An Analysis of New Expert Knowledge Scaling Methods for Biologically Inspired Computing

Abstract

High-throughput genotyping has made genome-wide data on human genetic variation commonly available, however, finding associations between specific variations and common diseases has proven difficult. Individual susceptibility to common diseases likely depends on gene-gene interactions, i.e. epistasis, and not merely on independent genes. Furthermore, genome-wide datasets present an informatic challenge because exhaustive searching within them for even pair-wise interactions is computationally infeasible. Instead, search methods must be used which efficiently and effectively mine these datasets. To meet these challenges, we turn to a biologically inspired ant colony optimization strategy. We have previously developed an ant system which allows the incorporation of expert knowledge as heuristic information. One method of scaling expert knowledge to probabilities usable in the algorithm, an exponential distribution function which respects intervals between raw expert knowledge scores, has been previously examined. Here, we develop and evaluate three additional expert knowledge scaling methods and find parameter sets for each which maximize power.