Name
Abstract | ||
|---|---|---|
This paper exploits “biological grammar” of transmembrane proteins to predict their membrane spanning regions using hidden Markov models and elaborates a set of syntactic rules to model the distinct features of transmembrane proteins. This paves the way to identify the characteristics of membrane proteins analogous to the way that identifies language contents of speech utterances by using hidden Markov models. The proposed method correctly predicts 95.24% of the membrane spanning regions of the known transmembrane proteins and correctly predicts 79.87% of the membrane spanning regions of the unknown transmembrane proteins on a benchmark dataset. |
Year | DOI | Venue |
|---|---|---|
2005 | 10.1007/978-3-540-32003-6_10 | EvoWorkshops |
Keywords | Field | DocType |
transmembrane protein,syntactic approach,biological grammar,hidden markov model,unknown transmembrane protein,paper exploit,language content,benchmark dataset,membrane protein,known transmembrane protein,distinct feature | Discrete mathematics,Membrane protein,Membrane topology,Computer science,Membrane,Transmembrane protein,Artificial intelligence,Computational biology,Hidden Markov model,Syntax,Machine learning | Conference |
Volume | ISSN | ISBN |
3449 | 0302-9743 | 3-540-25396-3 |
Citations | PageRank | References |
0 | 0.34 | 5 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Koliya Pulasinghe | 1 | 18 | 2.23 |
| Jagath C. Rajapakse | 2 | 747 | 83.00 |