Support Vector Machine GPCR Subfamily Classification Results for gi|14755123|ref|XP_033838.1|

>gi|14755123|ref|XP_033838.1| chemokine (C-C motif) receptor 6 [Homo sapiens] MSGESMNFSDVFDSSEDYFVSVNTSYYSVDSEMLLCSLQEVRQFSRLFVPIAYSLICVFGLLGNILVVIT FAFYKKARSMTDVYLLNMAIADILFVLTLPFWAVSHATGAWVFSNATCKLLKGIYAINFNCGMLLLTCIS MDRYIAIVQATKSFRLRSRTLPRSKIICLVVWGLSVIISSSTFVFNQKYNTQGSDVCEPKYQTVSEPIRW KLLMLGLELLFGFFIPLMFMIFCYTFIVKTLVQAQNSKRHKAIRVIIAVVLVFLACQIPHNMVLLVTAAN LGKMNRSCQSEKLIGYTKTVTEVLAFLHCCLNPVLYAFIGQKFRNYFLKILKDLWCVRRKYKSSGFSCAG RYSENISRQTSETADNDNASSFTM


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

1.0832689 Peptide GPCRDB Peptide
  1.379724 Chemokine GPCRDB Chemokine
   0.84371984 C-C Chemokine GPCRDB C-C Chemokine
    1.0 C-C Chemokine type 6 GPCRDB C-C Chemokine type 6
   -0.9446006 C-C Chemokine type 10
   -0.9716001 C-C Chemokine type 11
   -0.97635365 C-C Chemokine type 5
   -0.9858163 C-C Chemokine type 3
   -0.9926607 C-C Chemokine type 7
   -0.99569666 C-C Chemokine type 2
   -1.0514424 C-C Chemokine type 9
   -1.0805613 C-C Chemokine type X
   -1.0961349 C-C Chemokine type 1
   -1.1595582 C-C Chemokine type 8
   -1.1605346 C-C Chemokine type 4
   -1.3074707 C-C Chemokine other
  -0.755903 C-X-C Chemokine
  -1.1025046 C-X3-C Chemokine
  -1.122082 XC Chemokine
 -0.6457367Interleukin-8
 -0.95851123Angiotensin
 -1.0137523Galanin
 -1.0622956Thrombin
 -1.0797014Fmet-leu-phe
 -1.0922825Tachykinin
 -1.1063836C5a anaphylatoxin
 -1.1277552Endothelin
 -1.1284301Urotensin II
 -1.15518Neuromedin U
 -1.162282Adrenomedullin (G10D)
 -1.1705589CCK
 -1.176657APJ like
 -1.1805305GPR37 / endothelin B-like
 -1.2042328Bombesin
 -1.217999Melanocortin
 -1.2381049Bradykinin
 -1.2481141Chemokine receptor-like
 -1.2680204Proteinase activated
 -1.3075802Neurotensin
 -1.3331708Somatostatin
 -1.3404582Opioid
 -1.3435842Orexin & neuropeptide FF
 -1.3456244Vasopressin-like
 -1.6424098Neuropeptide Y
-0.9035147Viral
-0.9488338Class A Orphan/other
-0.95073414Nucleotide-like
-1.0202171Gonadotropin-releasing hormone
-1.0208769Platelet activating factor
-1.0554707Thyrotropin-releasing hormone & Secretagogue
-1.0746496Hormone protein
-1.0869067(Rhod)opsin
-1.1531184Prostanoid
-1.1542838Cannabis
-1.1622177Melatonin
-1.2653227Olfactory
-1.3352985Amine
-1.3584137Lysosphingolipid & LPA (EDG)




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 19 Class B Secretin 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 88 Class A Rhodopsin 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]