Microarray Data Classification Based on Computational Verb

Visualization-based cancer microarray data classification analysis

MOTIVATION Methods for analyzing cancer microarray data often face two distinct challenges: the models they infer need to perform well when classifying new tissue samples while at the same time providing an insight into the patterns and gene interactions hidden in the data. State-of-the-art supervised data mining methods often cover well only one of these aspects, motivating the development of ...

Blind Gene Classification Based on Ica of Microarray Data

The present study shows that an ICA-based method can, e®ectively and blindly, classify a vast amount of gene expression data into biologically meaningful groups. Speci ̄cally, we show (1) that genes, whose expression data are sampled at di®erent times, can be classi ̄ed into several groups, based on the correlation of each gene with independent component curves over time, and (2) that these class...

Multiclass microarray data classification based on confidence evaluation.

Microarray technology is becoming a powerful tool for clinical diagnosis, as it has potential to discover gene expression patterns that are characteristic for a particular disease. To date, this possibility has received much attention in the context of cancer research, especially in tumor classification. However, most published articles have concentrated on the development of binary classi...

biomarker discovery based on hybrid optimization algorithm and artificial neural networks on microarray data for cancer classification

the improvement of high-through-put gene profiling based microarrays technology has provided monitoring the expression value of thousands of genes simultaneously. detailed examination of changes in expression levels of genes can help physicians to have effi cient diagnosing, classification of tumors and cancer’s types as well as effective treatments. finding genes that can classify the group of...

cDNA Microarray Data Normalization