On a Possible Similarity between Gene and Semantic Networks

TL;DR

This paper explores the potential similarity between gene and semantic networks by using stochastic multi-agent systems to model associations and analyze their dynamics, revealing common organizing principles in cognition.

Contribution

It introduces a novel perspective linking gene and semantic networks through stochastic modeling, suggesting shared organizational principles in complex systems.

Findings

Evidence of structural similarities between gene and semantic networks

Case studies demonstrating comparable association patterns

Proposal of a unified framework for understanding macro structures

Abstract

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.