Abstract
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.
| Original language | English |
|---|---|
| Pages (from-to) | 125-132 |
| Number of pages | 8 |
| Journal | Applied Mathematics and Information Sciences |
| Volume | 8 |
| Issue number | 1 L |
| DOIs | |
| Publication status | Published - 2014 Apr |
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All Science Journal Classification (ASJC) codes
- Analysis
- Numerical Analysis
- Computer Science Applications
- Computational Theory and Mathematics
- Applied Mathematics
Cite this
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Data mining application in biomedical informatics for probing into protein stability upon double mutation. / Huang, Liang Tsung; Wu, Chao Chin; Lai, Lien Fu; Gromiha, M. Michael; Wang, Chang Sheng; Chen, Yet Ran.
In: Applied Mathematics and Information Sciences, Vol. 8, No. 1 L, 04.2014, p. 125-132.Research output: Contribution to journal › Article
TY - JOUR
T1 - Data mining application in biomedical informatics for probing into protein stability upon double mutation
AU - Huang, Liang Tsung
AU - Wu, Chao Chin
AU - Lai, Lien Fu
AU - Gromiha, M. Michael
AU - Wang, Chang Sheng
AU - Chen, Yet Ran
PY - 2014/4
Y1 - 2014/4
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=84896807296&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84896807296&partnerID=8YFLogxK
U2 - 10.12785/amis/081L16
DO - 10.12785/amis/081L16
M3 - Article
AN - SCOPUS:84896807296
VL - 8
SP - 125
EP - 132
JO - Applied Mathematics and Information Sciences
JF - Applied Mathematics and Information Sciences
SN - 1935-0090
IS - 1 L
ER -