Comparing two K-category assignments by a K-category correlation coefficient
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Comparing two K-category assignments by a K-category correlation coefficient. / Gorodkin, Jan.
In: Computational Biology and Chemistry, Vol. 28, No. 5-6, 2004, p. 367-374.Research output: Contribution to journal › Journal article › Research › peer-review
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TY - JOUR
T1 - Comparing two K-category assignments by a K-category correlation coefficient
AU - Gorodkin, Jan
PY - 2004
Y1 - 2004
N2 - Predicted assignments of biological sequences are often evaluated by Matthews correlation coefficient. However, Matthews correlation coefficient applies only to cases where the assignments belong to two categories, and cases with more than two categories are often artificially forced into two categories by considering what belongs and what does not belong to one of the categories, leading to the loss of information. Here, an extended correlation coefficient that applies to K-categories is proposed, and this measure is shown to be highly applicable for evaluating prediction of RNA secondary structure in cases where some predicted pairs go into the category "unknown" due to lack of reliability in predicted pairs or unpaired residues. Hence, predicting base pairs of RNA secondary structure can be a three-category problem. The measure is further shown to be well in agreement with existing performance measures used for ranking protein secondary structure predictions. Server and software is available at http://rk.kvl.dk/.
AB - Predicted assignments of biological sequences are often evaluated by Matthews correlation coefficient. However, Matthews correlation coefficient applies only to cases where the assignments belong to two categories, and cases with more than two categories are often artificially forced into two categories by considering what belongs and what does not belong to one of the categories, leading to the loss of information. Here, an extended correlation coefficient that applies to K-categories is proposed, and this measure is shown to be highly applicable for evaluating prediction of RNA secondary structure in cases where some predicted pairs go into the category "unknown" due to lack of reliability in predicted pairs or unpaired residues. Hence, predicting base pairs of RNA secondary structure can be a three-category problem. The measure is further shown to be well in agreement with existing performance measures used for ranking protein secondary structure predictions. Server and software is available at http://rk.kvl.dk/.
U2 - 10.1016/j.compbiolchem.2004.09.006
DO - 10.1016/j.compbiolchem.2004.09.006
M3 - Journal article
C2 - 15556477
VL - 28
SP - 367
EP - 374
JO - Computational Biology and Chemistry
JF - Computational Biology and Chemistry
SN - 1476-9271
IS - 5-6
ER -
ID: 8037562