Powerful specialized software is essential for managing, quantifying, and ultimately deriving
scientific insight from results of a microarray experiment. We have developed a suite of …

The biological sciences are undergoing an explosion in the amount of available data. New
data analysis methods are needed to deal with the data. We present work using KDD to …

In the context of cancer diagnosis and treatment, we consider the problem of constructing an
accurate prediction rule on the basis of a relatively small number of tumor tissue samples of …

Traditional (univariate) analysis of functional MRI (fMRI) data relies exclusively on the
information contained in the time course of individual voxels. Multivariate analyses can take …

A guide to machine learning approaches and their application to the analysis of biological
data. An unprecedented wealth of data is being generated by genome sequencing projects …

Philosopher, sociologist and urban theorist, Henri Lefebvre is one of the great social
theorists of the twentieth century. This accessible and innovative introduction to the work of …

ABSTRACT Support Vector Machines (SVMs) and related kernel methods have become
increasingly popular tools for data mining tasks such as classification, regression, and …

MicroRNAs (miRNAs) are a non-coding family of genes involved in post-transcriptional gene
regulation. These transcripts are associated with cell proliferation, cell differentiation, cell …

Motivation: Gene expression profile analyses have been used in numerous studies covering
a broad range of areas in biology. When unreliable measurements are excluded, missing …

Microarray experiments are providing unprecedented quantities of genome-wide data on
gene-expression patterns. Although this technique has been enthusiastically developed and …