Abstract
With the rapid development of bionanotechnology, there has been a growing interest recently in identifying the affinity classes of the inorganic materials binding peptide sequences. However, there are some distinct characteristics of inorganic materials binding sequence data that limit the performance of many widely-used classification methods. In this paper, we propose a novel framework to predict the affinity classes of peptide sequences with respect to an associated inorganic material. We first generate a large set of simulated peptide sequences based on our new amino acid transition matrix, and then the probability of test sequences belonging to a specific affinity class is calculated through solving an objective function. In addition, the objective function is solved through iterative propagation of probability estimates among sequences and sequence clusters. Experimental results on a real inorganic material binding sequence dataset show that the proposed framework is highly effective on identifying the affinity classes of inorganic material binding sequences.
| Original language | English |
|---|---|
| Title of host publication | 2013 ACM Conference on Bioinformatics, Computational Biology and Biomedical Informatics, ACM-BCB 2013 |
| Pages | 545-551 |
| Number of pages | 7 |
| DOIs | |
| State | Published - Nov 28 2013 |
| Event | 2013 4th ACM Conference on Bioinformatics, Computational Biology and Biomedical Informatics, ACM-BCB 2013 - Wshington, DC, United States Duration: Sep 22 2013 → Sep 25 2013 |
Other
| Other | 2013 4th ACM Conference on Bioinformatics, Computational Biology and Biomedical Informatics, ACM-BCB 2013 |
|---|---|
| Country | United States |
| City | Wshington, DC |
| Period | 9/22/13 → 9/25/13 |
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ASJC Scopus subject areas
- Bioengineering
- Biomedical Engineering
- Health Informatics
Cite this
A framework for identifying affinity classes of inorganic materials binding peptide sequences. / Du, Nan; Knecht, Marc; Prasad, Paras N.; Swihart, Mark T.; Walsh, Tiffany; Zhang, Aidong.
2013 ACM Conference on Bioinformatics, Computational Biology and Biomedical Informatics, ACM-BCB 2013. 2013. p. 545-551.Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
}
TY - GEN
T1 - A framework for identifying affinity classes of inorganic materials binding peptide sequences
AU - Du, Nan
AU - Knecht, Marc
AU - Prasad, Paras N.
AU - Swihart, Mark T.
AU - Walsh, Tiffany
AU - Zhang, Aidong
PY - 2013/11/28
Y1 - 2013/11/28
N2 - With the rapid development of bionanotechnology, there has been a growing interest recently in identifying the affinity classes of the inorganic materials binding peptide sequences. However, there are some distinct characteristics of inorganic materials binding sequence data that limit the performance of many widely-used classification methods. In this paper, we propose a novel framework to predict the affinity classes of peptide sequences with respect to an associated inorganic material. We first generate a large set of simulated peptide sequences based on our new amino acid transition matrix, and then the probability of test sequences belonging to a specific affinity class is calculated through solving an objective function. In addition, the objective function is solved through iterative propagation of probability estimates among sequences and sequence clusters. Experimental results on a real inorganic material binding sequence dataset show that the proposed framework is highly effective on identifying the affinity classes of inorganic material binding sequences.
AB - With the rapid development of bionanotechnology, there has been a growing interest recently in identifying the affinity classes of the inorganic materials binding peptide sequences. However, there are some distinct characteristics of inorganic materials binding sequence data that limit the performance of many widely-used classification methods. In this paper, we propose a novel framework to predict the affinity classes of peptide sequences with respect to an associated inorganic material. We first generate a large set of simulated peptide sequences based on our new amino acid transition matrix, and then the probability of test sequences belonging to a specific affinity class is calculated through solving an objective function. In addition, the objective function is solved through iterative propagation of probability estimates among sequences and sequence clusters. Experimental results on a real inorganic material binding sequence dataset show that the proposed framework is highly effective on identifying the affinity classes of inorganic material binding sequences.
UR - http://www.scopus.com/inward/record.url?scp=84888160920&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84888160920&partnerID=8YFLogxK
U2 - 10.1145/2506583.2506628
DO - 10.1145/2506583.2506628
M3 - Conference contribution
SN - 9781450324342
SP - 545
EP - 551
BT - 2013 ACM Conference on Bioinformatics, Computational Biology and Biomedical Informatics, ACM-BCB 2013
ER -