[PDF] Top 20 Microarray Gene Expression Data Classification using a Hybrid Algorithm: MRMRAGA
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Microarray Gene Expression Data Classification using a Hybrid Algorithm: MRMRAGA
... well-proven algorithm is known as a hybrid approach in order to design a new method to solve a ...The hybrid algorithm is normally built on the benefits of the existing approaches and hence ... See full document
8
Gene Selection for Tumor Classification Using Microarray Gene Expression Data
... significant gene expression analysis in different dimensions based on molecular profiles from microarray data, and compare several computational intelligent techniques for ... See full document
6
Classification approaches for microarray gene expression data analysis
... unknown gene sample based on microarray data using SVM and comparing the results with two other ...for classification of given microarray ...performed using different ... See full document
132
Tumor Classification by Partial Least Squares using Microarray Gene Expression Data
... the classification of tumors based on microarray gene expression ...p-dimensional gene expression space followed by logistic classification or ...channels ... See full document
13
Gene Ontology Analysis of 3D Microarray Gene Expression Data using Hybrid PSO Optimization
... 4. Gene Ontology Annotation The experimental study is conducted to CDC15 dataset is a microarray gene expression data with this link ...https://www.yeastgenome.org/goTermFinder. ... See full document
7
A Hybrid Gene Selection Method for Multi-category Tumor Classification using Microarray Data
... a hybrid gene selection strategy aiming to take advantage from the combination of different gene selection ...a gene pool, and a genetic algorithm further explores the search space for ... See full document
10
Novel approaches to biclustering and gene functional classification in microarray gene expression data
... frames using microarray gene expression ...internally, using cross validation, and externally, using protein sequence information and existing ‘wet lab ’ experimental ... See full document
143
Kernel-imbedded Gaussian processes for disease classification using microarray gene expression data
... of microarray gene expression data analysis, the majority of them are based on linear models, which however are not necessarily appropriate for the underlying connection between the target ... See full document
26
Mapping microarray gene expression data into dissimilarity spaces for tumor classification
... dissimilarity-based classification paradigm [26], samples to be clas- sified are encoded using pairwise dissimilarities (distances from other samples in the data ...linear classification model ... See full document
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The LeFE algorithm: embracing the complexity of gene expression in the interpretation of microarray data
... [40] using conventional t-statistics and confirmed that many of the fatty acid related genes are, indeed, significantly differentially expressed among the three classes of breast ...altered expression of ... See full document
14
A top-r feature selection algorithm for microarray gene expression data
... proposed algorithm is also compared to several other feature selection methods and promising results are ob- ...obtained gene subset was also highlighted by identifying its functional ...the ... See full document
11
A novel gene selection algorithm for cancer classification using microarray datasets
... our gene se- lection method for which candidates are constructed from two sets: terminal set (ts) and function set ...the microarray data is to minimize the number of unrelated ... See full document
12
A feature selection method using fixed-point algorithm for DNA microarray gene expression data
... fixed-point algorithm and show its application in the field of human cancer classification using DNA microarray gene expression ...fixed-point algorithm, we utilize ... See full document
11
Gene selection and classification of microarray data using random forest
... Most gene selection approaches in class prediction problems com- bine ranking genes ...(e.g., using an F-ratio or a Wilcoxon statistic) with a specific classifier ...for classification is a ... See full document
13
Title: Classification Techniques in Gene Expression Microarray Data
... ages. Gene expression data can serve to understand cancer or other types of disease ...Building classification system using gene expression dataset that can properly ... See full document
5
Complexity Reduced Tumor Classification System using Microarray Gene Expression Dataset
... The classification of cancer based on gene expression data is the advancements in DNA Microarray technology and genome ...purposes using such micro-array gene ... See full document
6
Microarray Gene Expression Data Clustering Using Red Black Tree Based K-Means Algorithm
... important data analysis methods and the k-means clustering is commonly used for diverse ...k-means algorithm is computationally expensive and the quality of clusters is determined by the random choice of ... See full document
5
MapReduce based Classification for Microarray data using Parallel Genetic Algorithm
... genes Microarray produces high throughput is used. Only few gene expression data out of thousands of data is used for disease predication and also for disease classification in ... See full document
7
Microarray data analysis: From hypotheses to conclusions using gene expression data
... of classification is cross- ...the data set at hand, because one has so many ‘predictors’ (all the genes) and usu- ally relatively few ‘outcomes’ (class label of the sam- ...misclassification using ... See full document
13
Vector Quantization of Microarray Gene Expression Data
... The data mining methods are used to find human-interpretable patterns that describe the data, for example, clustering, associations and ...or classification technique is to calculate the accuracy of ... See full document
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