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30th IEEE International Symposium on Computer-Based Medical Systems (IEEE CBMS)

作者： Corapcioglu, Erdem Ogul, Hasan Baskent Univ Dept Comp Engn Baglica Campus TR-06810 Ankara Turkey

ISBN: (纸本)9781538617106

In silico diagnosis through microRNA expression profiling experiments is a promising direction in the clinical practices of bioinformatics science. The task is computationally defined as a classification problem where a query experiment is required to be assigned into one of the predefined diseases using a model learned from previously labeled samples. While several powerful machine learning models exist to perform this task, the challenging issue is how to feed these models by effectively encoded samples. This encoding requires a sensible representation of experiment content. In contrast to previous data-driven representations based on observed differential expressions of individual miRNAs, we offer here a interaction-driven representation scheme that considers the active interactions of miRNAs with other entities in either direction of gene regulation, i.e. regulating or being regulated. We use the enrichment scores of the miRNA sets annotated by corresponding interaction elements to encode the input experiments. We have empirically shown that the new encoding can lead to a higher disease classification accuracy compared with the traditional data-driven approach.

关键词： microRNA microRNA interactions microarray classification set enrichment analysis

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Biomarker Identification of Rat Liver Regeneration via Adaptive Logistic Regression

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International Journal of Automation and computing 2016年第2期13卷 191-198页

作者： Liu-Yuan Chen Jie Yang Guo-Guo Xu Yun-Qing Liu Jun-Tao Li Cun-Shuan Xu School of Information Engineering Wuhan University of Technology College of Mathematics and Information Sciences Henan Normal University State Key Laboratory Cultivation Base for Cell Differentiation Regulation Henan Normal University

This paper is devoted to identifying the biomarkers of rat liver regeneration via the adaptive logistic regression. By combining the adaptive elastic net penalty with the logistic regression loss, the adaptive logistic regression is proposed to adaptively identify the important genes in groups. Furthermore, by improving the pathwise coordinate descent algorithm, a fast solving algorithm is developed for computing the regularized paths of the adaptive logistic regression. The results from the experiments performed on the microarray data of rat liver regeneration are provided to illustrate the effectiveness of the proposed method and verify the biological rationality of the selected biomarkers.

关键词： Adaptive logistic regression gene selection microarray classification grouping effect rat liver regeneration

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SoFoCles: Feature filtering for microarray classification based on Gene Ontology

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JOURNAL OF BIOMEDICAL INFORMATICS 2010年第1期43卷 1-14页

作者： Papachristoudis, Georgios Diplaris, Sotiris Mitkas, Pericles A. Aristotle Univ Thessaloniki Dept Elect & Comp Engn Thessaloniki 54124 Greece MIT Comp Sci & Artificial Intelligence Lab Cambridge MA USA

Marker gene selection has been an important research topic in the classification analysis of gene expression data. Current methods try to reduce the "curse of dimensionality" by using statistical intra-feature set calculations, or classifiers that are based on the given dataset. In this paper, we present SoFoCles, an interactive tool that enables semantic feature filtering in microarray classification problems with the use of external, well-defined knowledge retrieved from the Gene Ontology. The notion of semantic similarity is used to derive genes that are involved in the same biological path during the microarray experiment, by enriching a feature set that has been initially produced with legacy methods. Among its other functionalities, SoFoCles offers a large repository of semantic similarity methods that are used in order to derive feature sets and marker genes. The structure and functionality of the tool are discussed in detail, as well as its ability to improve classification accuracy. Through experimental evaluation, SoFoCles is shown to outperform other classification schemes in terms of classification accuracy in two real datasets using different semantic similarity computation approaches. (C) 2009 Elsevier Inc. All rights reserved.

关键词： Data mining Semantic similarity Ontologies Bioinformatics Feature filtering microarray classification

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Huberized Multiclass Support Vector Machine for microarray classification

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Acta Automatica Sinica 2010年第3期36卷 399-405页

作者： Jun-Tao LI Ying-Min JIA The Seventh Research Division Beihang University Beijing 100191 P.R.China

This paper proposes a new multiclass support vector machine (SVM) for simultaneous gene selection and microarray classification. Combining the huberized hinge loss function and the elastic net penalty, the proposed SVM can perform automatic gene selection and encourages a grouping effect. The coefficient paths of the proposed SVM are shown to be piecewise linear with respect to the single regularization parameter, based on which the solution path algorithm is developed with low computational complexity. Experiments performed on the leukemia data set are provided to verify the obtained results.

关键词： Gene selection grouping effect microarray classification solution path support vector machine (SVM)

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A feature selection method for classification within functional genomics experiments based on the proportional overlapping score

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BMC BIOINFORMATICS 2014年第1期15卷 1-20页

作者： Mahmoud, Osama Harrison, Andrew Perperoglou, Aris Gul, Asma Khan, Zardad Metodiev, Metodi V. Lausen, Berthold Univ Essex Dept Math Sci Colchester CO4 3SQ Essex England Univ Essex Prote Unit Sch Biol Sci Colchester CO4 3SQ Essex England Helwan Univ Dept Appl Stat Cairo Egypt

Background: microarray technology, as well as other functional genomics experiments, allow simultaneous measurements of thousands of genes within each sample. Both the prediction accuracy and interpretability of a classifier could be enhanced by performing the classification based only on selected discriminative genes. We propose a statistical method for selecting genes based on overlapping analysis of expression data across classes. This method results in a novel measure, called proportional overlapping score (POS), of a feature's relevance to a classification task. Results: We apply POS, along-with four widely used gene selection methods, to several benchmark gene expression datasets. The experimental results of classification error rates computed using the Random Forest, k Nearest Neighbor and Support Vector Machine classifiers show that POS achieves a better performance. Conclusions: A novel gene selection method, POS, is proposed. POS analyzes the expressions overlap across classes taking into account the proportions of overlapping samples. It robustly defines a mask for each gene that allows it to minimize the effect of expression outliers. The constructed masks along-with a novel gene score are exploited to produce the selected subset of genes.

关键词： Feature selection Gene ranking microarray classification Proportional overlap score Gene mask Minimum subset of genes

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Robust and stable feature selection by integrating ranking methods and wrapper technique in genetic data classification

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BIOCHEMICAL AND BIOPHYSICAL RESEARCH COMMUNICATIONS 2014年第4期446卷 850-856页

作者： Yassi, Maryam Moattar, Mohammad Hossein Islamic Azad Univ Mashhad Branch Young Researchers & Elite Club Mashhad Iran Islamic Azad Univ Mashhad Branch Dept Software Engn Mashhad Iran

High dimensional data increase the dimension of space and consequently the computational complexity and result in lower generalization. From these types of classification problems microarray data classification can be mentioned. microarrays contain genetic and biological data which can be used to diagnose diseases including various types of cancers and tumors. Having intractable dimensions, dimension reduction process is necessary on these data. The main goal of this paper is to provide a method for dimension reduction and classification of genetic data sets. The proposed approach includes different stages. In the first stage, several feature ranking methods are fused for enhancing the robustness and stability of feature selection process. Wrapper method is combined with the proposed hybrid ranking method to embed the interaction between genes. Afterwards, the classification process is applied using support vector machine. Before feeding the data to the SVM classifier the problem of imbalance classes of data in the training phase should be overcame. The experimental results of the proposed approach on five microarray databases show that the robustness metric of the feature selection process is in the interval of [0.70, 0.88]. Also the classification accuracy is in the range of [91%, 96%]. (C) 2014 Elsevier Inc. All rights reserved.

关键词： microarray classification Dimension reduction Filter method Wrapper method Support vector machine Imbalance classes

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A New Support Vector Machine for microarray classification and Adaptive Gene Selection

A New Support Vector Machine for Microarray Classification a...

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American Control Conference 2009

作者： Li, Juntao Jia, Yingmin Du, Junping Yu, Fashan Beihang Univ BUAA Res Div 7 Beijing 100191 Peoples R China Beijing Univ Posts & Telecommun Sch Comp Sci & Technol Beijing Key Lab Intelligent Telecommun Software & Beijing 100876 Peoples R China Henan Polytech Univ Sch Elect Engn & Automat Henan 454000 Peoples R China

ISBN: (纸本)9781424445233

This paper presents a new support vector machine for simultaneous gene selection and microarray classification. By introducing the adaptive elastic net penalty which is a convex combination of weighted 1-norm penalty and weighted 2-norm penalty, the proposed support vector machine can encourage an adaptive grouping effect and reduce the shrinkage bias for the large coefficients. According to a reasonable correlation between the two regularization parameters, the optimal coefficient paths are shown to be piecewise linear and the corresponding solving algorithm is developed. Experiments are performed on leukaemia data that verify the research results.

关键词： Gene selection grouping effect microarray classification solution path support vector machine (SVM)

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A New Support Vector Machine for microarray classification and Adaptive Gene Selection

A New Support Vector Machine for Microarray Classification a...

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2009 American Control Conference (ACC 2009)

作者： Juntao Li Yingmin Jia Junping Du Fashan Yu Seventh Research Division Beihang University (BUAA) Beijing 100191 China Beijing Key Laboratory of Intelligent Telecommunications Software and Multimedia School of Computer Science and Technology Beijing University of Posts and Telecommunications Beijing 100876 China School of Electrical Engineering and Automation Henan Polytechnic University Jiaozuo 454000 Henan China

ISBN: (纸本)9781424445233

关键词： Gene selection grouping effect microarray classification solution path support vector machine (SVM) Support Vector Network penalty Self tuning microarray

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Maximum weight and minimum redundancy: A novel framework for feature subset selection

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PATTERN RECOGNITION 2013年第6期46卷 1616-1627页

作者： Wang, Jianzhong Wu, Lishan Kong, Jun Li, Yuxin Zhang, Baoxue NE Normal Univ Coll Comp Sci & Informat Technol Changchun 130000 Jilin Peoples R China NE Normal Univ Natl Engn Lab Druggable Gene & Prot Screening Changchun 130000 Jilin Peoples R China NE Normal Univ Jilin Univ Key Lab Intelligent Informat Proc Changchun 130000 Jilin Peoples R China MOE Key Lab Appl Stat Changchun Peoples R China

Feature subset selection is often required as a preliminary work for many pattern recognition problems. In this paper, a novel filter framework is presented to select optimal feature subset based on a maximum weight and minimum redundancy (MWMR) criterion. Since the weight of each feature indicates its importance for some ad hoc tasks (such as clustering and classification) and the redundancy represents the correlations among features. Through the proposed MWMR, we can select the feature subset in which the features are most beneficial to the subsequent tasks while the redundancy among them is minimal. Moreover, a pair-wise updating based iterative algorithm is introduced to solve our framework effectively. In the experiments, three feature weighting algorithms (Laplacian score, Fisher score and Constraint score) are combined with two redundancy measurement methods (Pearson correlation coefficient and Mutual information) to test the performances of proposed MWMR. The experimental results on five different databases (CMU PIE, Extended YaleB, Colon, DLBCL and PCMAC) demonstrate the advantage and efficiency of our MWMR. (c) 2012 Elsevier Ltd. All rights reserved.

关键词： Feature selection Maximum weight and minimum redundancy Face recognition microarray classification Text categorization

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Studying the Robustness of Ensembles of Classifiers Used for Cancer Diagnosis Using microarrays Datasets

Studying the Robustness of Ensembles of Classifiers Used for...

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IEEE International Conference on Bioinformatics and Biomedicine (IEEE BIBM)

作者： Gaafar, Mohammed A. Yousri, Noha A. Ismail, Mohamed A. Univ Alexandria Alexandria Egypt

ISBN: (纸本)9781479913091;9781479913107

Ensembles of classifiers were shown to provide better accuracy than single classifiers. However, the classification robustness is an important performance measure for classifiers and ensembles, besides accuracy, that should be considered. Increasing the robustness of classification systems results in reducing the probability of over-fitting. The robustness, as defined in this study, has not been studied in the literature. In this paper, a framework is used to prove that ensembles of classifiers are more robust than single classifiers. The framework selects different ensembles of classifiers and compares their robustness to the robustness of their members. The experiments performed on six different microarray datasets showed that ensembles of classifiers are more robust than their members.

关键词： Ensemble Selection microarray classification Cancer classification Classifiers Robustness

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