Name
Abstract | ||
|---|---|---|
Prediction of trans-membrane helices continues to be a difficult task with a few prediction methods clearly taking the lead; none of these is clearly best on all accounts. Recently, we have carefully set up protocols for benchmarking the most relevant aspects of prediction accuracy and have applied it to >30 prediction methods. Here, we present the extension of that analysis to the level of an automatic web server evaluating new methods (http://cubic.bioc.columbia.edu/services/tmh_benchmark/). The most important achievements of the tool are: (i) any new method is compared to the battery of well-established tools; (ii) the battery of measures explored allows spotting strengths in methods that may not be 'best' overall. In particular, we report per-residue and per-segment scores for accuracy and the error-rates for confusing membrane helices with globular proteins or signal peptides. An additional feature is that developers can directly investigate any hydrophobicity scale for its potential in predicting membrane helices. |
Year | DOI | Venue |
|---|---|---|
2003 | 10.1093/nar/gkg532 | NUCLEIC ACIDS RESEARCH |
Keywords | Field | DocType |
signal peptide,error rate | Data mining,Helix (Snails),Biology,Software,Helix,Bioinformatics,Battery (electricity),Genetics,Spotting,Benchmarking,Web server | Journal |
Volume | Issue | ISSN |
31 | 13.0 | 0305-1048 |
Citations | PageRank | References |
19 | 1.30 | 5 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Andrew Kernytsky | 1 | 19 | 1.30 |
| Burkhard Rost | 2 | 795 | 88.14 |