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BIOMARKER IDENTIFICATION IN BREAST CANCER: BETA-ADRENERGIC RECEPTOR SIGNALING AND PATHWAYS TO THERAPEUTIC RESPONSE

COMPUTATIONAL AND STRUCTURAL BIOTECHNOLOGY JOURNAL 2013年第7期6卷 e201303003页

作者： Kafetzopoulou, Liana E. Boocock, David J. Dhondalay, Gopal Krishna R. Powe, Desmond G. Ball, Graham R. Nottingham Trent Univ Sch Sci & Technol John Geest Canc Res Ctr Nottingham NGI 8NS England Univ Nottingham Nottingham Univ Hosp Trust Dept Cellular Pathol Nottingham NG7 2UH England

Recent preclinical studies have associated beta-adrenergic receptor (beta-AR) signaling with breast cancer pathways such as progression and metastasis. These findings have been supported by clinical and epidemiological studies which examined the effect of beta-blocker therapy on breast cancer metastasis, recurrence and mortality. Results from these studies have provided initial evidence for the inhibition of cell migration in breast cancer by beta-blockers and have introduced the beta-adrenergic receptor pathways as a target for therapy. This paper analyzes gene expression profiles in breast cancer patients, utilising Artificial Neural Networks (ANNs) to identify molecular signatures corresponding to possible disease management pathways and biomarker treatment strategies associated with beta-2-adrenergic receptor (ADRB2) cell signaling. The adrenergic receptor relationship to cancer is investigated in order to validate the results of recent studies that suggest the use of beta-blockers for breast cancer therapy. A panel of genes is identified which has previously been reported to play an important role in cancer and also to be involved in the beta-adrenergic receptor signaling.

关键词： Artificial Neural Networks microarray data Beta-2-Adrenergic Receptor beta-blockers

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Gene classification for microarray data with multiple time measurements

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BMC BIOINFORMATICS 2008年第SUPPL 7期9卷 P18-P18页

作者： Quiton, Jonathan Rinehart, Claire Chavarria-Smith, Joseph Rice, Nancy Western Kentucky Univ Dept Math Bowling Green KY 42101 USA Western Kentucky Univ Dept Biol Bowling Green KY 42101 USA

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Gene Expression data Classification by VVRKFA

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Procedia Technology 2012年 4卷 330-335页

作者： Santanu Ghorai Anirban Mukherjee Pranab K. Dutta Deptt. of AEIE Heritage Institute of TechnologyChowabaga Road Anandapur East Kolkata-700107W. B.India Deptt. of Electrical Engineering Indian Institute of Technology Kharagpu Kharagpur-721302W.B.India

An efficient approach of cancer classification using microarray expression data by vector-valued regularized kernel function approximation (VVRKFA) method is presented in a true computer aided diagnosis framework. A fast dimensionality reduction method based on maximum relevance minimum redundancy (MRMR) criteria is used to select very few genes so that both the classification accuracy and computational speed are enhanced. The experimental results are compared with support vector machines (SVM). It is observed that VVRKFA has achieved at least equal or better classification accuracy. This method also has the advantage that the separability of the data set can be observed in the label space.

关键词： cancer classification feature selection microarray data kernel function approximation

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A Comparison Of Validation Indices For Evaluation Of Clustering Results Of DNA microarray data

A Comparison Of Validation Indices For Evaluation Of Cluster...

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The 2nd International Conference on Bioinformatics and Biomedical Engineering(iCBBE 2008)(第二届生物信息与生物医学工程国际会议)

作者： Suzana Loshkovska Blagoj Ristevski Sasho Dzeroski Ivica Slavkov Department of Computer Science and Informatics Faculty of Electrical Eng. and Information Technologi Department of Knowledge Technologies Jozef Stefan Institute Ljubljana Slovenia

In this paper we assess the clustering results of gene expression (microarray) data by using different validation indices. We perform k-means clustering and evaluate the results by using three validation indices: Silhouette, Davies-Bouldin's and our proposed BR index. For performing the experiments we use our implementation of these measures in MATLAB. From the comparison of the validation indices we can conclude that BR index has better decreasing tendency depending on the number of clusters than Davies Bouldin's index for yeast microarray dataset.

关键词： clustering microarray data validation indez bioinformatics

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Identifying combinatorial transcription factor interactions with microarray data and ChIP-chip data

Identifying combinatorial transcription factor interactions ...

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The 2nd International Conference on Bioinformatics and Biomedical Engineering(iCBBE 2008)(第二届生物信息与生物医学工程国际会议)

作者： Ting Chen Feng Li Dept. of Electrical Engineering Fudan University Shanghai China

Combinatorial interactions of transcription factors play a key role in modulating transcriptional regulatory mechanisms in Saccharomyces cerevisiae. Here we apply correlation analysis to search potential combinatorial TF interactions with the integration of microarray data and ChIPchip data. Our results find that (ⅰ) eight significant cell cycle activators {Ace2, Fkh1, Fkh2, Mbp1, Ndd1, Swi4, Swi5, Swi6} are calculated to positively correlate with their respective targets;(ⅱ) seven TF pairs in nine important pairs have statistical correlation with their targets and three TF triplets in five triplets predicted by literature statistically correlate with shared targets. These results may highlight the enrichment of combinatorial TF interactions with statistical correlation. Besides, twenty-two TFs including the eight cell cycle activators are inferred to potentially interact with other TFs to regulate gene transcription in S. cerevisiae.

关键词： combinatorial TF interactions microarray data ChIP-chip data average composite ezpression profile time-lagged correlation

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Analysis of Gene Network in MCF-7 Human Breast Cancer Cells

Analysis of Gene Network in MCF-7 Human Breast Cancer Cells

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International Conference on Control, Automation and Systems

作者： Ryohei Shiraishi Takashi Nakakuki Major in Mechanical Engineering Kogakuin University Department of Mechanical Systems Engineering Kogakuin University

ISBN: (纸本)9781467322478

Recent technological progress on high-throughput measurements for gene expression such as microarray analysis enables us to collect time-series gene expression data for each of tens of thousands of genes. Although a genomic analysis with those data has identified key genes relating to various diseases, few results on estimation of gene regulatory networks with real microarray data are available so far. Recently, the immediately early response (IER) genes upon epidermal growth factor stimulation in a human breast cancer cell line, MCF-7, have been identified in which time-course microarray data were measured during 90 minutes and 63 IER genes were chosen from tens of thousands of genes by using statistical analysis. In this paper, we estimate the gene regulatory networks among the 63 IER genes. To this end, we apply an estimation method based on a mixed logic dynamical modeling developed in an earlier study to the microarray data. However, the original method is executable for continuous gene expression time-series data whereas the real microarray time-course data have very few time points. In addition, some presetting parameters in the model are critical for a successful result on a network estimation. Then, we add a preprocessing and Monte Carlo-based calculation for the original method.

关键词： Gene network Network estimation Mixed logic dynamical modeling Constrained optimization problem microarray data

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data integration model for cancer subtype identification using Kernel Dimensionality Reduction-Support Vector Machine (KDR-SVM)

Data integration model for cancer subtype identification usi...

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International Conference on Computer Sciences and Convergence Information Technology (ICCIT)

作者： Ito Wasito Aulia N. Istiqlal Indra Budi Faculty of Computer Science Universitas Indonesia Depok Indonesia

ISBN: (纸本)9781467308946

In this paper, an integration model of cancer patients data types such as microarray DNA and clinical data will be experimentally explored. The data of integration will be used for cancer subtype identification using kernel based classification methods which is the extension of Support Vector Machine (SVM) approach with Kernel Dimensionality Reduction (KDR). KDR-SVM method will be implemented in Lymphoma cancer database and the relevant clinical information. data type representation will be modeled in an appropriate kernel matrix. The results of the experiment show that the KDR-10 dimensions and data integration can improve the accuracy of the identification of subtype cancer.

关键词： data Integration microarray data Clinical data Kernel Dimensionality Reduction Support Vector Machine Kernel Matrix data integration Support Vector Network Seeds Cancer Cancer data types vector computer Kernel Classification Methods

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Predicting Prostate Cancer Progression with Penalized Logistic Regression Model Based on Co-expressed Genes

Predicting Prostate Cancer Progression with Penalized Logist...

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International Conference on Biomedical Engineering and Informatics

作者： Hongya Zhao Songru Qi Qi Dong Industrial center Shenzhen Polytechnic Shenzhen China Department of Muscle Biochemistry Mudanjiang Medical Colledge Mudanjiang China

ISBN: (纸本)9781467311830

The prediction of cancer progression is one of the most challenging problems in oncology. In this paper, we apply the penalized logistic model to microarray data in combination with co-expression genes to identify patients with prostate cancer progression. Compared with conventional methods, penalized logistic regression (PLR) has some advantages such as providing an estimate of the probability in classification label, genetic interpretation of regression coefficients, and short computation time. We employed the top score pair (TSP) approach to select genes for PLR. The TSP method was originally proposed for binary classification of phenotypes according to the relative expression of one gene pair. In the proposed algorithm of this paper, we first identified co-expressed TSP genes and then used PLR to the microarray data for predicting prostate cancer. We applied the framework to the microarray analysis on prostate cancer progression. We have identified three gene pairs associated with prostate cancer progression for PLR model. We compared our approach with the standard classification techniques such as support vector machines (SVMs), Lasso, and Fisher discriminative analysis (FDA). We found that our method yielded better performance in terms of classification and prediction. Furthermore, it has the advantages to provide the underlying probability of predicting the classification, robust biomarker genes and interpretable regression coefficients.

关键词： cancer progression microarray data feature selection penalized logistic regression top-score pair

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A Integrated Computational Approach for Protein Sub-network Detection in Parkinson's Disease

A Integrated Computational Approach for Protein Sub-network ...

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2010 3rd International Conference on Computer and Electrical Engineering(ICCEE 2010)

作者： Yue Huang Yunying Huang Institute of Signal and Information Processing Department of Communication Engineering Xiamen University Department of Electronic Engineering Xiamen University

Parkinson's disease (PD) is a typical case of neurodegenerative disorder, which often impairs the sufferer's motor skills, speech, and other functions. Combination of protein-protein interaction (PPI) network analysis and gene expression studies provides a better insight of Parkinson's disease. A computational approach was developed in our work to identify protein signal network in PD study. First, a linear regression model is setup and then a network-constrain regularization analysis was applied to microarray data from transgenic mouse model with Parkinson's disease. Then protein network was detected based on an integer linear programming model by integrating microarray data and PPI database.

关键词： Parkinson’s disease microarray data linear regression model integer linear programming protein network detection

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A Weighted Principal Component Analysis and Its Application to Gene Expression data

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IEEE-ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2011年第1期8卷 246-252页

作者： da Costa, Joaquim F. Pinto Alonso, Hugo Roque, Luis Univ Porto Fac Ciencias Dept Matemat P-4169007 Oporto Portugal Univ Porto CMUP Ctr Matemat Oporto Portugal Univ Lusofona Porto Fac Econ & Gestao P-4000098 Oporto Portugal Univ Aveiro Dept Matemat P-3810193 Aveiro Portugal Univ Aveiro CIDMA Aveiro Portugal Inst Super Engn Porto Grp Invest Engn Conhecimento & Apoio Decisao GECA P-4200072 Oporto Portugal

In this work, we introduce in the first part new developments in Principal Component Analysis (PCA) and in the second part a new method to select variables (genes in our application). Our focus is on problems where the values taken by each variable do not all have the same importance and where the data may be contaminated with noise and contain outliers, as is the case with microarray data. The usual PCA is not appropriate to deal with this kind of problems. In this context, we propose the use of a new correlation coefficient as an alternative to Pearson's. This leads to a so-called weighted PCA (WPCA). In order to illustrate the features of our WPCA and compare it with the usual PCA, we consider the problem of analyzing gene expression data sets. In the second part of this work, we propose a new PCA-based algorithm to iteratively select the most important genes in a microarray data set. We show that this algorithm produces better results when our WPCA is used instead of the usual PCA. Furthermore, by using Support Vector Machines, we show that it can compete with the Significance Analysis of microarrays algorithm.

关键词： Correlation principal component analysis support vector machines microarray data gene selection

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