Data-driven subclassification of speech sound disorders in preschool children.

Purpose: The purpose of the study was to determine whether distinct subgroups of preschool children with speech sound disorders (SSD) could be identified using a subgroup discovery algorithm (SUBgroup discovery via Alternate Random Processes, or SUBARP). Of specific interest was finding evidence of a subgroup of SSD exhibiting performance consistent with atypical speech motor control.

Method: Ninety-seven preschool children with SSD completed speech and nonspeech tasks.

Logic integer programming models for signaling networks.

We propose a static and a dynamic approach to model biological signaling networks, and show how each can be used to answer relevant biological questions. For this, we use the two different mathematical tools of Propositional Logic and Integer Programming. The power of discrete mathematics for handling qualitative as well as quantitative data has so far not been exploited in molecular biology, which is mostly driven by experimental research, relying on first-order or statistical models.

Application of a new logic domain method for the diagnosis of hepatocellular carcinoma.

In this paper we describe the application of a new learning tool for the diagnosis of hepatocellular carcinoma. The method adopted operates in the logic domain and presents several interesting features for the development of medical diagnostic systems. We consider a database of 128 patients, 64 of which affected by hepatocellular carcinoma, while the others affected by cirrhosis but not from hepatocellular carcinoma.