Name
Abstract | ||
|---|---|---|
Snake neurotoxins are important experimental tool in pharmacological research. Over the years, the number of snake neurotoxin sequences identified is increasing at a very fast pace. However, only a small portion of them are experimentally characterized from more than 200,000 variants estimated to exist in nature. In this paper, we report a systematic functional analysis on snake neurotoxins using a statistical machine learning method - nearest neighbour approach for functional prediction together with a set of rules. Based on this method we built a highly accurate functional prediction tool for putative annotation for snake neurotoxins |
Year | DOI | Venue |
|---|---|---|
2006 | 10.1109/ICARCV.2006.345470 | ICARCV |
Keywords | Field | DocType |
nearest neighbour,neurophysiology,nearest neighbour approach,statistical analysis,learning (artificial intelligence),pharmacological research,systematic functional analysis,snake neurotoxins,biology computing,molecular biophysics,prediction,functional prediction,toxicology,statistical machine learning method,learning artificial intelligence,functional analysis,rule based | Nearest neighbour,Computer science,Control engineering,Artificial intelligence,Computational biology,Machine learning,Statistical analysis | Conference |
ISSN | ISBN | Citations |
2474-2953 | 1-4214-042-1 | 0 |
PageRank | References | Authors |
0.34 | 6 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| S H Seah | 1 | 79 | 10.51 |
| C K Kwoh | 2 | 559 | 46.55 |
| Vladimir Brusic | 3 | 551 | 63.37 |
| Meena K Sakharkar | 4 | 73 | 13.63 |
| Geok See Ng | 5 | 215 | 21.04 |