title: A 9-state hidden Markov model using protein secondary structure information for protein fold recognition.
abstract: In protein fold recognition, the main disadvantage of hidden Markov models (HMMs) is the employment of large-scale model architectures which require large data sets and high computational resources for training. Also, HMMs must consider sequential information about secondary structures of proteins, to improve prediction performance and reduce model parameters. Therefore, we propose a novel method for protein fold recognition based on a hidden Markov model, called a 9-state HMM. The method can (i) reduce the number of states using secondary structure information about proteins for each fold and (ii) recognize protein folds more accurately than other HMMs.
authors.affiliation: Department of Computer Education, Chungbuk National University, 410 Sungbong-ro Heungduk-gu Cheongju, Chungbuk 312-763, South Korea.
authors.forename: Sun Young
authors.initials: SY
authors.lastname: Lee
authors.forename: Jong Yun
authors.initials: JY
authors.lastname: Lee
authors.forename: Kwang Su
authors.initials: KS
authors.lastname: Jung
authors.forename: Keun Ho
authors.initials: KH
authors.lastname: Ryu
mesh_terms: D008390:Markov Chains; D008958:Models, Molecular; D017510:Protein Folding; D017433:Protein Structure, Secondary