data. This particularity engenders challenges to BN learning in terms of dimensionality and data over-fitting. In this paper, we propose a fuzzy ensemble clustering method that allows outputting small and highly inter-correlated partitions of genes so that we can overcome dimensionality problem. We present a weighted committee based structure algorithm for learning BNs of each partition without over-fitting training dataset. Moreover, we offer an approach for assembling the sub-BNs through genes in common. We also statistically verify and biologically validate our approach. (C) 2014 Elsevier B.V. All rights reserved.

关键词： Ensemble learning Cluster analysis Bayesian network Bayesian network fusion microarray data

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Accelerating wrapper-based feature selection with K-nearest-neighbor

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KNOWLEDGE-BASED SYSTEMS 2015年第1期83卷 81-91页

作者： Wang, Aiguo An, Ning 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 Univ Sch Med Ctr Biomed Informat Boston MA USA Harvard Univ Sch Med Boston Childrens Hosp Childrens Hosp Informat Program Boston MA USA MIT Dept Elect Engn & Comp Sci Cambridge MA 02139 USA

Wrapper-based feature subset selection (FSS) methods tend to obtain better classification accuracy than filter methods but are considerably more time-consuming, particularly for applications that have thousands of features, such as microarray data analysis. Accelerating this process without degrading its high accuracy would be of great value for gene expression analysis. In this study, we explored how to reduce the time complexity of wrapper-based FSS with an embedded K-Nearest-Neighbor (KNN) classifier. Instead of considering KNN as a black box, we proposed to construct a classifier distance matrix and incrementally update the matrix to accelerate the calculation of the relevance criteria in evaluating the quality of the candidate features. Extensive experiments on eight publicly available microarray datasets were first conducted to demonstrate the effectiveness of the wrapper methods with KNN for selecting informative features. To demonstrate the performance gain in terms of time cost reduction, we then conducted experiments on the eight microarray datasets with the embedded KNN classifiers and analyzed the theoretical time/space complexity. Both the experimental results and theoretical analysis demonstrated that the proposed approach markedly accelerates the wrapper-based feature selection process without degrading the high classification accuracy, and the space complexity analysis indicated that the additional space overhead is affordable in practice. (C) 2015 Elsevier B.V. All rights reserved.

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

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A hierarchical two-phase framework for selecting genes in cancer datasets with a neuro-fuzzy system

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TECHNOLOGY AND HEALTH CARE 2016年第2_SUPPL期24卷 S601-S605页

作者： Lim, Jongwoo Wang, Bohyun Lim, Joon S. Gachon Univ IT Coll Songnam South Korea

Finding the minimum number of appropriate biomarkers for specific targets such as a lung cancer has been a challenging issue in bioinformatics. We propose a hierarchical two-phase framework for selecting appropriate biomarkers that extracts candidate biomarkers from the cancer microarray datasets and then selects the minimum number of appropriate biomarkers from the extracted candidate biomarkers datasets with a specific neuro-fuzzy algorithm, which is called a neural network with weighted fuzzy membership function (NEWFM). In this context, as the first phase, the proposed framework is to extract candidate biomarkers by using a Bhattacharyya distance method that measures the similarity of two discrete probability distributions. Finally, the proposed framework is able to reduce the cost of finding biomarkers by not receiving medical supplements and improve the accuracy of the biomarkers in specific cancer target datasets.

关键词： microarray data feature selection neuro network fuzzy algorithm

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A review of microarray datasets and applied feature selection methods

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INFORMATION SCIENCES 2014年 282卷 111-135页

作者： Bolon-Canedo, V. Sanchez-Marono, N. Alonso-Betanzos, A. Benitez, J. M. Herrera, F. Univ A Coruna Dept Comp Sci La Coruna 15071 Spain Univ Granada Dept Comp Sci & Artificial Intelligence E-18071 Granada Spain King Abdulaziz Univ Fac Comp & Informat Technol North Jeddah Jeddah 21589 Saudi Arabia

microarray data classification is a difficult challenge for machine learning researchers due to its high number of features and the small sample sizes. Feature selection has been soon considered a de facto standard in this field since its introduction, and a huge number of feature selection methods were utilized trying to reduce the input dimensionality while improving the classification performance. This paper is devoted to reviewing the most up-to-date feature selection methods developed in this field and the microarray databases most frequently used in the literature. We also make the interested reader aware of the problematic of data characteristics in this domain, such as the imbalance of the data, their complexity, or the so-called dataset shift. Finally, an experimental evaluation on the most representative datasets using well-known feature selection methods is presented, bearing in mind that the aim is not to provide the best feature selection method, but to facilitate their comparative study by the research community. (C) 2014 Elsevier Inc. All rights reserved.

关键词： Feature selection microarray data Unbalanced data dataset shift

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Bias and variance reduction in estimating the proportion of true-null hypotheses

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BIOSTATISTICS 2015年第1期16卷 189-204页

作者： Cheng, Yebin Gao, Dexiang Tong, Tiejun Shanghai Univ Finance & Econ Sch Stat & Management Shanghai Peoples R China Univ Colorado Dept Biostat & Informat Denver CO 80202 USA Hong Kong Baptist Univ Dept Math Kowloon Hong Kong Peoples R China

When testing a large number of hypotheses, estimating the proportion of true nulls, denoted by , becomes increasingly important. This quantity has many applications in practice. For instance, a reliable estimate of can eliminate the conservative bias of the Benjamini-Hochberg procedure on controlling the false discovery rate. It is known that most methods in the literature for estimating are conservative. Recently, some attempts have been paid to reduce such estimation bias. Nevertheless, they are either over bias corrected or suffering from an unacceptably large estimation variance. In this paper, we propose a new method for estimating that aims to reduce the bias and variance of the estimation simultaneously. To achieve this, we first utilize the probability density functions of false-null -values and then propose a novel algorithm to estimate the quantity of . The statistical behavior of the proposed estimator is also investigated. Finally, we carry out extensive simulation studies and several real data analysis to evaluate the performance of the proposed estimator. Both simulated and real data demonstrate that the proposed method may improve the existing literature significantly.

关键词： Effect size False-null p-value microarray data Multiple testing Probability density function Upper tail probability

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Meta-classifiers for high-dimensional, small sample classification for gene expression analysis

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PATTERN ANALYSIS AND APPLICATIONS 2015年第3期18卷 553-569页

作者： Kim, Kyung-Joong Cho, Sung-Bae Sejong Univ Dept Comp Engn Seoul 143747 South Korea Yonsei Univ Dept Comp Sci Seoul 120749 South Korea

Classification using small sample size (limited number of samples) with high dimension is a challenging problem in both machine learning and medicine as there are a wide variety of possible modeling approaches. Furthermore, it is not always clear which method is optimal for a prediction task. Different modeling choices include feature selection (dimensionality reduction), classification algorithms, and ensemble selection. There are several possible combinations of these methods, and it is not always clear which is the best. In the previous works, researchers show that evolutionary computation is useful to build an ensemble from the pairs of feature selection and classification algorithms. However, there are several parameters to be determined for the evolutionary computation and it requires computational time for the optimization. In this paper, we attempt to improve the approach by adopting meta-classification with the farthest-first clustering algorithm. The effectiveness and accuracy of our method are validated by experiments on four real micro-array datasets (colon, breast, prostate and lymphoma cancers) publicly available. The results confirm that the proposed method outperforms single individual classifiers and other alternatives (standard genetic algorithm, and methods from literature).

关键词： High dimension Small sample size Meta-classification Ensemble classifier microarray data

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Expectation Propagation for microarray data classification

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PATTERN RECOGNITION LETTERS 2010年第12期31卷 1618-1626页

作者： Hernandez-Lobato, Daniel Hernandez-Lobato, Jose Miguel Suarez, Alberto Catholic Univ Louvain Dept Comp Sci & Engn B-1348 Louvain Belgium Univ Autonoma Madrid Escuela Politecn Super E-28049 Madrid Spain

microarray experiments are a very promising tool for early diagnosis and disease treatment. The datasets obtained in these experiments typically consist of a small number of instances and a large number of covariates, most of which are irrelevant for discrimination. These characteristics pose severe difficulties for standard learning algorithms. A Bayesian approach can be useful to overcome these problems and produce more accurate and robust predictions. However, exact Bayesian inference is computationally costly and in many cases infeasible. In practice, some form of approximation has to be made. In this paper we consider a Bayesian linear model for microarray data classification based on a prior distribution that favors sparsity in the model coefficients. Expectation Propagation (EP) is then used to perform approximate inference as an alternative to computationally more expensive methods, such as Markov Chain Monte Carlo (MCMC) sampling. The model considered is evaluated on 15 microarray datasets and compared with other state-of-the-art classification algorithms. These experiments show that the Bayesian model trained with EP performs well on the datasets investigated and is also useful to identify relevant genes for subsequent analysis. (C) 2010 Elsevier B.V. All rights reserved.

关键词： microarray data Bayesian inference Expectation Propagation

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Partition-conditional ICA for Bayesian classification of microarray data

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EXPERT SYSTEMS WITH APPLICATIONS 2010年第12期37卷 8188-8192页

作者： Fan, Liwei Poh, Kim-Leng Zhou, Peng Natl Univ Singapore Dept Ind & Syst Engn Singapore 119260 Singapore Nanjing Univ Aeronaut & Astronaut Coll Econ & Management Nanjing 210016 Peoples R China

Accurate classification of microarray data is very important for medical decision making. Past studies have shown that class-conditional independent component analysis (CC-ICA) is capable of improving the performance of naive Bayes classifier in microarray data analysis. However, when a microarray dataset has a small number of samples for some classes, the application of CC-ICA may become infeasible. This paper extends CC-ICA and proposes a partition-conditional independent component analysis (PC-ICA) method for naive Bayes classification of microarray data. Compared to ICA and CC-ICA, PC-ICA represents an in-between concept for feature extraction. Our experimental results on two microarray datasets show that PC-ICA is more effective than ICA in improving the performance of naive Bayes classification of microarray data. (C) 2010 Elsevier Ltd. All rights reserved.

关键词： Independent component analysis microarray data Naive Bayes Feature extraction Mutual information

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TMA-TAB: A spreadsheet-based document for exchange of tissue microarray data based on the tissue microarray-object model

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JOURNAL OF BIOMEDICAL INFORMATICS 2010年第3期43卷 435-441页

作者： Song, Young Soo Lee, Hye Won Park, Yu Rang Kim, Do Kyoon Sim, Jaehyun Kang, Hyunseok Peter Kim, Ju Han Seoul Natl Univ Coll Med Div Biomed Informat Seoul 110799 South Korea Seoul Natl Univ Coll Med SNUBI Seoul 110799 South Korea Univ Florida Coll Med Dept Mol Genet & Microbiol Gainesville FL 32611 USA Roswell Pk Canc Inst Dept Pathol & Lab Med Buffalo NY 14263 USA

The importance of tissue microarrays (TMA) as clinical validation tools for cDNA microarray results is increasing, whereas researchers are still suffering from TMA data management issues. After we developed a comprehensive data model for TMA data storage, exchange and analysis, TMA-OM, we focused our attention on the development of a user-friendly exchange format with high expressivity in order to promote data communication of TMA results and TMA-OM supportive database applications. We developed TMA-TAB, a spreadsheet-based data format for TMA data submission to the TMA-OM supportive TMA database system. TMA-TAB was developed by simplifying, modifying and reorganizing classes, attributes and templates of TMA-OM into five entities: experiment, block, slide, core_in_block, and core_in_slide. Five tab-delimited formats (investigation design format, block description format, slide description format, core clinicohistopathological data format, and core result data format) were made, each representing the entities of experiment, block, slide, core_in_block, and core_in_slide. We implemented TMA-TAB import and export modules on Xperanto-TMA, a TMA-OM supportive database application, to facilitate data submission. Development and implementation of TMA-TAB and TMA-OM provide a strong infrastructure for powerful and user-friendly TMA data management. (C) 2009 Elsevier Inc. All rights reserved.

关键词： database microarray data Modeling Tissue microarray

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Identify the signature genes for diagnose of uveal melanoma by weight gene co-expression network analysis

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International Journal of Ophthalmology(English edition) 2015年第2期8卷 269-274页

作者： Kai Shi Zhi-Tong Bing Gui-Qun Cao Ling Guo Ya-Na Cao Hai-Ou Jiang Mei-Xia Zhang Department of Ophthalmology West China HospitalSichuan University Molecular Medicine Research Center West China HospitalSichuan University Institute of Modern Physics Chinese Academy of Sciences College of Electrical Engineering Northwest University for Nationalities

AIM: To identify and understand the relationship between co-expression pattern and clinic traits in uveal melanoma, weighted gene co-expression network analysis(WGCNA) is applied to investigate the gene expression levels and patient clinic features. Uveal melanoma is the most common primary eye tumor in adults. Although many studies have identified some important genes and pathways that were relevant to progress of uveal melanoma, the relationship between co-expression and clinic traits in systems level of uveal melanoma is unclear yet. We employ WGCNA to investigate the relationship underlying molecular and phenotype in this ***: Gene expression profile of uveal melanoma and patient clinic traits were collected from the Gene Expression Omnibus(GEO) database. The gene co-expression is calculated by WGCNA that is the R package software. The package is used to analyze the correlation between pairs of expression levels of *** function of the genes were annotated by gene ontology(GO).RESULTS: In this study, we identified four co-expression modules significantly correlated with clinictraits. Module blue positively correlated with radiotherapy treatment. Module purple positively correlates with tumor location(sclera) and negatively correlates with patient age. Module red positively correlates with sclera and negatively correlates with thickness of tumor. Module black positively correlates with the largest tumor diameter(LTD). Additionally, we identified the hug gene(top connectivity with other genes) in each module. The hub gene RPS15 A, PTGDS, CD53 and MSI2 might play a vital role in progress of uveal ***: From WGCNA analysis and hub gene calculation, we identified RPS15 A, PTGDS, CD53 and MSI2 might be target or diagnosis for uveal melanoma.

关键词： weighted gene co-expression network analysis microarray data gene ontology

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