Ben Goertzel, Cassio Pennachin, Lucio Coelho, Leonardo Shikida, Murilo Queiroz
Analysis of postgenomic biological data (such as microarray and SNP data) is a subtle art and science, and the statistical methods most commonly utilized sometimes prove inadequate. Machine learning techniques can provide superior understanding in many cases, but are rarely used due to their relative complexity and obscurity. A challenge, then, is to make machine learning approaches to data analysis available to the average biologist in a user-friendly way. This challenge is addressed by the Biomind ArrayGenius product, an easy-to-use Web-based system providing microarray analysis based on genetic programming, kernel methods, and incorporation of knowledge from biological ontologies; and GeneGenius, its sister product for SNP data. This paper focuses on the obstacles faced and lessons learned in the course of creating, deploying, maintaining and selling ArrayGenius and GeneGenius — many of which are generic to any effort involving the creation of complex AI-based products addressing complex domain problems.
Subjects: 1.6 Engineering And Science; 1.9 Genetic Algorithms
Submitted: Apr 2, 2007