Elena Marchiori

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MOTIVATION The in silico prediction of potential interactions between drugs and target proteins is of core importance for the identification of new drugs or novel targets for existing drugs. However, only a tiny portion of all drug-target pairs in current datasets are experimentally validated interactions. This motivates the need for developing(More)
We provide here a systematic comparative study of the relative strength and expressive power of a number of methods for program analysis of Prolog. Among others we show that these methods can be arranged in the following hierarchy: mode analysis ⇒ type analysis ⇒ monotonic properties ⇒ nonmonotonic run-time properties. We also discuss a method allowing us(More)
In this paper we evaluate on-the-fly population (re)sizing mechanisms for evolutionary algorithms (EAs). Evaluation is done by an experimental comparison, where the contestants are various existing methods and a new mechanism, introduced here. These comparisons consider EA performance in terms of success rate, speed, and solution quality, measured on a(More)
In supervised learning, a training set consisting of labeled instances is used by a learning algorithm for generating a model (classifier) that is subsequently employed for deciding the class label of new instances (for generalization). Characteristics of the training set, such as presence of noisy instances and size, influence the learning algorithm and(More)
SUMMARY We describe a tool, called aCGH-Smooth, for the automated identification of breakpoints and smoothing of microarray comparative genomic hybridization (array CGH) data. aCGH-Smooth is written in visual C++, has a user-friendly interface including a visualization of the results and user-defined parameters adapting the performance of data smoothing and(More)
We introduce a simple compositional proof system for proving (partial) correctness of concurrent constraint programs (CCP). The proof system is based on a denotational approximation of the strongest postcondition semantics of CCP programs. The proof system is proved to be correct for full CCP and complete for the class of programs in which the denotational(More)
This paper presents a relational framework for studying properties of labeled data points related to proximity and labeling information in order to improve the performance of the 1NN rule. Specifically, the class conditional nearest neighbor (ccnn) relation over pairs of points in a labeled training set is introduced. For a given class label c, this(More)
Several evolutionary algorithms have been proposed for the satisfiability problem. We review the solution representations suggested in literature and choose the most promising one - the bit string representation - for further evaluation. An empirical comparison on commonly used benchmarks is presented for the most successful evolutionary algorithms and for(More)