Abstract
The prediction of stability change for protein mutants is one of the important issues in protein design. Recently, the prediction upon double mutation has attracted more and more attention. In this work, we have employed a data mining approach to discriminating stability change for protein double mutants. We incorporated a reliable rule induction algorithm along with accuracy of 82.2% to construct rule-based knowledge patterns. Further, a fuzzy query method was utilized to value important and similar rule patterns for an input with partial sequence information. The results showed that the approach has two major advantages: (i) A rule-based knowledge representation offers intuitive interpretation on raw data, which is helpful to understand the content; and (ii) A fuzzy query method incorporates the concept of uncertainty, which can make predictions from partial information. Based on the proposed approach, we have also developed a web service for predicting protein stability change upon double mutation from partial sequence information and it is available at http://bioinformatics.myweb.hinet.net/tandem.htm.
| Original language | English |
|---|---|
| Title of host publication | Advanced Intelligent Computing Theories and Applications - 6th International Conference on Intelligent Computing, ICIC 2010, Proceedings |
| Pages | 664-671 |
| Number of pages | 8 |
| DOIs | |
| Publication status | Published - 2010 Oct 29 |
| Event | 6th International Conference on Intelligent Computing, ICIC 2010 - Changsha, China Duration: 2010 Aug 18 → 2010 Aug 21 |
Publication series
| Name | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) |
|---|---|
| Volume | 6215 LNCS |
| ISSN (Print) | 0302-9743 |
| ISSN (Electronic) | 1611-3349 |
Other
| Other | 6th International Conference on Intelligent Computing, ICIC 2010 |
|---|---|
| Country | China |
| City | Changsha |
| Period | 10-08-18 → 10-08-21 |
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All Science Journal Classification (ASJC) codes
- Theoretical Computer Science
- Computer Science(all)
Cite this
}
Predicting protein stability change upon double mutation from partial sequence information using data mining approach. / Lai, Lien Fu; Wu, Chao Chin; Huang, Liang Tsung.
Advanced Intelligent Computing Theories and Applications - 6th International Conference on Intelligent Computing, ICIC 2010, Proceedings. 2010. p. 664-671 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 6215 LNCS).Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
TY - GEN
T1 - Predicting protein stability change upon double mutation from partial sequence information using data mining approach
AU - Lai, Lien Fu
AU - Wu, Chao Chin
AU - Huang, Liang Tsung
PY - 2010/10/29
Y1 - 2010/10/29
N2 - The prediction of stability change for protein mutants is one of the important issues in protein design. Recently, the prediction upon double mutation has attracted more and more attention. In this work, we have employed a data mining approach to discriminating stability change for protein double mutants. We incorporated a reliable rule induction algorithm along with accuracy of 82.2% to construct rule-based knowledge patterns. Further, a fuzzy query method was utilized to value important and similar rule patterns for an input with partial sequence information. The results showed that the approach has two major advantages: (i) A rule-based knowledge representation offers intuitive interpretation on raw data, which is helpful to understand the content; and (ii) A fuzzy query method incorporates the concept of uncertainty, which can make predictions from partial information. Based on the proposed approach, we have also developed a web service for predicting protein stability change upon double mutation from partial sequence information and it is available at http://bioinformatics.myweb.hinet.net/tandem.htm.
AB - The prediction of stability change for protein mutants is one of the important issues in protein design. Recently, the prediction upon double mutation has attracted more and more attention. In this work, we have employed a data mining approach to discriminating stability change for protein double mutants. We incorporated a reliable rule induction algorithm along with accuracy of 82.2% to construct rule-based knowledge patterns. Further, a fuzzy query method was utilized to value important and similar rule patterns for an input with partial sequence information. The results showed that the approach has two major advantages: (i) A rule-based knowledge representation offers intuitive interpretation on raw data, which is helpful to understand the content; and (ii) A fuzzy query method incorporates the concept of uncertainty, which can make predictions from partial information. Based on the proposed approach, we have also developed a web service for predicting protein stability change upon double mutation from partial sequence information and it is available at http://bioinformatics.myweb.hinet.net/tandem.htm.
UR - http://www.scopus.com/inward/record.url?scp=77958501180&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=77958501180&partnerID=8YFLogxK
U2 - 10.1007/978-3-642-14922-1_83
DO - 10.1007/978-3-642-14922-1_83
M3 - Conference contribution
AN - SCOPUS:77958501180
SN - 3642149219
SN - 9783642149214
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 664
EP - 671
BT - Advanced Intelligent Computing Theories and Applications - 6th International Conference on Intelligent Computing, ICIC 2010, Proceedings
ER -