On a Possible Similarity between Gene and Semantic Networks
This work addresses the challenge of understanding holistic effects in fields such as systems biology and semantic web, though it appears incremental in applying existing methods to new data.
The paper tackles the problem of modeling macro structures in domains like linguistics and molecular biology by using a stochastic multi-agent system to analyze associations, showing that clustering around target objects reveals similarities in gene-gene and term-term relationships, suggesting a common organizing principle with random and deterministic effects.
In several domains such as linguistics, molecular biology or social sciences, holistic effects are hardly well-defined by modeling with single units, but more and more studies tend to understand macro structures with the help of meaningful and useful associations in fields such as social networks, systems biology or semantic web. A stochastic multi-agent system offers both accurate theoretical framework and operational computing implementations to model large-scale associations, their dynamics and patterns extraction. We show that clustering around a target object in a set of associations of object prove some similarity in specific data and two case studies about gene-gene and term-term relationships leading to an idea of a common organizing principle of cognition with random and deterministic effects.