Context sensitive vocabulary and its application in protein secondary structure prediction

Protein secondary structure prediction is an important step towards understanding the relation between protein sequence and structure. However, most current prediction methods use features difficult for biologists to interpret. In this paper, we present a new method that applies information retrieval techniques to solve the problem: we extract a context sensitive biological vocabulary for protein sequences and apply text classification methods to predict protein secondary structure. Experimental results show that our method performs comparably to the state-of-art methods. Furthermore, the context sensitive vocabularies can serve as a useful tool to discover meaningful regular expression patterns for protein structures. Categories and Subject Descriptors: H.4.4 [Information Systems Applications]: Miscellaneous General Terms: Algorithms, Experimentation.

• Type: Conference
• Year: 2004
• Where: SIGIR
• Authors: Yan Liu, Jaime G. Carbonell, Judith Klein-Seetharaman, Vanathi Gopalakrishnan
• Keywords: Protein Secondary Structure, Protein Sequences, Secondary Structure Prediction, SIGIR 2004