Computing Entropy for Ortholog Detection

Abstract-- Biological sequences from different species are called orthologs if they evolved from a sequence of a common ancestor species and they have the same biological function. Approximations of Kolmogorov complexity or entropy of biological sequences are already well known to be useful in extracting similarity information between such sequences -in the interest, for example, of ortholog detection. As is well known, the exact Kolmogorov complexity is not algorithmically computable. In practice one can approximate it by computable compression methods. However, such compression methods do not provide a good approximation to Kolmogorov complexity for short sequences. Herein is suggested a new approach to overcome the problem that compression approximations may not work well on short sequences. This approach is inspired by new, conditional computations of Kolmogorov entropy. A main contribution of the empirical work described shows the new set of entropy-based machine learning attribut...

• Type: Conference
• Year: 2004
• Where: IJIT
• Authors: Hsing-Kuo Pao, John Case
• Keywords: Artificial Intelligence, Attribute Sets, IJIT 2004, Kolmogorov Complexity, Short Sequences