Machine Learning
An accelerated procedure for recursive feature ranking on microarray data
Neural Networks - 2003 Special issue: Advances in neural networks research IJCNN'03
Semisupervised Learning for Molecular Profiling
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
CliDaPa: A new approach to combining clinical data with DNA microarrays
Intelligent Data Analysis - Knowledge Discovery in Bioinformatics
The Depth Problem: Identifying the Most Representative Units in a Data Group
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
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We propose a combination of machine learning techniques to integrate predictive profiling from gene expression with clinical and epidemiological data. Starting from BioDCV, a complete software setup for predictive classification and feature ranking without selection bias, we apply semisupervised profiling for detecting outliers and deriving informative subtypes of patients. During the profiling process, sampletracking curves are extracted, and then clustered according to a distance derived from dynamic time warping. Sampletracking allows also the identification of outlier cases, whose removal is shown to improve predictive accuracy and stability of derived gene profiles. Here we propose to employ clinical features to validate the semisupervising procedure. The procedure is demonstrated in the analysis of a liver cancer dataset of 213 samples described by 1993 genes and by pathological features.