Rule-based Machine Learning Methods for Functional Prediction

We describe a machine learning method for predicting the value of a real-valued function, given the values of multiple input variables. The method induces solutions from samples in the form of ordered disjunctive normal form (DNF) decision rules. A central objective of the method and representation is the induction of compact, easily interpretable solutions. This rule-based decision model can be extended to search efficiently for similar cases prior to approximating function values. Experimental results on real-world data demonstrate that the new techniques are competitive with existing machine learning and statistical methods and can sometimes yield superior regression performance.Comment: See http://www.jair.org/ for any accompanying file

Multi-Scale Stochastic Simulation of Diffusion-Coupled Agents and Its Application to Cell Culture Simulation

Many biological systems consist of multiple cells that interact by secretion and binding of diffusing molecules, thus coordinating responses across cells. Techniques for simulating systems coupling extracellular and intracellular processes are very limited. Here we present an efficient method to stochastically simulate diffusion processes, which at the same time allows synchronization between internal and external cellular conditions through a modification of Gillespie's chemical reaction algorithm. Individual cells are simulated as independent agents, and each cell accurately reacts to changes in its local environment affected by diffusing molecules. Such a simulation provides time-scale separation between the intra-cellular and extra-cellular processes. We use our methodology to study how human monocyte-derived dendritic cells alert neighboring cells about viral infection using diffusing interferon molecules. A subpopulation of the infected cells reacts early to the infection and secretes interferon into the extra-cellular medium, which helps activate other cells. Findings predicted by our simulation and confirmed by experimental results suggest that the early activation is largely independent of the fraction of infected cells and is thus both sensitive and robust. The concordance with the experimental results supports the value of our method for overcoming the challenges of accurately simulating multiscale biological signaling systems

Normalized Web Distance and Word Similarity

There is a great deal of work in cognitive psychology, linguistics, and computer science, about using word (or phrase) frequencies in context in text corpora to develop measures for word similarity or word association, going back to at least the 1960s. The goal of this chapter is to introduce the normalized is a general way to tap the amorphous low-grade knowledge available for free on the Internet, typed in by local users aiming at personal gratification of diverse objectives, and yet globally achieving what is effectively the largest semantic electronic database in the world. Moreover, this database is available for all by using any search engine that can return aggregate page-count estimates for a large range of search-queries. In the paper introducing the NWD it was called `normalized Google distance (NGD),' but since Google doesn't allow computer searches anymore, we opt for the more neutral and descriptive NWD. web distance (NWD) method to determine similarity between words and phrases

From Elementary Pragmatic Model (EPM) to Evolutive Elementary Pragmatic Model (E2PM)

The Elementary Pragmatic Model (EPM) is a high operative and didactic, versatile tool and new application areas are envisaged continuously. Recently, EPM contributed to find a solution to the double-bind problem in classic information and algorithmic theory. This new awareness allowed to enlarge our panorama for neurocognitive system behavior understanding. To cope with ontological uncertainty, it is possible to use two coupled irreducible information subsystems, based on an ideal asymptotic ichotomy: Information Reliable Predictability and Information Reliable Unpredictability. A natural operating point can emerge as a new Trans-disciplinary Reality Level, out of the Interaction of Two Complementary Irreducible Management Subsystems. It is possible to envisage an Evolutive Elementary Pragmatic Model (E2PM) able to profit by both classic EPM Self-Reflexive Functional Logical Closure and new evolutive Self-Reflective Functional Logical Aperture. This paper presents a relevant contribute to models and simulations offering an example of new forms of evolutive behavior inter- and interdisciplinary modeling