Knowledge discovery in biomedical databases: a machine induction approach

The increase in the number and size of available databases by far exceeds the growth of the corresponding knowledge. Furthermore, many databases contain information which is not possessed by an existing human expert. This creates both a need and an opportunity for extracting knowledge from databases. An unsolved problem in molecular biology is the problem of predicting a protein's secondary structure from its primary structure. Inductive machine learning is a search for a plausible general description which can explain the given input data, and is useful for predicting new data. In this paper we present a statistical inductive algorithm which can be used to produce new rules for predicting multiple protein secondary structures from protein primary structure databases.