Name
Abstract | ||
|---|---|---|
Protein thermostability information is closely linked to commercial production of many biomaterials. Recent developments have shown that amino acid composition, special sequence patterns and hydrogen bonds, disulfide bonds, salt bridges and so on are of considerable importance to thermostability. In this study, we present a system to integrate these various factors that predict protein thermostability. In this study, the features of proteins in the PGTdb are analyzed. We consider both structure and sequence features and correlation coefficients are incorporated into the feature selection algorithm. Machine learning algorithms are then used to develop identification systems and performances between the different algorithms are compared. In this research, two features, (E+F+M+R)/residue and charged/non-charged, are found to be critical to the thermostability of proteins. Although the sequence and structural models achieve a higher accuracy, sequence-only models provides sufficient accuracy for sequence-only thermostability prediction. |
Year | DOI | Venue |
|---|---|---|
2009 | 10.1016/j.eswa.2008.12.020 | Expert Syst. Appl. |
Keywords | Field | DocType |
sufficient accuracy,protein thermostability,expert system,sequence-only model,protein thermostability information,sequence-only thermostability prediction,amino acid composition,commercial production,special sequence pattern,decision tree,sequence feature,higher accuracy,machine learning,hydrogen bond,disulfide bond,bioinformatics,feature selection | Thermostability,Data mining,Decision tree,Feature selection,Biological system,Computer science,Amino acid composition,Disulfide bond,Expert system,Bioinformatics,Salt bridge | Journal |
Volume | Issue | ISSN |
36 | 5 | Expert Systems With Applications |
Citations | PageRank | References |
7 | 0.52 | 7 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Li-Cheng Wu | 1 | 56 | 4.37 |
| Jian-Xin Lee | 2 | 7 | 0.52 |
| Hsien-Da Huang | 3 | 835 | 63.83 |
| Baw-Juine Liu | 4 | 31 | 1.68 |
| Jorng-Tzong Horng | 5 | 541 | 67.78 |