[PDF] Top 20 An Embedded Gene Selection Method for Gene Expression Data
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An Embedded Gene Selection Method for Gene Expression Data
... a gene selection process, and K-nearest neighbor (KNN) with Leave-one-out cross validation (LOOCV) method serve as a classifier for six classification ...proposed method simplified the ... See full document
13
Improved binary PSO for feature selection using gene expression data
... crossover. For exam- ple, the random parameters rand 1 and rand 2 (in ...towards an optimal ...searching for only one ...looks for the best ... See full document
5
Design of an Integrated and Effective Platform for Gene Expression Data Mining
... relative expression levels in two or more mRNA populations derived from tissue samples can be assayed for thousands of genes simultaneously [4, ...approaches for large-scale gene ... See full document
14
An efficient method for mining cross-timepoint gene regulation sequential patterns from time course gene expression datasets
... of data mining, especially for the data- base ...potential gene regulations occurred in a period of time, it could be identi- fied by mining such sequential patterns from a dataset- converted ... See full document
12
An evolutionary approach for gene expression patterns
... the expression levels of a large number of genes simultaneously. Gene clustering and gene ordering are important in analyzing a large body of microarray expression ...proposed method ... See full document
10
An evolutionary approach for gene expression patterns
... the expression levels of a large number of genes simultaneously. Gene clustering and gene ordering are important in analyzing a large body of microarray expression ...proposed method ... See full document
10
Clustering Gene Expression Time Series Data
... clustering method we addressed is divided into three parts: First, data preprocessing, second clusters adjusting, and third stop criterion ...the gene expression time series data set ... See full document
6
Interpretable gene expression classifier with an accurate and compact fuzzy rule base for microarray data analysis
... iGEC 97.1 87.9 3.9 5.0 1.1 V200 81.5 81.2 4.9 7.2 1.4 V15 79.6 77.6 5.4 7.4 1.6 age (average distance, UPGMA) clustering based on Euclidean distances squared by EPCLUST (Parkinson et al., 2003). Fig. 7 shows the ... See full document
12
An integrated approach for genome-wide gene expression analysis
... developed for determining biosequences, a lot of biosequence data has been ...the data instead of the data acquisition, part of the study of computational biology is to extract all kinds of ... See full document
12
An expert system to identify co-regulated gene groups from time-lagged gene clusters using cell cycle expression data
... edge method strongly focuses on local similarity between two gene expression curves, there are al- ways too few edges that match between gene pairs and this gives rise to relatively low ... See full document
12
A Hybrid BPSO-CGA Approach for Gene Selection and Classification of Microarray Data
... in gene expressions, and changes in the transcription rates of an entire genome in ...Microarray gene expression profiles indicate the relative abundance of mRNA corresponding to the ...The ... See full document
7
Regulate-SAGE: Mining Software for Serial Analysis of Gene Expression Data
... of gene expression) is a powerful method that allows the analysis of complete gene expression patterns with computer-aided digital ...NCI-CGAP for paralleled comparison ... See full document
5
Gene selection and sample classification on microarray data based on adaptive genetic algorithm / k-nearest neighbor method
... performance for dimension reduc- tion and gene selection on gene expression ...good for dimension ...microarray data. After using this proposed method, biologists ... See full document
7
An integrative tool for gene regulatory network reconstruction based on microarray data
... analysis method where the genes expression relationships between genes ...factors for each related ...the gene regulatory network based on microarray ... See full document
2
A hybrid feature selection method for DNA microarray data
... t Gene expression profiles, which represent the state of a cell at a molecular level, have great potential as a medical diagnosis ...training data sets are generally of a fairly small sample size ... See full document
5
A new regularized least squares support vector regression for gene selection
... similar gene expressions contribute in a similar ...weights for tissue ...weighted expression sums. The results of empirical data analysis show that the proposed selection procedure ... See full document
27
Improved Feature Selection on Microarray Expression Data
... Feature Selection, Filter, Wrapper, Support Vector Machine, Microarray ...tool for biologists to discover the gene ...genes expression data could be processed quickly. However, the ... See full document
6
Identifying Regulatory Targets of Cell Cycle Transcription Factors Using Gene Expression and ChIP-chip Data
... Since co-expressed genes are not necessarily co-regulated and vice versa [21], it is important to develop a method that can identify co-regulated genes that are not co-expressed. TRIA has the ability to do this ... See full document
6
Reducing the Variation of Gene Expression Patterns: A Grey Model Approach Applied to Microarray Data Classification
... GM(1,1)-GA/MLHD method begin with setting 100 runs by following the program, with each run beginning with a different initial gene pool in order to have an unbiased estimate of classifier ... See full document
7
An expert system to classify microarray gene expression data using gene selection by decision tree
... Document type:Journal article (JA) Publisher:Elsevier Ltd, Langford Lane, Kidlington, Oxford, OX5 1GB, United Kingdom Abstract:Gene selection can help the analysis of microarray gene ... See full document
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