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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-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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Stability of gene contributions and identification of outliers in multivariate analysis of microarray data

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BMC BIOINFORMATICS 2008年第1期9卷 289-289页

作者： Baty, Florent Jaeger, Daniel Preiswerk, Frank Schumacher, Martin M. Brutsche, Martin H. Univ Basel Hosp CH-4031 Basel Switzerland Univ Basel Dept Comp Sci Basel Switzerland Novartis AG Biomarker Dev Basel Switzerland

Background: Multivariate ordination methods are powerful tools for the exploration of complex data structures present in microarray data. These methods have several advantages compared to common gene-by-gene approaches. However, due to their exploratory nature, multivariate ordination methods do not allow direct statistical testing of the stability of genes. Results: In this study, we developed a computationally efficient algorithm for: i) the assessment of the significance of gene contributions and ii) the identification of sample outliers in multivariate analysis of microarray data. The approach is based on the use of resampling methods including bootstrapping and jackknifing. A statistical package of R functions was developed. This package includes tools for both inferring the statistical significance of gene contributions and identifying outliers among samples. Conclusion: The methodology was successfully applied to three published data sets with varying levels of signal intensities. Its relevance was compared with alternative methods. Overall, it proved to be particularly effective for the evaluation of the stability of microarray data.

关键词： microarray data Correspondence Analysis Ordination Method Gene Contribution Sarcoidosis Patient

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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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Extracting Gene-Gene Interactions Through Curve Fitting

IEEE TRANSACTIONS ON NANOBIOSCIENCE

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IEEE TRANSACTIONS ON NANOBIOSCIENCE 2012年第4期11卷 402-409页

作者： Das, Ranajit Mitra, Sushmita Murthy, C. A. Indian Stat Inst Machine Intelligence Unit Kolkata 700108 India

This paper presents a simple and novel curve fitting approach for generating simple gene regulatory subnetworks from time series gene expression data. microarray experiments simultaneously generate massive data sets and help immensely in the large-scale study of gene expression patterns. Initial biclustering reduces the search space in the high-dimensional microarray data. The least-squares error between fitting of gene pairs is minimized to extract a set of gene-gene interactions, involving transcriptional regulation of genes. The higher error values are eliminated to retain only the strong interacting gene pairs in the resultant gene regulatory subnetwork. Next the algorithm is extended to a generalized framework to enhance its capability. The methodology takes care of the higher-order dependencies involving multiple genes co-regulating a single gene, while eliminating the need for user-defined parameters. It has been applied to the time-series Yeast data, and the experimental results biologically validated using standard databases and literature.

关键词： Gene expressions gene interaction network least-squares fitting microarray data transcriptional regulation

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Bayesian Model Selection for High-dimensional High-throughput data

Bayesian Model Selection for High-dimensional High-throughpu...

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作者： Joshi, Adarsh Texas A&M University

Bayesian methods are often criticized on the grounds of subjectivity. Furthermore, misspecified priors can have a deleterious effect on Bayesian inference. Noting that model selection is effectively a test of many hypotheses, Dr. Valen E. Johnson sought to eliminate the need of prior specification by computing Bayes’ factors from frequentist test statistics. In his pioneering work that was published in the year 2005, Dr. Johnson proposed using so-called local priors for computing Bayes? factors from test statistics. Dr. Johnson and Dr. Jianhua Hu used Bayes’ factors for model selection in a linear model setting. In an independent work, Dr. Johnson and another colleage, David Rossell, investigated two families of non-local priors for testing the regression parameter in a linear model setting. These non-local priors enable greater separation between the theories of null and alternative hypotheses. In this dissertation, I extend model selection based on Bayes’ factors and use nonlocal priors to define Bayes’ factors based on test statistics. With these priors, I have been able to reduce the problem of prior specification to setting to just one scaling parameter. That scaling parameter can be easily set, for example, on the basis of frequentist operating characteristics of the corresponding Bayes’ factors. Furthermore, the loss of information by basing a Bayes’ factors on a test statistic is minimal. Along with Dr. Johnson and Dr. Hu, I used the Bayes’ factors based on the likelihood ratio statistic to develop a method for clustering gene expression data. This method has performed well in both simulated examples and real datasets. An outline of that work is also included in this dissertation. Further, I extend the clustering model to a subclass of the decomposable graphical model class, which is more appropriate for genotype data sets, such as single-nucleotide polymorphism (SNP) data. Efficient FORTRAN programming has enabled me to apply the methodology to hundreds of

关键词： Bayes factors Bayes factors based on test statistics Bayesian Graphs MCMC Objective BayesianAnalysis Bayesian Model Selection microarray data Single-nucleotide polymorphism (SNP) data Thesis

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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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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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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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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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