Name
Abstract | ||
|---|---|---|
Because of the relatively large gap of knowledge between number of protein sequences and protein structures, the ability to construct a computational model predicting structure from sequence information has become an important area of research. The knowledge of a protein's structure is crucial in understanding its biological role. In this work, we present a support vector machine based method for recognising a protein's fold from sequence information alone, where this sequence has less similarity with sequences of known structures. We have focused on improving multi-class classification, parameter tuning, descriptor design, and feature selection. The current implementation demonstrates better prediction accuracy than previous similar approaches, and has similar performance when compared with straightforward threading. |
Year | DOI | Venue |
|---|---|---|
2005 | 10.1504/IJBRA.2005.007909 | IJBRA |
Field | DocType | Volume |
Pattern recognition,Proteomics,Feature selection,Computer science,Threading (manufacturing),Threading (protein sequence),Support vector machine,Artificial intelligence,Bioinformatics,Machine learning,Protein structure | Journal | 1 |
Issue | Citations | PageRank |
3 | 4 | 0.49 |
References | Authors | |
16 | 5 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Robert E. Langlois | 1 | 24 | 1.62 |
| Alice Diec | 2 | 4 | 0.49 |
| Ognjen Perisic | 3 | 4 | 0.49 |
| Yang Dai | 4 | 7 | 3.40 |
| Hui Lu | 5 | 11 | 1.00 |