Biomedical subjects

Lucia Sacchi

Publications and source records attributed to Lucia Sacchi.

3 recordsLinked to original sources

PubMed2006

Inferring gene expression networks via static and dynamic data integration.

This paper presents a novel approach for the extraction of gene regulatory networks from DNA microarray data. The approach is characterized by the integration of data coming from static and dynamic experiments, exploiting also prior knowledge on the biological process under analysis. A starting network topology is built by analyzing gene expression data measured during knockout experiments. The analysis of time series expression profiles allows to derive the complete network structure and to learn a model of the gene expression dynamics: to this aim a genetic algorithm search coupled with a regression model of the gene interactions is exploited. The method has been applied to the reconstruction of a network of genes involved into the Saccharomyces Cerevisiae cell cycle. The proposed approach was able to reconstruct known relationships among genes and to provide meaningful biological results.

Artificial Intelligence↗

PubMed2005

TA-clustering: cluster analysis of gene expression profiles through Temporal Abstractions.

This paper describes a new technique for clustering short time series of gene expression data. The technique is a generalization of the template-based clustering and is based on a qualitative representation of profiles which are labelled using trend Temporal Abstractions (TAs); clusters are then dynamically identified on the basis of this qualitative representation. Clustering is performed in an efficient way at three different levels of aggregation of qualitative labels, each level corresponding to a distinct degree of qualitative representation. The developed TA-clustering algorithm provides an innovative way to cluster gene profiles. We show the developed method to be robust, efficient and to perform better than the standard hierarchical agglomerative clustering approach when dealing with temporal dislocations of time series. Results of the TA-clustering algorithm can be visualized as a three-level hierarchical tree of qualitative representations and as such easy to interpret. We demonstrate the utility of the proposed algorithm on a set of two simulated data sets and on a study of gene expression data from S. cerevisiae.

Algorithms↗

PubMed2005

Comparison of two temporal abstraction procedures: a case study in prediction from monitoring data.

This paper presents an empirical comparison of two temporal abstraction procedures, that were applied to derive predictive features for a prediction problem in intensive care medicine. The first procedure employs knowledge from practitioners to derive qualitative patterns of state changes; the second procedure searches through a large number of data summaries to discover those that have predictive value. The derived features were used to predict whether postsurgical patients would need mechanical ventilation longer then 24h. The data-driven temporal abstraction procedure was found to provide more informative predictors, resulting in better predictions.

Cardiac Surgical Procedures↗