Support Vector Machine GPCR Subfamily Classification Results for NP_004237

>NP_004237 MKLGSSRAGPGRGSAGLLPGVHELPMGIPAPWGTSPLSFHRKCSLWAPGRPFLTLVLLVSIKQVTGSLLE ETTRKWAQYKQACLRDLLKEPSGIFCNGTFDQYVCWPHSSPGNVSVPCPSYLPWWSEESSGRAYRHCLAQ GTWQTIENATDIWQDDSECSENHSFKQNVDRYALLSTLQLMYTVGYSFSLISLFLALTLLLFLRKLHCTR NYIHMNLFASFILRTLAVLVKDVVFYNSYSKRPDNENGWMSYLSEMSTSCRSVQVLLHYFVGANYLWLLV EGLYLHTLLEPTVLPERRLWPRYLLLGWAFPVLFVVPWGFARAHLENTGCWTTNGNKKIWWIIRGPMMLC VTVNFFIFLKILKLLISKLKAHQMCFRDYKYRLAKSTLVLIPLLGVHEILFSFITDDQVEGFAKLIRLFI QLTLSSFHGFLVALQYGFANGEVKAELRKYWVRFLLARHSGCRACVLGKDFRFLGKCPKKLSEGDGAEKL RKLQPSLNSGRLLHLAMRGLGELGAQPQQDHARWPRGSSLSECSEGDVTMANTMEEILEESEI



Here is a list of Class B Secretin like subfamilies with support vector machine models and the score this sequence received with respect to each model.

0.92663014 Glucagon GPCRDB Glucagon
-0.760934Gastric inhibitory peptide
-0.7975165Parathyroid hormone
-1.0046625Corticotropin releasing factor
-1.0124733Growth hormone-releasing hormone
-1.0455905Calcitonin
-1.0789497Methuselah-like proteins (MTH)
-1.0966885Brain-specific angiogenesis inhibitor (BAI)
-1.175392PACAP
-1.179467Latrophilin
-1.2614392Diuretic hormone
-1.2925339Secretin
-1.2930304Vasoactive intestinal polypeptide
-1.3247337Class B orphan/other
-1.3566308EMR1




HOW TO INTERPRET THE SCORES:

Subfamily Scoring:

The support vector machine for each subfamily is trained to score positive examples (members of the subfamily) as 1.0 and negative examples (non-members) as -1.0

If this sequence receives a negative score with respect to a subfamily, it is probably not a member of the subfamily.

If this sequence receives a positive score with respect to a subfamily, it is probably in the subfamily.

In general, the score's distance from zero gives you the confidence level of the prediction. Positive scores greater than +1.0 mean a classifier has strongly accepted your sequence. Negative scores less than -1.0 mean a classifier has strongly rejected your sequence. For a more detailed evaluation of scores and confidence levels, take a look at Classification Statistics.
SOME ADDITIONAL INFORMATION:

There were 199 Class A Rhodopsin like and 11 Class C Metabotropic glutamate / pheromone and 2 Class D Fungal pheromone and 5 Frizzled/Smoothened family and 4 Nematode chemoreceptors and 4 Vomeronasal receptors (V1R & V3R) and 4 Class B Secretin like subfamilies whose classifiers were not run, because the families or subfamilies that contain them were rejected by classifiers higher in the hierarchy. For example, if the Amine receptor classifier scores this sequence negatively, it will not be checked with respect to the subfamilies of Amine receptors (Histamine, Serotonin, Dopamine, Octopamine and Acetylcholine (muscarinic) receptors).

Not all subfamilies have models yet. This sequence may be in a subfamily that has not yet been modeled.

Please cite: R. Karchin, K. Karplus and D. Haussler "Classifying G-Protein Coupled Receptors with Support Vector Machines" Bioinformatics 2002 in press [postscript, pdf]