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Feature Selection with Fluid Mechanics Inspired Particle Swarm Optimization for microarray data

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Journal of Beijing Institute of Technology 2017年第4期26卷 517-524页

作者： Shengsheng Wang Ruyi Dong College of Computer Science and Technology Jilin UniversityChangchun 130012China Jilin Vocational College of Industry and Technology JilinJilin 132013China

Deoxyribonucleic acid（ DNA） microarray gene expression data has been widely utilized in the field of functional genomics,since it is helpful to study cancer,cells,tissues,organisms *** the sample sizes are relatively small compared to the number of genes,so feature selection is very necessary to reduce complexity and increase the classification accuracy of samples. In this paper,a completely newimprovement over particle swarm optimization（ PSO） based on fluid mechanics is proposed for the feature selection. This newimprovement simulates the spontaneous process of the air from high pressure to lowpressure,therefore it allows for a search through all possible solution spaces and prevents particles from getting trapped in a local optimum. The experiment shows that,this newimproved algorithm had an elaborate feature simplification which achieved a very precise and significant accuracy in the classification of 8 among the 11 datasets,and it is much better in comparison with other methods for feature selection.

关键词： feature selection particle swarm optimization (PSO) fluid mechanics (FM) microarray data support vector machine (SVM)

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Predictive Analysis of microarray data

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Open Journal of Genetics 2014年第1期4卷 63-68页

作者： Paulo C.Marques F. Carlos A.de B.Pereira Departamento de Estatistica Instituto de Matematica e EstatísticaUniversidade de Sao PauloSao PauloBrasil

microarray gene expression data are analyzed by means of a Bayesian nonparametric model, with emphasis on prediction of future observables, yielding a method for selection of differentially expressed genes and the cor... 详细信息

关键词： Bayesian Nonparametrics Dirichlet Process microarray data Differential Gene Expression Classification

A Novel Approach to Select Important Genes from microarray data

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A Novel Approach to Select Important Genes from Microarray D...

Identification of a 5-gene signature for clinical and prognostic prediction in gastric cancer patients upon microarray data

2011 Chinese Control and Decision Conference（CCDC）

作者： Xianchang Wang,Lishi Zhang and Junfu Du are with the School of Science,Dalian Ocean University,116023,Dalian,China

Feature subset selection is a well-known pattern recognition problem,which aims to reduce the number of features used in classification or *** reduction is expected to improve the performance of classification algorithms in terms of speed,accuracy and *** existing feature selection investigations are not suitable for microarray data,so this paper focuses on gene selection *** main contributions of this paper are that a new feature selection method A-score is introduced and constructed an improved fuzzy Bayesian *** evaluate the performance of Ascore using three well-known benchmark data sets:the iris data,the wine data,and the Wisconsin breast cancer data and two microarray data:ALL-AML Leukemia and colon *** general,A-score can significantly reduce the number of genes,and perform better than T-score and C-score.

关键词： microarray data Feature selection Important genes A-score

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Identification of a 5-gene signature for clinical and progno...

A Novel Approach to Revealing Positive and Negative Co-Regulated Genes

湖北省抗癌协会青年委员会第五届青年学术论坛

作者：王志刚 Zhi Yan Bicheng Zhang Zhiguo Rao Yafei Zhang Jian Liu Lifang Yu Yong Zhao Bo Yang Tingting Wu Jianfei Gao Department of Oncology Wuhan General Hospital of Guangzhou CommandPeople's Liberation Army Department of Digestive Diseases Wuhan General Hospital of Guangzhou CommandPeople's Liberation Army

In this study,we aimed to investigate the clinical and prognostic value of a 5-gene expression signature model for gastric cancer patients upon microarray data.A total of 158 gastric cancer patients were selected,with 33 cases used for microarray analysis as training set and 125 cases for validation real-time quantitative polymerase chain reaction analysis as test *** clustering algorithms and supervised clustering algorithm were used to identify differentially expressed *** ontology analyses were used to determine functional prediction of gene biomarkers and receiver operating characteristic analyses to verify the specificity and sensitivity of the ***,the correlation between clinicopathological characteristics and 5-gene expression was *** results showed that there were poor disease progression and clinic prognosis in the patients with proximal gastric cancer compared with distal gastric cancer, including differentiation grade(P=0.001),depth of tumor invasion(P<0.01),lymph node metastasis(P<0.01),UICC stage(P<0.01) and survival status(P<0.01).Furthermore,a 5-gene signature,including Cordon-bleu protein-like 1,Damage-specific DNA binding protein 1, BCL2-like 13,Nuclear receptor coactivator-6,and F-box leucine-rich protein 11,were identified based on the combination of multiple bioinformatics *** expression of 5-gene signature(HR=2.35;95%confidence interval(CI) 1.24-5.06;P=0.026) and UICC stage(HR=5.35;95%confidence interval 1.36-19.15;P=0.032) were independent prognostic factors for overall survival in the survival multivariate ***-expression of the 5-gene signature in patients with proximal gastric cancer is strongly associated with disease progression and poor prognosis, suggesting that might be potential prognostic predictors in gastric cancer.

关键词： gastric cancer gene signature prognostic predictor microarray data

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Journal of Computer Science & Technology 2007年第2期22卷 261-272页

作者：赵宇海王国仁印莹许光宇 Department of Computer Science and Engineering Northeastern University Shengyang 110004 China

As explored by biologists, there is a real and emerging need to identify co-regulated gene clusters, which include both positive and negative regulated gene clusters. However, the existing pattern-based and tendency-based clustering approaches are only designed for finding positive regulated gene clusters. In this paper, a new subspace clustering model called g-Cluster is proposed for gene expression data. The proposed model has the following advantages： 1） find both positive and negative co-regulated genes in a shot, 2） get away from the restriction of magnitude transformation relationship among co-regulated genes, and 3） guarantee quality of clusters and significance of regulations using a novel similarity measurement gCode and a user-specified regulation threshold δ, respectively. No previous work measures up to the task which has been set. Moreover, MDL technique is introduced to avoid insignificant g-Clusters generated. A tree structure, namely GS-tree, is also designed, and two algorithms combined with efficient pruning and optimization strategies to identify all qualified g-Clusters. Extensive experiments are conducted on real and synthetic datasets. The experimental results show that 1） the algorithm is able to find an amount of co-regulated gene clusters missed by previous models, which are potentially of high biological significance, and 2） the algorithms are effective and efficient, and outperform the existing approaches.

关键词： microarray data pattern-based clustering co-regulated genes

Correlating Expression data with Gene Function Using Gene Ontology

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Chinese Journal of Chemistry 2006年第9期24卷 1247-1254页

作者：刘琪邓勇王川石铁流李亦学 School of Life Science & Biotechnology Shanghai Jiao Tong University Shanghai 200240 China Zhejiang Police Vocational Academy Hangzhou Zhejiang 310018 China Biomformatton Center Shanghat Instttutesfor Btologtcal Sctences Chinese Academy of Sciences Shanghai 200031 China

Clustering is perhaps one of the most widely used tools for microarray data analysis. Proposed roles for genes of unknown function are inferred from clusters of genes similarity expressed across many biological conditions. However, whether function annotation by similarity metrics is reliable or not and to what extent the similarity in gene expression patterns is useful for annotation of gene functions, has not been evaluated. This paper made a comprehensive research on the correlation between the similarity of expression data and of gene functions using Gene Ontology. It has been found that although the similarity in expression patterns and the similarity in gene functions are significantly dependent on each other, this association is rather weak. In addition, among the three categories of Gene Ontology, the similarity of expression data is more useful for cellular component annotation than for biological process and molecular function. The results presented are interesting for the gene functions prediction research area.

关键词： microarray data gene ontology similarity of expression data function annotation

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

A DSRPCL-SVM Approach to Informative Gene Analysis

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Genomics, Proteomics & Bioinformatics 2008年第2期6卷 83-90页

作者： Wei Xiong Zhibin Cai Jinwen Ma School of Mathematical Sciences and Laboratory of Mathematics and Applied Mathematics （LMAM） PekingUniversity Beijing 100871 China

microarray data based tumor diagnosis is a very interesting topic in bioinformatics. One of the key problems is the discovery and analysis of informative genes of a tumor. Although there are many elaborate approaches to this problem, it is still difficult to select a reasonable set of informative genes for tumor diagnosis only with microarray data. In this paper, we classify the genes expressed through microarray data into a number of clusters via the distance sensitive rival penalized competitive learning （DSRPCL） algorithm and then detect the informative gene cluster or set with the help of support vector machine （SVM）. Moreover, the critical or powerful informative genes can be found through further classifications and detections on the obtained informative gene clusters. It is well demonstrated by experiments on the colon, leukemia, and breast cancer datasets that our proposed DSRPCL-SVM approach leads to a reasonable selection of informative genes for tumor diagnosis.

关键词： microarray data informative gene selection clustering analysis DSRPCL tumordiagnosis

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A Modified Ant Colony Optimization Algorithm for Tumor Marker Gene Selection

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Genomics, Proteomics & Bioinformatics 2009年第4期7卷 200-208页

作者： Hualong Yu Guochang Gu Haibo Liu Jing Shen Jing Zhao College of Computer Science and Technology Harbin Engineering University Harbin 150001 China.

microarray data are often extremely asymmetric in dimensionality, such as thousands or even tens of thousands of genes but only a few hundreds of samples or less. Such extreme asymmetry between the dimensionality of genes and samples can lead to inaccurate diagnosis of disease in clinic. Therefore, it has been shown that selecting a small set of marker genes can lead to improved classification accuracy. In this paper, a simple modified ant colony optimization （ACO） algorithm is proposed to select tumorelated marker genes, and support vector machine （SVM） is used as classifier to evaluate the performance of the extracted gene subset. Experimental results on several benchmark tumor microarray datasets showed that the proposed approach produces better recognition with fewer marker genes than many other methods. It has been demonstrated that the modified ACO is a useful tool for selecting marker genes and mining high dimension data

关键词： microarray data ant colony optimization marker gene selection support vector machine

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