TY - GEN
T1 - First report of knowledge discovery in predicting protein folding rate change upon single mutation
AU - Lai, Lien Fu
AU - Wu, Chao Chin
AU - Huang, Liang Tsung
PY - 2011/12/1
Y1 - 2011/12/1
N2 - To explore the mechanism of protein folding is one of the important topics in protein research. The accurate prediction of protein folding rate change is helpful and useful in protein design. In earlier study, we have firstly analyzed the prediction of folding rate change upon single point mutation and constructed a non-redundant dataset of F467. F467 consists of 467 mutants with various features and widely distributed on secondary structure, solvent accessibility, conservation score and long-range contacts. In this work, we therefore focused on effectively developing the knowledge in F467 dataset. We have systematically analyzed the dataset and presented several representative data mining techniques, including decision tree, decision table and association rule algorithms. Furthermore, we have interpreted, evaluated, and compared the knowledge obtained from different techniques. The experimental results showed that the present approach can effectively develop the knowledge in the dataset and the outcomes can increase the understanding of predicting protein folding rate change upon single mutation. We have also created a website with related information about this work and it is freely available at http://bioinformatics. myweb.hinet.net/kdfreedom.htm .
AB - To explore the mechanism of protein folding is one of the important topics in protein research. The accurate prediction of protein folding rate change is helpful and useful in protein design. In earlier study, we have firstly analyzed the prediction of folding rate change upon single point mutation and constructed a non-redundant dataset of F467. F467 consists of 467 mutants with various features and widely distributed on secondary structure, solvent accessibility, conservation score and long-range contacts. In this work, we therefore focused on effectively developing the knowledge in F467 dataset. We have systematically analyzed the dataset and presented several representative data mining techniques, including decision tree, decision table and association rule algorithms. Furthermore, we have interpreted, evaluated, and compared the knowledge obtained from different techniques. The experimental results showed that the present approach can effectively develop the knowledge in the dataset and the outcomes can increase the understanding of predicting protein folding rate change upon single mutation. We have also created a website with related information about this work and it is freely available at http://bioinformatics. myweb.hinet.net/kdfreedom.htm .
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U2 - 10.1007/978-3-642-24553-4_83
DO - 10.1007/978-3-642-24553-4_83
M3 - Conference contribution
AN - SCOPUS:84862935249
SN - 9783642245527
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 624
EP - 631
BT - Bio-Inspired Computing and Applications - 7th International Conference on Intelligent Computing, ICIC 2011, Revised Selected Papers
T2 - 7th International Conference on Intelligent Computing, ICIC 2011
Y2 - 11 August 2011 through 14 August 2011
ER -