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IEEE International Conference on Bioinformatics and Biomedicine (IEEE BIBM)

作者： Joshi, Pujan Pei, Baikang Hong, Seung-Hyun Kalajzic, Ivo Shin, Dong-Ju Rowe, David Shin, Dong-Guk Univ Connecticut Storrs CT 06269 USA Yale Univ Computat Biol & Bioinformat New Haven CT 06520 USA Univ Connecticut Ctr Hlth Genet & Dev Biol Farmington CT 06030 USA Sanford Burnham Med Res Inst Metabol Signaling & Dis Program Orlando FL 32827 USA

ISBN: (纸本)9781479913091;9781479913107

One known challenge in analyzing gene expression data is to combine analysis outcomes obtained disparately by applying multiple, independent meta-analysis methods. Here we present an integrative computational system that narrows down biological hypotheses by integrating gene expression patterns, transcription factor (TF) binding site analysis outcomes, and Gene Ontology (GO) enrichment analysis outcomes. This system identifies regulated genes from microarray experiments through statistical processes, categorizes similarly behaving groups of genes and then carries out binding site analysis and gene function enrichment analysis based on some significant clusters. The output is an ordered set of "putative" pair-wise relationships between TFs and their potential target genes. The relationships are ranked based on their closeness to the experimental context. We demonstrate the effectiveness of our framework using two independent microarray data sets.

关键词： microarray data Pattern based clustering TF binding site analysis ChIP-seq GO Gene regulation relationship

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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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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 (SMC)

作者： Jungjit, Suwimol Freitas, Alex A. Michaelis, M. Cinatl, J. Univ Kent Sch Comp Canterbury CT2 7NF Kent England

ISBN: (纸本)9781479906529

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

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A Functional Workbench for Anopheles gambiae Micro Array Analysis

A Functional Workbench for <i>Anopheles gambiae</i> Micro Ar...

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UKSim-AMSS 7th European Modelling Symposium on Computer Modelling and Simulation (EMS)

作者： Adebiyi, Marion Oghuan, Josiah Fatumo, Segun Adebiyi, Ezekiel Rasgon, Jason Covenant Univ Dept Comp & Informat Sci Ota Nigeria Penn State Univ Dept Entomol Ctr Infect Dis Dynam University Pk PA 16802 USA Penn State Univ Inst Life Sci University Pk PA 16802 USA

ISBN: (纸本)9781479925780

Insecticide resistance, a character inherited that encompasses alteration in one or more of insect's genes is now a major public health challenge combating world efforts on malaria control strategies. Anopheles has developed heavy resistance to pyrethroids, the only World Health Organization (WHO) recommended class for Indoor Residual Spray (IRS) and Long-Lasting Insecticide Treated Nets (LLITNs) through P450 pathways. We used the biochemical network of Anopheles gambiae (henceforth Ag) to deduce its resistance mechanism(s) using two expression data (when Ag is treated with pyrethroid and when controlled). The employed computational techniques are accessible by a robust, multi-faceted and friendly automated graphic user interface (GUI) tagged 'workbench' with JavaFX Scenebuilder. In this work, we introduced a computational platform to determine and also elucidate for the first time resistance mechanism to a commonly used class of insecticide, Pyrethroid. Significantly, our work is the first computational work to identify genes associated or involved in the efflux system in Ag and as a resistance mechanism in the Anopheles.

关键词： Anopheles gambiae biochemical network microarray data resistance mechanism and Features extraction

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INTEGRATIVE MODEL-BASED CLUSTERING OF microarray METHYLATION AND EXPRESSION data

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ANNALS OF APPLIED STATISTICS 2012年第3期6卷 1327-1347页

作者： Kormaksson, Matthias Booth, James G. Figueroa, Maria E. Melnick, Ari Cornell Univ Dept Stat Sci Ithaca NY 14853 USA Weill Cornell Med Coll Dept Med Hematol Oncol Div New York NY 10065 USA

In many fields, researchers are interested in large and complex biological processes. Two important examples are gene expression and DNA methylation in genetics. One key problem is to identify aberrant patterns of these processes and discover biologically distinct groups. In this article we develop a model-based method for clustering such data. The basis of our method involves the construction of a likelihood for any given partition of the subjects. We introduce cluster specific latent indicators that, along with some standard assumptions, impose a specific mixture distribution on each cluster. Estimation is carried out using the EM algorithm. The methods extend naturally to multiple data types of a similar nature, which leads to an integrated analysis over multiple data platforms, resulting in higher discriminating power.

关键词： Integrative model-based clustering microarray data mixture models EM algorithm methylation expression AML

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Validation of MIMGO: a method to identify differentially expressed GO terms in a microarray dataset.

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BMC research notes 2012年第1期5卷 680-680页

作者： Yamada, Yoichi Sawada, Hiroki Hirotani, Ken-Ichi Oshima, Masanobu Satou, Kenji Institute of Science and Engineering Kanazawa University Kanazawa Japan

We previously proposed an algorithm for the identification of GO terms that commonly annotate genes whose expression is upregulated or downregulated in some microarray data compared with in other microarray data. We call these "differentially expressed GO terms" and have named the algorithm "matrix-assisted identification method of differentially expressed GO terms" (MIMGO). MIMGO can also identify microarray data in which genes annotated with a differentially expressed GO term are upregulated or downregulated. However, MIMGO has not yet been validated on a real microarray dataset using all available GO terms. We combined Gene Set Enrichment Analysis (GSEA) with MIMGO to identify differentially expressed GO terms in a yeast cell cycle microarray dataset. GSEA followed by MIMGO (GSEA + MIMGO) correctly identified (p < 0.05) microarray data in which genes annotated to differentially expressed GO terms are upregulated. We found that GSEA + MIMGO was slightly less effective than, or comparable to, GSEA (Pearson), a method that uses Pearson's correlation as a metric, at detecting true differentially expressed GO terms. However, unlike other methods including GSEA (Pearson), GSEA + MIMGO can comprehensively identify the microarray data in which genes annotated with a differentially expressed GO term are upregulated or downregulated. MIMGO is a reliable method to identify differentially expressed GO terms comprehensively.

关键词： Gene Ontology microarray data microarray dataset Strict Threshold Ideal Expression Pattern

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microarray-based prediction of Parkinson's disease using clinical data as additional response variables

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STATISTICS IN MEDICINE 2012年第30期31卷 4369-4381页

作者： Karlsson, Magdalena Kauczynska Lonneborg, Anders Saebo, Solve DiaGenic ASA N-0663 Oslo Norway Norwegian Univ Life Sci Dept Chem Biotechnol & Food Sci N-1432 As Norway

Recent discoveries and developments in the field of genomics have led to the commercialization of novel diagnostic devices for studying disease or estimating therapeutic outcomes in individual patients. With this emerging field, the emphasis is shifting to integration of clinical research into product development. data acquisition is primary in the initial exploratory phase of product development, and during the process of sample collection and data generation in clinical microarray studies, great amounts of additional information, such as demographic, clinical, and study design variables associated with the data, are often accumulated and made available. Including additional information in classification has been addressed in many different ways. However, in previous studies, the additional information have consistently been treated as extra predictors, which can be a problem for future prediction if such information are not available or collectable for the new samples. We instead propose to adopt a method called canonical partial least squares, which for our purpose, only uses the additional information at the model building stage to stabilize the construction of a classifier for disease status from microarray data. The canonical partial least squares method is compared with regular partial least squares for the classification of Parkinson's disease from gene expression in peripheral blood samples and also through computer simulations. The present study showed that including clinical data in the model building produces simpler and more stable models for prediction of Parkinson's disease from gene expression data. Copyright (c) 2012 John Wiley & Sons, Ltd.

关键词： microarray data PLS regression additional responses Parkinson's disease prediction

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A Comparative Study of Feature Selection and Classification Methods for Gene Expression data of Glioma

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Procedia Computer Science 2013年 23卷 5-14页

作者： Heba Abusamra Computer Electrical and Mathematical Sciencies and Engineering Division King Abdullah University of Science and Technology (KAUST) Thuwal 23955-6900 Saudi Arabia

microarray gene expression data gained great importance in recent years due to its role in disease diagnoses and prognoses which help to choose the appropriate treatment plan for patients. This technology has shifted a new era in molecular classification. Interpreting gene expression data remains a difficult problem and an active research area due to their native nature of “high dimensional low sample size”. Such problems pose great challenges to existing classification methods. Thus, effective feature selection techniques are often needed in this case to aid to correctly classify different tumor types and consequently lead to a better understanding of genetic signatures as well as improve treatment strategies. This paper aims on a comparative study of state-of-the- art feature selection methods, classification methods, and the combination of them, based on gene expression data. We compared the efficiency of three different classification methods including: support vector machines, k-nearest neighbor and random forest, and eight different feature selection methods, including: information gain, twoing rule, sum minority, max minority, gini index, sum of variances, t-statistics, and one-dimension support vector machine. Five-fold cross validation was used to evaluate the classification performance. Two publicly available gene expression data sets of glioma were used in the experiments. Results revealed the important role of feature selection in classifying gene expression data. By performing feature selection, the classification accuracy can be significantly boosted by using a small number of genes. The relationship of features selected in different feature selection methods is investigated and the most frequent features selected in each fold among all methods for both datasets are evaluated.

关键词： gene expression microarray data feature selection classification glioma

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An Efficient and Effective Wrapper based on Paired t-test for Learning Naive Bayes Classifiers from Large-scale Domains

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Procedia Computer Science 2013年 23卷 102-112页

作者： Chanju Kim Honglan Li Soo-Yong Shin Kyu-Baek Hwang School of Computer Science and Engineering Soongsil University Seoul 156-743 Korea Department of Clinical Epidemiology & Biostatistics Asan Medical Center & University of Ulsan College of Medicine Seoul 138-736 Korea

Feature selection is one of the crucial steps in supervised learning, which influences the entire subsequent classification (or regression) process. The approaches to this task can largely be divided into two categories: filter-based and wrapper-based methods. Generally, the latter produces better results than the former with regard to given learning methods, though it consumes more computational resources for searches over the feature subset space. In this paper, we propose an Efficient wRapper based on a Paired t -Test (ERPT) for choosing features from large-scale data consisting of thousands of variables, such as microarrays. Statistical tests are a reasonable option when the number of features is very large because they have more predictable behavior and can be more efficient than most search methods. The proposed method consists of two phases: decrement phase and increment phase. In the decrement phase, it selects strongly relevant features. In the increment phase, it adds weakly relevant features, given the previously selected features. Our method, combined with naive Bayes classifiers, has been tested in an extensive set of experiments on University of California Irvine (UCI) Machine Learning Repository data. The results showed that the performance of the proposed method is comparable to that of the backward search-based wrapper and superior to that of the forward search-based wrapper. Furthermore, it demonstrated much better performance than the forward search-based wrapper when applied to three microarray data sets, for which the backward search-based wrapper was impractical because of the computational burden involved. The proposed method has the following three merits: (1) it is applicable to data sets having thousands of variables, (2) it provides a theoretically sound and controllable criterion for thresholding features, and (3) it finds feature subsets for the maximizing of classification performance on sparse domains.

关键词： feature selection wrappers Naive Bayes classifiers microarray data

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A Functional Workbench for Anopheles gambiae Micro Array Analysis

A Functional Workbench for Anopheles gambiae Micro Array Ana...

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European Modelling Symposium

作者： Marion Adebiyi Josiah Oghuan Segun Fatumo Ezekiel Adebiyi Jason Rasgon Department of Computer and Information Sciences Covenant University Department of Entomology The Center for Infectious Disease Dynamics and the Huck Institutes of the Life Sciences The Pennsylvania State University

ISBN: (纸本)9781479925797

关键词： Anopheles gambiae Biochemical network microarray data Resistance mechanism Features extraction

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