A Microarray Analysis Technique Using a Self-Organizing Multiagent Approach.

Microarray technology is fully established among the research fields in genetic domain. Academia and industrial researchers investigate and analyze genes' expression to obtain more and more useful information about given organisms, with the aim to perform better disease diagnosis and prediction, accurate medical data analysis, etc. Analyzing gene expression data, often available in raw form, implies a huge amount of analytical and computational complexities and therefore, innovative and intelligent mechanisms have to be designed to obtain useful information from this precious data. This chapter proposes a multiagent algorithm for building a distributed algorithm for DNA Microarray management. A collection of agents, in which each one representing a Microarray (or chip), execute in parallel a sequence of simple operations exploiting local information, and an organized virtual structure is built at global level. A word embeddings approach, able to capture the semantic context and represent Microarrays with vectors, is employed to map the chips, so allowing advanced agents' operations. A similarity-based overlay network of agents is brought out and an efficient management system of DNA Microarray is enabled. The generated virtual structure allows executing of informed operations, such as range queries, in a large dataset containing unstructured data. Preliminary results were confirm the validity of the algorithm proposed.