Comparing two K-category assignments by a K-category correlation coefficient

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Standard

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

Harvard

Gorodkin, J 2004, 'Comparing two K-category assignments by a K-category correlation coefficient', Computational Biology and Chemistry, vol. 28, no. 5-6, pp. 367-374. https://doi.org/10.1016/j.compbiolchem.2004.09.006

APA

Gorodkin, J. (2004). Comparing two K-category assignments by a K-category correlation coefficient. Computational Biology and Chemistry, 28(5-6), 367-374. https://doi.org/10.1016/j.compbiolchem.2004.09.006

Vancouver

Gorodkin J. Comparing two K-category assignments by a K-category correlation coefficient. Computational Biology and Chemistry. 2004;28(5-6):367-374. https://doi.org/10.1016/j.compbiolchem.2004.09.006

Author

Gorodkin, Jan. / Comparing two K-category assignments by a K-category correlation coefficient. In: Computational Biology and Chemistry. 2004 ; Vol. 28, No. 5-6. pp. 367-374.

Bibtex

@article{36f997f0a1c111ddb6ae000ea68e967b,
title = "Comparing two K-category assignments by a K-category correlation coefficient",
abstract = "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/.",
author = "Jan Gorodkin",
year = "2004",
doi = "10.1016/j.compbiolchem.2004.09.006",
language = "English",
volume = "28",
pages = "367--374",
journal = "Computational Biology and Chemistry",
issn = "1476-9271",
publisher = "Elsevier",
number = "5-6",

}

RIS

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