On the complexity of deriving score functions from examples for problems in molecular biology

Abstract

Score functions (potential functions) have been used effectively in many problems in molecular biology. We propose a general method for deriving score functions that are consistent with example data, which yields polynomial time learning algorithms for several important problems in molecular biology (including sequence alignment). On the other hand, we show that deriving a score function for some problems (multiple alignment and protein threading) is computationally hard. However, we show that approximation algorithms for these optimization problems can also be used for deriving score functions.