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Gene regulatory network from microarray data of colon cancer patients using TSK-type recurrent neural fuzzy network

GENE 2012年第2期506卷 408-416页

作者： Vineetha, S. Bhat, C. Chandra Shekara Idicula, Sumam Mary Rajiv Gandhi Inst Technol Dept Comp Sci Kottayam Kerala India Natl Inst Interdisciplinary Sci & Technol Trivandrum Kerala India Cochin Univ Sci & Technol Cochin 682016 Kerala India

In this work we applied a TSK-type recurrent neural fuzzy approach to extract regulatory relationship among genes and reconstruct gene regulatory network from microarray data. The identified signature has captured the regulatory relationship among 27 differentially expressed genes from microarray dataset. We applied three different methods viz., feed forward neural fuzzy, modified genetic algorithm and recurrent neural fuzzy, on the same data set for the inference of GRNs and the results obtained are almost comparable. In all tested cases, TRNFN identified more biologically meaningful relations. We found that 87.8% of the total interactions extracted by TRNFN are correct in accordance with the biological knowledge. Our analysis resulted in 2 major outcomes. First, upregulated genes are regulated by more genes than downregulated genes. Second, tumor activators activate other tumor activators and suppress tumor suppressers strongly in the disease environment. These findings will help to elucidate the common molecular mechanism of colon cancer, and provide new insights into cancer diagnostics, prognostics and therapy. (C) 2012 Elsevier B.V. All rights reserved.

关键词： microarray data Gene regulatory network Recurrent neural fuzzy network Fuzzy logic

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An effective double-bounded tree-connected Isomap algorithm for microarray data classification

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PATTERN RECOGNITION LETTERS 2012年第1期33卷 9-16页

作者： Orsenigo, C. Vercellis, C. Politecn Milan Dept Management Econ & Ind Engn I-20156 Milan Italy

Isometric mapping (Isomap) is a popular nonlinear dimensionality reduction technique which has shown high potential in visualization and classification. However, it appears sensitive to noise or scarcity of observations. This inadequacy may hinder its application for the classification of microarray data, in which the expression levels of thousands of genes in a few normal and tumor sample tissues are measured. In this paper we propose a double-bounded tree-connected variant of Isomap, aimed at being more robust to noise and outliers when used for classification and also computationally more efficient. It differs from the original Isomap in the way the neighborhood graph is generated: in the first stage we apply a double-bounding rule that confines the search to at most k nearest neighbors contained within an epsilon-radius hypersphere;the resulting subgraphs are then joined by computing a minimum spanning tree among the connected components. We therefore achieve a connected graph without unnaturally inflating the values of k and epsilon. The computational experiences show that the new method performs significantly better in terms of accuracy with respect to Isomap, k-edge-connected Isomap and the direct application of support vector machines to data in the input space, consistently across seven microarray datasets considered in our tests. (C) 2011 Elsevier B.V. All rights reserved.

关键词： Nonlinear dimensionality reduction Isomap microarray data Support vector machines

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Virulence factor prediction in Streptococcus pyogenes using classification and clustering based on microarray data

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APPLIED MICROBIOLOGY AND BIOTECHNOLOGY 2012年第5期93卷 2091-2098页

作者： Lopez-Kleine, Liliana Torres-Aviles, Francisco Tejedor, Fabio H. Gordillo, Luz A. Univ Nacl Colombia Dept Estadist Bogota Colombia Univ Santiago Chile Dept Matemat & Ciencia Computac Santiago 8330111 Chile

Interesting biological information as, for example, gene expression data (microarrays), can be extracted from publicly available genomic data. As a starting point in order to narrow down the great possibilities of wet lab experiments, global high throughput data and available knowledge should be used to infer biological knowledge and emit biological hypothesis. Here, based on microarray data, we propose the use of cluster and classification methods that have become very popular and are implemented in freely available software in order to predict the participation in virulence mechanisms of different proteins coded by genes of the pathogen Streptococcus pyogenes. Confidence of predictions is based on classification errors of known genes and repetitive prediction by more than three methods. A special emphasis is done on the nonlinear kernel classification methods used. We propose a list of interesting candidates that could be virulence factors or that participate in the virulence process of S. pyogenes. Biological validations should start using this list of candidates as they show similar behavior to known virulence factors.

关键词： Classification microarray data Protein function Statistical genomics Support vector machines Virulence factor

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Optimal significance analysis of microarray data in a class of tests whose null statistic can be constructed

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TEST 2012年第2期21卷 280-300页

作者： Fujisawa, Hironori Sakaguchi, Takayuki Inst Stat Math Tachikawa Tokyo 1908562 Japan Oita Univ Nursing & Hlth Sci Oita 8701201 Japan

microarray data often consist of a large number of genes and a small number of replicates. We have examined testing the null hypothesis of equality of mean for detecting differentially expressed genes. The p-value for each gene is often estimated using permutation samples not only for the target gene but also for other genes. This method has been widely used and discussed. However, direct use of the permutation method for the p-value estimation may not work well, because two types of genes are mixed in the sample;some genes are differentially expressed, whereas others are not. To overcome this difficulty, various methods for appropriately generating null permutation samples have been proposed. In this paper, we consider two classes of test statistics that are naturally modified to null statistics. We then obtain the uniformly most powerful (UMP) unbiased tests among these classes. If the underlying distribution is symmetric, the UMP unbiased test statistic is similar to that proposed by Pan (Bioinformatics 19:1333-1340, 2003). Under another condition, the UMP unbiased test statistic has a different formula with one more degree of freedom and therefore is expected to give a more powerful test and a more accurate p-value estimation from a modified null statistic. In microarray data, because the number of replicates is often small, differences in the degree of freedom will produce large effects on the power of test and the variance of the p-value estimation. Some simulation studies and real data analyses are illustrated to investigate the performances of the methods.

关键词： microarray data Multiple test Null statistic Small sample Uniformly most powerful unbiased test

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CTen: a web-based platform for identifying enriched cell types from heterogeneous microarray data

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BMC GENOMICS 2012年第1期13卷 1-11页

作者： Shoemaker, Jason E. Lopes, Tiago J. S. Ghosh, Samik Matsuoka, Yukiko Kawaoka, Yoshihiro Kitano, Hiroaki JST ERATO KAWAOKA Infect Induced Host Responses P Tokyo Japan Syst Biol Inst Tokyo Japan Univ Wisconsin Sch Vet Med Dept Pathobiol Sci Influenza Res Inst Madison WI 53706 USA Univ Tokyo Inst Med Sci Dept Microbiol & Immunol Div Virol Tokyo Japan Sony Comp Sci Labs Inc Tokyo Japan Okinawa Inst Sci & Technol Open Biol Unit Okinawa Japan

Background: Interpreting in vivo sampled microarray data is often complicated by changes in the cell population demographics. To put gene expression into its proper biological context, it is necessary to distinguish differential gene transcription from artificial gene expression induced by changes in the cellular demographics. Results: CTen (cell type enrichment) is a web-based analytical tool which uses our highly expressed, cell specific (HECS) gene database to identify enriched cell types in heterogeneous microarray data. The web interface is designed for differential expression and gene clustering studies, and the enrichment results are presented as heatmaps or downloadable text files. Conclusions: In this work, we use an independent, cell-specific gene expression data set to assess CTen's performance in accurately identifying the appropriate cell type and provide insight into the suggested level of enrichment to optimally minimize the number of false discoveries. We show that CTen, when applied to microarray data developed from infected lung tissue, can correctly identify the cell signatures of key lymphocytes in a highly heterogeneous environment and compare its performance to another popular bioinformatics tool. Furthermore, we discuss the strong implications cell type enrichment has in the design of effective microarray workflow strategies and show that, by combining CTen with gene expression clustering, we may be able to determine the relative changes in the number of key cell types. CTen is available at http://***/similar to jshoemaker/cten/

关键词： Cell type enrichment microarray data Deconvolution Influenza Systems immunology

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Combining literature text mining with microarray data: advances for system biology modeling

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BRIEFINGS IN BIOINFORMATICS 2012年第1期13卷 61-82页

作者： Faro, Alberto Giordano, Daniela Spampinato, Concetto Univ Catania Fac Engn I-95124 Catania Italy

A huge amount of important biomedical information is hidden in the bulk of research articles in biomedical fields. At the same time, the publication of databases of biological information and of experimental datasets generated by high-throughput methods is in great expansion, and a wealth of annotated gene databases, chemical, genomic (including microarray datasets), clinical and other types of data repositories are now available on the Web. Thus a current challenge of bioinformatics is to develop targeted methods and tools that integrate scientific literature, biological databases and experimental data for reducing the time of database curation and for accessing evidence, either in the literature or in the datasets, useful for the analysis at hand. Under this scenario, this article reviews the knowledge discovery systems that fuse information from the literature, gathered by text mining, with microarray data for enriching the lists of down and upregulated genes with elements for biological understanding and for generating and validating new biological hypothesis. Finally, an easy to use and freely accessible tool, GeneWizard, that exploits text mining and microarray data fusion for supporting researchers in discovering gene-disease relationships is described.

关键词： literature text mining microarray data biological databases knowledge discovery

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Identification of dysregulatedmiRNAs-genes network in ovarian cancer: An integrative approach to uncover the molecular interactions and oncomechanisms

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CANCER REPORTS 2020年第6期3卷 e1286页

作者： Kadkhoda, Sepideh Darbeheshti, Farzaneh Tavakkoly-Bazzaz, Javad Univ Tehran Med Sci Sch Med Dept Med Genet Tehran Iran Univ Sci Educ & Res Network USERN Breast Canc Assoc BrCA Tehran Iran

Background Ovarian (OV) cancer is considered as one of the most deadly malignancies in women, since it is unfortunately diagnosed in advanced stages. Nowadays, the importance of bioinformatics tools and their frequent usage in tracking dysregulated cancer-related genes and pathways have been highlighted in researches. Aim The aim of this study is to investigate dysregulated miRNAs-genes network and its function in OV tumors based on the integration of microarray data through a system biology approach. Methods Two microarray data (GSE119056 and GSE4122) were analyzed to explore the differentially expressed miRNAs (DEmiRs) and genes among OV tumors and normal tissues. Then, through the help of TargetScan, miRmap, and miRTarBase databases, the dysregulated miRNA-gene network in OV tumors was constructed by Cytoscape. In the next step, co-expression and protein-protein interaction networks were made using GEPIA and STRING databases. Moreover, the functional analysis of the hub genes was done by DAVID, KEGG, and Enrichr databases. Eventually, the regulatory network of TF-miRNA-gene was constructed. Results The potential dysregulated miRNAs-genes network in OV tumors has been constructed, including 109 differentially expressed genes (DEGs), 25 DEmiRs, and 213 interactions. Two down-regulated microRNAs, miR-660-3p and hsa-miR-4510, have the most interactions with up-expressed oncogenic DEGs. CDK1, PLK1, CCNB1, CCNA2, and EZH2 are involved in protein module, which show significant overexpression in OV tumors according to The Cancer Genome Atlas (TCGA) data. EZH2 shows amplification in OV tumors with remarkable percentage. The transcription factors TFAP2C and GATA4 have the pivotal regulatory functions in oncotranscriptomic profile of OV tumors. Conclusion In current study, we have collected and integrated different data to uncover the complex molecular interactions and oncomechanisms in OV tumors. The DEmiRs-DEGs and TF-miRNA-gene networks reveal the potential interactions

关键词： bioinformatics microarray data miRNA ovarian cancer systems biology

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Subtype dependent biomarker identification and tumor classification from gene expression profiles

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KNOWLEDGE-BASED SYSTEMS 2018年 146卷 104-117页

作者： Wang, Aiguo An, Ning Chen, Guilin Liu, Li Alterovitz, Gil Hefei Univ Technol Sch Comp & Informat Hefei Anhui Peoples R China Chuzhou Univ Sch Comp & Informat Engn Chuzhou Peoples R China Chongqing Univ Sch Software Engn Chongqing Peoples R China Harvard Med Sch Ctr Biomed Informat Boston MA USA MIT Dept Elect Engn & Comp Sci Cambridge MA 02139 USA

Gene expression profiles are being used to categorize disease specific genes and classify different tumor subtypes at the molecular level. Due to the inherent nature of these data having high dimensionality and small sample sizes, current conventional machine learning and statistical techniques have drawbacks in achieving satisfactory predictive classification performance in clinical samples. The typical approach to handling this situation is to eliminate noisy and redundant genes from the original gene space. There are currently multiple gene selection methods available, but most of them seek to find a common subset of genes for all tumor subtypes and fail to reflect the unique characteristics of each subtype. Consequently, in this study, we propose a general framework that aims to identify subset of genes for each tumor subtype, and also give another gene selection framework that combines the obtained subtype specific gene subsets into a single gene subset. We then present a corresponding classification model for distinguishing different tumor subtypes, and implement three specific gene selection algorithms within the two frameworks. Finally, extensive experimental results on the six benchmark microarray data validate the proposed tumor subtype dependent selection process to predict and rank specific molecular biomarkers to define tumor subtypes. This new process contributes significantly to the enhancement of tumor-predictive classification performance. (C) 2018 Elsevier B.V. All rights reserved.

关键词： Biomarker identification Tumor subtype Gene selection microarray data Subtype dependent

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Identification of target gene and prognostic evaluation for lung adenocarcinoma using gene expression meta-analysis, network analysis and neural network algorithms

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JOURNAL OF BIOMEDICAL INFORMATICS 2018年 86卷 120-134页

作者： Selvaraj, Gurudeeban Kaliamurthi, Satyavani Kaushik, Aman Chandra Khan, Abbas Wei, Yong-Kai Cho, William C. Gu, Keren Wei, Dong-Qing Henan Univ Technol Coll Food Sci & Engn Ctr Interdisciplinary Sci Computat Life Sci Zhengzhou Henan Peoples R China Henan Univ Technol Coll Chem Chem Engn & Environm Zhengzhou Henan Peoples R China Henan Univ Technol Coll Sci Zhengzhou Henan Peoples R China Queen Elizabeth Hosp Dept Clin Oncol Kowloon Hong Kong Peoples R China Shanghai Jiao Tong Univ Coll Life Sci & Biotechnol State Key Lab Microbial Metab Dept Bioinformat Shanghai Peoples R China

Background: Lung adenocarcinoma (LUAD) is a heterogeneous disease with poor survival in the advanced stage and a high incidence rate in the world. Novel drug targets are urgently required to improve patient treatment. Therefore, we aimed to identify therapeutic targets for LUAD based on protein-protein and protein-drug interaction network analysis with neural network algorithms using mRNA expression profiles. Results: A comprehensive meta-analysis of selective non-small cell lung cancer (NSCLC) mRNA expression profile datasets from Gene Expression Omnibus were used to identify potential biomarkers and the molecular mechanisms related to the prognosis of NSCLC patients. Using the Network Analyst tool, based on combined effect size (ES) methods, we recognized 6566 differentially expressed genes (DEGs), which included 3036 downregulated and 3530 upregulated genes linked to NSCLC patient survival. ClueGO, a Cytoscape plugin, was exploited to complete the function and pathway enrichment analysis, which disclosed "regulated exocytosis", "purine nucleotide binding", "pathways in cancer", and "cell cycle" between exceptionally supplemented terms. Enrichr, a web tool examination, demonstrated "early growth response protein 1 (EGR-1)", "hepatocyte nuclear factor 4 alpha (HNF4A)", "mitogen-activated protein kinase 14 (MAP3K14)", and "cyclin-dependent kinase 1 (CDK1)" to be among the most prevalent TFs and kinases associated with NSCLC. Our meta-analysis identified that MAPK1 and aurora kinase (AURKA) are the most obvious class of hub nodes. Furthermore, protein-drug interaction network and neural network algorithms identified candidate drugs such as phosphothreonine and 4-(4-methylpiperazin-1-yl)-n-[5-(2-thienylacetyl)-1,5-dihydropyrrolo[3,4-c]pyrazol-3-yl] benzamide and for the targets MAPK1 and AURKA, respectively. Conclusion: Our study has identified novel candidate biomarkers, pathways, transcription factors (TFs), and kinases associated with NSCLC prognosis, as well as dr

关键词： Hub nodes microarray data Lung adenocarcinoma Neural network Network analyst STRING Walktrap module

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Computational synchronization of microarray data with application to Plasmodium falciparum

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PROTEOME SCIENCE 2012年第1期10卷 1-17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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