Data mining application in biomedical informatics for probing into protein stability upon double mutation

Liang Tsung Huang, Chao-Chin Wu, Lien-Fu Lai, M. Michael Gromiha, Chang Sheng Wang, Yet Ran Chen

Research output: Contribution to journalArticle

2 Citations (Scopus)

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 languageEnglish
Pages (from-to)125-132
Number of pages8
JournalApplied Mathematics and Information Sciences
Volume8
Issue number1 L
DOIs
Publication statusPublished - 2014 Jan 1

Fingerprint

Data mining
Data Mining
Mutation
Proteins
Protein
Decision tables
Decision Table
Prediction
Association rules
Association Rules
Decision trees
Decision tree
Mutant
Attribute
Experimental Results
Knowledge

All Science Journal Classification (ASJC) codes

  • Analysis
  • Numerical Analysis
  • Computer Science Applications
  • Computational Theory and Mathematics
  • Applied Mathematics

Cite this

Huang, Liang Tsung ; Wu, Chao-Chin ; Lai, Lien-Fu ; Gromiha, M. Michael ; Wang, Chang Sheng ; Chen, Yet Ran. / Data mining application in biomedical informatics for probing into protein stability upon double mutation. In: Applied Mathematics and Information Sciences. 2014 ; Vol. 8, No. 1 L. pp. 125-132.
@article{bcd06822ffd14d7caf07d4b5c0db596a,
title = "Data mining application in biomedical informatics for probing into protein stability upon double mutation",
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.",
author = "Huang, {Liang Tsung} and Chao-Chin Wu and Lien-Fu Lai and Gromiha, {M. Michael} and Wang, {Chang Sheng} and Chen, {Yet Ran}",
year = "2014",
month = "1",
day = "1",
doi = "10.12785/amis/081L16",
language = "English",
volume = "8",
pages = "125--132",
journal = "Applied Mathematics and Information Sciences",
issn = "1935-0090",
publisher = "Natural Sciences Publishing Corporation",
number = "1 L",

}

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, 01.01.2014, p. 125-132.

Research output: Contribution to journalArticle

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 - 2014/1/1

Y1 - 2014/1/1

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 -