Name
Title | ||
|---|---|---|
Comparing protein contact maps via Universal Similarity Metric: an improvement in the noise-tolerance. |
Abstract | ||
|---|---|---|
Comparing protein structures based on their contact maps similarity is an important problem in molecular biology. One motivation to seek fast algorithms for comparing contact maps is devising systems for reconstructing three-dimensional structure of proteins from their predicted contact maps. In this paper, we propose an algorithm to apply the Universal Similarity Metric (USM) to contact map comparison problem in a two-dimensional space. The major advantage of this algorithm is the highly improved noise-tolerance of the metric in comparison with its previous one-dimensional implementations. This is the first successful attempt to apply the USM to two-dimensional objects, without reducing their dimensionality. |
Year | DOI | Venue |
|---|---|---|
2009 | 10.1504/IJCBDD.2009.028821 | I. J. Computational Biology and Drug Design |
Keywords | Field | DocType |
mutual information,molecular biology,bioinformatics | Protein structure comparison,Curse of dimensionality,Artificial intelligence,Mutual information,Bioinformatics,Noise tolerance,Machine learning,Mathematics | Journal |
Volume | Issue | ISSN |
2 | 2 | 1756-0756 |
Citations | PageRank | References |
2 | 0.38 | 10 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Sara Rahmati | 1 | 2 | 0.72 |
| Janice I. Glasgow | 2 | 392 | 127.97 |