Biomedical subjects

Stefano Merler

Publications and source records attributed to Stefano Merler.

4 recordsLinked to original sources

PubMed2006

Terminated Ramp-Support vector machines: a nonparametric data dependent kernel.

We propose a novel algorithm, Terminated Ramp-Support Vector Machines (TR-SVM), for classification and feature ranking purposes in the family of Support Vector Machines. The main improvement relies on the fact that the kernel is automatically determined by the training examples. It is built as a function of simple classifiers, generalized terminated ramp functions, obtained by separating oppositely labeled pairs of training points. The algorithm has a meaningful geometrical interpretation, and it is derived in the framework of Tikhonov regularization theory. Its unique free parameter is the regularization one, representing a trade-off between empirical error and solution complexity. Employing the equivalence between the proposed algorithm and two-layer networks, a theoretical bound on the generalization error is also derived, together with Vapnik-Chervonenkis dimension. Performances are tested on a number of synthetic and real data sets.

Algorithms↗

PubMed2003

Entropy-based gene ranking without selection bias for the predictive classification of microarray data.

BACKGROUND: We describe the E-RFE method for gene ranking, which is useful for the identification of markers in the predictive classification of array data. The method supports a practical modeling scheme designed to avoid the construction of classification rules based on the selection of too small gene subsets (an effect known as the selection bias, in which the estimated predictive errors are too optimistic due to testing on samples already considered in the feature selection process). RESULTS: With E-RFE, we speed up the recursive feature elimination (RFE) with SVM classifiers by eliminating chunks of uninteresting genes using an entropy measure of the SVM weights distribution. An optimal subset of genes is selected according to a two-strata model evaluation procedure: modeling is replicated by an external stratified-partition resampling scheme, and, within each run, an internal K-fold cross-validation is used for E-RFE ranking. Also, the optimal number of genes can be estimated according to the saturation of Zipf's law profiles. CONCLUSIONS: Without a decrease of classification accuracy, E-RFE allows a speed-up factor of 100 with respect to standard RFE, while improving on alternative parametric RFE reduction strategies. Thus, a process for gene selection and error estimation is made practical, ensuring control of the selection bias, and providing additional diagnostic indicators of gene importance.

Adenocarcinoma↗

PubMed2002

Geographical information systems and bootstrap aggregation (bagging) of tree-based classifiers for Lyme disease risk prediction in Trentino, Italian Alps.

The risk of exposure to Lyme disease in the province of Trento, Italian Alps, was predicted through the analysis of the distribution of Ixodes ricinus (L.) nymphs infected with Borrelia burgdorferi s.l. with a model based on bootstrap aggregation (bagging) of tree-based classifiers within a geographical information system (GIS). Data on L ricinus density assessed by dragging the vegetation in 438 sites during 1996 were cross-correlated with the digital cartography of a GIS, which included the variables altitude, exposure and slope, substratum, vegetation type and roe deer density. Ticks were more abundant at altitudes below 1,300 m a.s.l., in the presence of limestone and vegetation cover with thermophile deciduous forests and high densities of roe deer. A bootstrap aggregation procedure (bagging) was used to produce a model for the prediction of tick occurrence, the accuracy of which was tested on actual tick counts assessed by a further dragging campaign carried out during 1997 to determine infection prevalence and resulted in average 77%. Other tests of the model were made on additional and independent data sets. The prevalence of infection with Borrelia burgdorferi s.l, determined by polymerase chain reaction on 2,208 nymphs collected by random dragging in 245 transects selected within eight areas where the model predicted the occurrence of I. ricinus during 1997, was 17.5% and was positively correlated to tick abundance and roe deer density. These findings were used to relate the output of the bagged model (probability of tick occurrence) to the density of infected nymphs through a stepwise model selection procedure and thus to produce a GIS digital map of the probability distribution of infected nymphs in the Province of Trento at high resolution scale (50 by 50-m cell resolution). The application of the bagging procedure increased the accuracy of the prediction made by a single classification tree, a well-known classification method for the analysis of epidemiological data.

Animals↗

PubMedDate not supplied

Semisupervised learning for molecular profiling.

Class prediction and feature selection are two learning tasks that are strictly paired in the search of molecular profiles from microarray data. Researchers have become aware how easy it is to incur a selection bias effect, and complex validation setups are required to avoid overly optimistic estimates of the predictive accuracy of the models and incorrect gene selections. This paper describes a semisupervised pattern discovery approach that uses the by-products of complete validation studies on experimental setups for gene profiling. In particular, we introduce the study of the patterns of single sample responses (sample-tracking profiles) to the gene selection process induced by typical supervised learning tasks in microarray studies. We originate sample-tracking profiles as the aggregated off-training evaluation of SVM models of increasing gene panel sizes. Genes are ranked by E-RFE, an entropy-based variant of the recursive feature elimination for support vector machines (RFE-SVM). A Dynamic Time Warping (DTW) algorithm is then applied to define a metric between sample-tracking profiles. An unsupervised clustering based on the DTW metric allows automating the discovery of outliers and of subtypes of different molecular profiles. Applications are described on synthetic data and in two gene expression studies.

Algorithms↗