Sentence examples for microarray data classification from inspiring English sources
In this paper, a novel Bio-inspired Multi-objective algorithm is proposed for gene selection in microarray data classification specifically in the binary domain of feature selection.
Canedo et al. [5] propose an ensemble of filters and classifiers for microarray data classification.
Some properties determine that the ANMM4CBR can be well applied to microarray data classification.
It has been applied successfully to various bioinformatics problems including breast cancer studies, 21 protein structure prediction, 22, 23 gene function prediction, 24 protein-protein interaction prediction, 25 protein-ligand interaction prediction 26 and microarray data classification.
In a microarray data classification problem, we are given N training samples, where x i is an M-dimensional vector in the feature space and y i ∈ {0, ⋯ K - 1} is the class label.
A recent comparison of feature selection methods for multiclass microarray data classification (Chai and Domeniconi, 2004) shows that wrapper methods such as SVM-RFE lead to better classification accuracy for large number of features, but often gives lower accuracy than filter methods when the number of selected features is very small.
Similar classification performance was achieved using real-time PCR data compared to that of microarray data: the classification accuracy, sensitivity and specificity for the testing set based on TaqMan® assay data were 80 %, 71, and 100% respectively (Figure 4).
We applied these optimized regression procedures in two very differing genomic settings: predicting survival of cancer patients from microarray data, and classification of lean and obese individuals from metagenomic sequence data.
This would provide a possibility to directly compare new measurements to previous data and to use SVM model trained on historical microarray data for classification of new samples measured on alternative platforms.
Arisi, I. et al. Gene expression biomarkers in the brain of a mouse model for Alzheimer's disease: mining of microarray data by logic classification and feature selection.
It is essential to select a minimal number of relevant genes from microarray data while maximizing classification accuracy for the development of inexpensive diagnostic tests.
