Trend Pattern Extraction from Microarray Data with Symbolic Encoding

Pattern Extraction from Data

Searching for patterns is one of the main goals in data mining. Patterns have important applications in many KDD domains like rule extraction or classiication. In this paper we present some methods of rule extraction by generalizing the existing approaches for the pattern problem. These methods, called partition of attribute values or grouping of attribute values, can be applied to decision tab...

Graph-driven features extraction from microarray data

Gene function prediction from microarray data is a first step toward better understanding the machinery of the cell from relatively cheap and easy-to-produce data. In this paper we investigate whether the knowledge of many metabolic pathways and their catalyzing enzymes accumulated over the years can help improve the performance of classifiers for this problem. The complex network of known bioc...

cDNA Microarray Data Normalization

A trend pattern assessment approach to microarray gene expression profiling data analysis

We study the problem of how to assess the reliability of a statistical measurement on data set containing unknown quantity of noises, inconsistencies, and outliers. A practical approach that analyzes the dynamical patterns (trends) of the statistical measurements through a sequential extreme-boundary-points (EBP) weed-out process is explored. We categorize the weed-out trend patterns (WOTP) and...

Feature Extraction from Optimization Data via DataModeler's Ensemble Symbolic Regression

We demonstrate a means of knowledge discovery through feature extraction that exploits the search history of an optimization run. We regress a symbolic model ensemble from optimization run search points and their objective scores. The frequency of a variable in the models of the ensemble indicates to what the extent it is an influential feature. Our demonstration uses a genetic programming symb...