检索结果-内蒙古大学图书馆

An efficient gene selection algorithm based on mutual information

NEUROCOMPUTING 2009年第4-6期72卷 991-999页

作者： Cai, Ruichu Hao, Zhifeng Yang, Xiaowei Wen, Wen S China Univ Technol Sch Engn & Comp Sci Guangzhou 510640 Guangdong Peoples R China S China Univ Technol Coll Math Sci Guangzhou 510640 Guangdong Peoples R China

Gene selection, a significant preprocessing of the discriminant analysis of microarray data, is to select the most informative genes from the whole gene set. In this paper, an efficient mutual information-based gene selection algorithm (MIGS) is proposed, in which genes are sequentially forward selected according to an approximate measure of the mutual information between the class and the selected genes. In order to improve the efficiency of the MIGS, an effective pruning strategy is introduced in the selection procedure as well as the employment of Parzen window density estimation technique. Extensive experiments are conducted on three public gene expression datasets and the experimental results confirm the efficiency and effectiveness of the algorithm, Though the computational cost of MIGS-Pruning increases with the number of selected genes, it still has good performance applied in the microarray problems. (C) 2008 Elsevier B.V. All rights reserved.

关键词： microarray data Gene expression Gene selection Mutual information Parzen window density estimation

来源：评论

学校读者我要写书评

暂无评论

Virtual Screening of Abl Inhibitors from Large Compound Libraries by Support Vector Machines

引用

JOURNAL OF CHEMICAL INFORMATION AND MODELING 2009年第9期49卷 2101-2110页

作者： Liu, X. H. Ma, X. H. Tan, C. Y. Jiang, Y. Y. Go, M. L. Low, B. C. Chen, Y. Z. Natl Univ Singapore Bioinformat & Drug Design Grp Dept Pharm Ctr Computat Sci & Engn Singapore 117543 Singapore Tsinghua Univ Key Lab Chem Biol Grad Sch Shenzhen Shenzhen 518057 Guangdong Peoples R China Natl Univ Singapore Dept Biol Sci Singapore 117543 Singapore

Abl promotes cancers by regulating cell morphogenesis, motility, growth, and survival. Successes of several marketed and clinical trial Abl inhibitors against leukemia and other cancers and appearances of reduced efficacies and drug resistances have led to significant interest in and efforts for developing new Abl inhibitors. In silico methods of pharmacophore, fragment, and molecular docking have been used in some of these efforts. It is desirable to explore other in silico methods capable of searching large compound libraries at high yields and reduced false-hit rates. We evaluated support vector machines (SVM) as a virtual screening tool for searching Abl inhibitors from large compound libraries. SVM trained and tested by 708 inhibitors and 65 494 putative noninhibitors correctly identified 84.4 to 92.3% inhibitors and 99.96 to 99.99% noninhibitors in 5-fold cross validation studies. SVM trained by 708 pre-2008 inhibitors and 65 494 putative noninhibitors Correctly identified 50.5% of the 91 inhibitors reported since 2008 and predicted as inhibitors 29 072 (0.21%) of 13.56M PubChem, 659 (0.39%) of 168K MDDR, and 330 (5.0%) of 6 638 MDDR compounds similar to the known inhibitors. SVM showed comparable yields and substantially reduced false-hit rates against two similarity based and another machine learning VS methods based on the same training and testing data sets and molecular descriptors. These suggest that SVM is capable of searching Abl inhibitors from large compound libraries at low false-hit rates.

关键词： TYROSINE KINASE INHIBITORS LEARNING-METHODS DRUG DISCOVERY NEURAL-NETWORK BCR-ABL PHYSICOCHEMICAL PROPERTIES CHEMICAL LIBRARIES ACTIVE COMPOUNDS microarray data PREDICTION virtual screen protein tyrosine kinase inhibitor Support Vector Network BCR-ABL Fusion Gene hit rate compounds Small Molecule Libraries Libraries Neural network Drug Discovery

来源：评论

学校读者我要写书评

暂无评论

A maximally diversified multiple decision tree algorithm for microarray data classification 06

A maximally diversified multiple decision tree algorithm for...

引用

Proceedings of the 2006 workshop on Intelligent systems for bioinformatics - Volume 73

作者： Hong Hu Jiuyong Li Hua Wang Grant Daggard Mingren Shi University of Southern Queensland Toowoomba QLD Australia

ISBN: (纸本)9781920682545

We investigate the idea of using diversified multiple trees for microarray data classification. We propose an algorithm of Maximally Diversified Multiple Trees (MDMT), which makes use of a set of unique trees in the decision committee. We compare MDMT with some well-known ensemble methods, namely AdaBoost, Bagging, and Random Forests. We also compare MDMT with a diversified decision tree algorithm, Cascading and Sharing trees (CS4), which forms the decision committee by using a set of trees with distinct roots. Based on seven microarray data sets, both MDMT and CS4 are more accurate on average than AdaBoost, Bagging, and Random Forests. Based on a sign test of 95% confidence, both MDMT and CS4 perform better than majority traditional ensemble methods tested. We discuss differences between MDMT and CS4.

关键词： decision tree microarray data diversified classifiers ensemble classifier

来源：评论

学校读者我要写书评

暂无评论

A comparative study of classification methods for microarray data analysis 06

A comparative study of classification methods for microarray...

引用

Proceedings of the fifth Australasian conference on data mining and analystics - Volume 61

作者： Hong Hu Jiuyong Li Ashley Plank Hua Wang Grant Daggard University of Southern Queensland Toowoomba QLD Australia

ISBN: (纸本)9781920682415

In response to the rapid development of DNA microarray technology, many classification methods have been used for microarray classification. SVMs, decision trees, Bagging, Boosting and Random Forest are commonly used methods. In this paper, we conduct experimental comparison of LibSVMs, C4.5, BaggingC4.5, AdaBoostingC4.5, and Random Forest on seven microarray cancer data sets. The experimental results show that all ensemble methods outperform C4.5. The experimental results also show that all five methods benefit from data preprocessing, including gene selection and discretization, in classification accuracy. In addition to comparing the average accuracies of ten-fold cross validation tests on seven data sets, we use two statistical tests to validate findings. We observe that Wilcoxon signed rank test is better than sign test for such purpose.

关键词： microarray data classification

来源：评论

学校读者我要写书评

暂无评论

Robust data imputation

引用

COMPUTATIONAL BIOLOGY AND CHEMISTRY 2009年第1期33卷 7-13页

作者： Branden, Karlien Vanden Verboven, Sabine Univ Antwerp Dept Math Stat & Actuarial Sci B-2000 Antwerp Belgium Commiss European Communities Joint Res Ctr I-21020 Ispra Italy

Single imputation methods have been wide-discussed topics among researchers in the field of bioinformatics. One major shortcoming of methods proposed until now is the lack of robustness considerations. Like all data, gene expression data can possess outlying values. The presence of these outliers could have negative effects on the imputated values for the missing values. Afterwards, the outcome of any statistical analysis on the completed data could lead to incorrect conclusions. Therefore it is important to consider the possibility of outliers in the data set, and to evaluate how imputation techniques will handle these values. In this paper, a simulation study is performed to test existing techniques for data imputation in case outlying values are present in the *** some shortcomings of the existing imputation techniques, a new robust imputation method that can deal with the presence of outliers in the data is introduced. In addition, the robust imputation procedure cleans the data for further statistical analysis. Moreover, this method can be easily extended towards a Multiple imputation approach by which the uncertainty of the imputed values is emphasised. Finally, a classification example illustrates the lack of robustness of some existing imputation methods and shows the advantage of the multiple imputation approach of the new robust imputation technique. (c) 2008 Elsevier Ltd. All rights reserved.

关键词： Missing genes microarray data Imputation methods Robust statistics

来源：评论

学校读者我要写书评

暂无评论

Use of Bayesian networks to probabilistically model and improve the likelihood of validation of microarray findings by RT-PCR

引用

JOURNAL OF BIOMEDICAL INFORMATICS 2009年第2期42卷 287-295页

作者： English, Sangeeta B. Shih, Shou-Ching Ramoni, Marco F. Smith, Lois E. Butte, Atul J. Stanford Univ Sch Med Stanford Ctr Biomed Informat Res BMIR Stanford CA 94305 USA Beth Israel Deaconess Med Ctr Res N Dept Pathol Boston MA 02215 USA Childrens Hosp Boston Dept Ophthalmol Boston MA 02115 USA Harvard Univ Sch Med Harvard Partners Ctr Genet & Genom Boston MA 02115 USA Harvard Univ Sch Med Harvard MIT Div Hlth Sci & Technol Childrens Hosp Informat Program Boston MA 02115 USA

Though genome-wide technologies, such as microarrays, are widely used, data from these methods are considered noisy: there is still varied Success in downstream biological validation. We report a method that increases the likelihood Of Successfully validating microarray findings using real time RT-PCR. including genes at low expression levels and with small differences. We use a Bayesian network to identify the most relevant sources of noise based on the successes and failures in validation for an initial set of selected genes, and then improve Our subsequent selection of genes for validation based on eliminating these sources of noise. The network displays the significant Sources of noise in an experiment, and scores the likelihood of validation for every gene. We show how the method can significantly increase validation success rates. In conclusion, in this study, we have Successfully added a new automated step to determine the contributory sources of noise that determine successful or unsuccessful downstream biological validation. (C) 2008 Elsevier Inc. All rights reserved.

关键词： Bioinformatics Bayesian network microarray RT-PCR microarray data

来源：评论

学校读者我要写书评

暂无评论

Adaptive clustering for time series: Application for identifying cell cycle expressed genes

引用

COMPUTATIONAL STATISTICS & data ANALYSIS 2009年第4期53卷 1414-1426页

作者： Douzal-Chouakria, Ahlame Diallo, Alpha Giroud, Francoise Univ Grenoble 1 LAB TIMC CNRSTIMB IMAGIN3SFac MedUMR 5525 F-38706 La Tronche France Univ Grenoble 1 CNRS RFMQ TIMC IMAG UMR 5525 F-38706 La Tronche France

The biological problem of identifying the active genes during the cell division process is addressed. The cell division ensures the proliferation of cells, which is drastically aberrant in cancer cells. The studied genes are described by their expression profiles during the cell division cycle. Commonly, the identification process is a supervised approach based on an a priori set of reference genes, assumed as well-characterizing the cell cycle phases. Each studied gene is then classified by its peak similarity to one pre-specified reference gene. This classical approach suffers from two limitations. On the one hand, there is no consensus between biologists about the set of reference genes to consider for the identification process. On the other hand, the proximity measures used for genes expression profiles are unjustified and mainly based on the expression values regardless of the genes expression behavior. To identify genes expression profiles, a new adaptive clustering approach is proposed which consists of two main points. First, it allows in an unsupervised way the selection of a well-justified set of reference genes, to be compared with the pre-specified ones. Secondly, it enables the users to learn the appropriate proximity measure to use for genes expression data, a measure which will cover both proximity on values and on behavior. The adaptive clustering method is compared to a correlation-based approach through public and simulated genes expression data. (C) 2008 Elsevier B.V. All rights reserved.

关键词： LOGISTIC-REGRESSION microarray data CLASSIFICATION TRANSFORMATION VALIDATION SELECTION Genes Cell Cycle Logistic Regression Cell Division Proximity Cell Cycle Phase

来源：评论

学校读者我要写书评

暂无评论

In Silico Evaluation of Predicted Regulatory Interactions in Arabidopsis thaliana

引用

BMC BIOINFORMATICS 2009年第1期10卷 1-15页

作者： Nero, Damion Katari, Manpreet S. Kelfer, Jonathan Tranchina, Daniel Coruzzi, Gloria M. NYU Dept Biol Ctr Genom & Syst Biol New York NY 10003 USA NYU Courant Inst Math Sci New York NY 10003 USA

Background: Prediction of transcriptional regulatory mechanisms in Arabidopsis has become increasingly critical with the explosion of genomic data now available for both gene expression and gene sequence composition. We have shown in previous work [1], that a combination of correlation measurements and cis-regulatory element (CRE) detection methods are effective in predicting targets for candidate transcription factors for specific case studies which were validated. However, to date there has been no quantitative assessment as to which correlation measures or CRE detection methods used alone or in combination are most effective in predicting TF -> target relationships on a genome-wide scale. Results: We tested several widely used methods, based on correlation (Pearson and Spearman Rank correlation) and cis-regulatory element (CRE) detection (>= 1 CRE or CRE over-representation), to determine which of these methods individually or in combination is the most effective by various measures for making regulatory predictions. To predict the regulatory targets of a transcription factor ( TF) of interest, we applied these methods to microarray expression data for genes that were regulated over treatment and control conditions in wild type (WT) plants. Because the chosen data sets included identical experimental conditions used on TF over-expressor or T-DNA knockout plants, we were able to test the TF. target predictions made using microarray data from WT plants, with microarray data from mutant/transgenic plants. For each method, or combination of methods, we computed sensitivity, specificity, positive and negative predictive value and the F-measure of balance between sensitivity and positive predictive value (precision). This analysis revealed that the >= 1 CRE and Spearman correlation (used alone or in combination) were the most balanced CRE detection and correlation methods, respectively with regard to their power to accurately predict regulatory-target interactions. Con

关键词： microarray data Positive Predictor Value Spearman Correlation microarray dataset Regulatory Prediction

来源：评论

学校读者我要写书评

暂无评论

Gene selection and parameter determination of support vector machines based on BPSO algorithm

引用

INTERNATIONAL JOURNAL OF MODELLING IDENTIFICATION AND CONTROL 2009年第4期8卷 344-352页

作者： Li, Shutao Wu, Xixian Tan, Mingkui Hunan Univ Coll Elect & Informat Engn Changsha 410082 Hunan Peoples R China

The advent of microarray technology allows researchers to perform cancer diagnosis on gene level. However, the microarray data usually contains a large quantity of irrelevant, noisy and redundant genes which may seriously deteriorate the prediction accuracy. What's more, microarray data always suffers from the problem of a relatively small number of samples, which raises the difficulties in diagnosis. Accordingly, gene selection plays an important role in cancer diagnosis using microarray data. As a state-of-the-art classifier, support vector machine (SVM) has been successfully applied in solving the small-sample recognition problems and therefore is very suitable for microarray data processing. However, to improve the recognition accuracy, it is essential to choose certain good enough parameters for SVM in applications. This study proposes a new gene selection method based on a binary version of particle swarm optimisation (BPSO) algorithm and SVM. In the method, the task of gene selection and parameter tuning of SVM is performed simultaneously by BPSO. To verify the proposed method, three benchmark dataset (leukaemia, breast and colon) are studied. The experimental results show that the proposed method is highly effective and competitive compared with other methods.

关键词： microarray data cancer diagnosis gene selection particle swarm optimisation PSO support vector machine SVM

来源：评论

学校读者我要写书评

暂无评论

Integrating Gene Expression and Phenotypic Information to Analyze Alzheimer's Disease

引用

JOURNAL OF ALZHEIMERS DISEASE 2009年第1期16卷 73-84页

作者： Ray, Monika Zhang, Weixiong Washington Univ Dept Comp Sci & Engn St Louis MO USA Washington Univ Sch Med Dept Genet St Louis MO 63110 USA

The assessment of the relationship between gene expression profiling, clinical and histopathological phenotypes would be better suited to understanding Alzheimer's disease (AD) pathogenesis. We developed a multiple linear regression (MLR) method to simultaneously model three variables - Mini-Mental Status Examination (MMSE) score, neurofibrillary tangles (NFT) score and gene expression profile - to identify significant genes. These genes were also used to distinguish subjects with incipient AD from healthy controls. Finally we investigated the behavior of the significant genes across the entorhinal cortex and hippocampus of AD subjects in two different Braak stages. Results indicate that integrating multiple phenotypic and gene expression information of samples increases the power of methods while analyzing small datasets. The MLR method could identify significant genes at reasonable false discovery rates (FDRs), thereby providing a choice of reasonable FDRs. The accuracy in discriminating between subjects affected and unaffected by AD using MLR identified genes was high. We found that transcription and tumor suppressor responses do begin quite early in AD and therefore should be the target of drugs. Several genes were consistently up/down-regulated across the two brain regions and Braak stages and, therefore, can be used as predictive markers to detect AD at an earlier stage.

关键词： Alzheimer's disease classification gene selection microarray data Mini-Mental Status Examination neurofibrillary tangles score

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：