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Data Mining Application in Biomedical Informatics for Probing into Protein Stability upon Double Mutation |
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PP: 125-132 |
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Author(s) |
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Liang-Tsung Huang,
Chao-Chin Wu,
Lien-Fu Lai,
M. Michael Gromiha,
Chang-Sheng Wang,
Yet-Ran Chen,
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Abstract |
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| To explore the mechanism of protein stability change is one of the important topics in protein design. The accurate prediction
of protein stability change upon mutation is very useful for enhancing the experimental efficiency in many biological and medical
studies. In this work, we aimed at effectively introducing data mining technologies for investigating the understanding of protein
stability change upon double mutation. We constructed a non-redundant dataset of protein mutants with various attributes and applied
systematically analyses on the dataset. Therefore, we developed general knowledge from the dataset by several data mining techniques,
including decision tree, decision table and association rule algorithms. Furthermore, we interpreted, evaluated, and compared those
knowledge outcomes obtained from different techniques. The observations on the experimental results demonstrated that the present
method may serve as an effective tool in biomedical informatics to understand the prediction of protein stability change upon double
mutation. |
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