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Information content-based Gene Ontology functional similarity measures: which one to use for a given biological data type?

Mazandu GK, Mulder NJ - PLoS ONE (2014)

Bottom Line: However, it is not clear whether a specific functional similarity measure associated with a given approach is the most appropriate, given a biological data set or an application, i.e., achieving the best performance compared to other functional similarity measures for the biological application under consideration.We have conducted a performance evaluation of a number of different functional similarity measures using different types of biological data in order to infer the best functional similarity measure for each different term IC and semantic similarity approach.The comparisons of different protein functional similarity measures should help researchers choose the most appropriate measure for the biological application under consideration.

View Article: PubMed Central - PubMed

Affiliation: Computational Biology Group, Department of clinical Laboratory Sciences, IDM, University of Cape Town Faculty of Health Sciences, Cape Town, South Africa; African Institute for Mathematical Sciences (AIMS), Cape Town, South Africa, and Cape Coast, Ghana.

ABSTRACT
The current increase in Gene Ontology (GO) annotations of proteins in the existing genome databases and their use in different analyses have fostered the improvement of several biomedical and biological applications. To integrate this functional data into different analyses, several protein functional similarity measures based on GO term information content (IC) have been proposed and evaluated, especially in the context of annotation-based measures. In the case of topology-based measures, each approach was set with a specific functional similarity measure depending on its conception and applications for which it was designed. However, it is not clear whether a specific functional similarity measure associated with a given approach is the most appropriate, given a biological data set or an application, i.e., achieving the best performance compared to other functional similarity measures for the biological application under consideration. We show that, in general, a specific functional similarity measure often used with a given term IC or term semantic similarity approach is not always the best for different biological data and applications. We have conducted a performance evaluation of a number of different functional similarity measures using different types of biological data in order to infer the best functional similarity measure for each different term IC and semantic similarity approach. The comparisons of different protein functional similarity measures should help researchers choose the most appropriate measure for the biological application under consideration.

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Performance evaluation in terms of clustering power (RI and NI) and Area Under the Curve (AUC) values.Different x-axis labels are the same as in Fig. 1, where different prefixes and suffixes stand for different term semantic similarity approaches and functional similarity measures.
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pone-0113859-g002: Performance evaluation in terms of clustering power (RI and NI) and Area Under the Curve (AUC) values.Different x-axis labels are the same as in Fig. 1, where different prefixes and suffixes stand for different term semantic similarity approaches and functional similarity measures.

Mentions: We used human PPI and co-expressed networks to assess the performance of different functional similarity measures. In the case of the PPI network, we are using the AUC values computed using the ROCR package under the R programming language as a measure of classification power. The larger the upper AUC value, the more efficient the functional similarity measure is. For the co-expression network, we computed the NI and RI values as measures of clustering power, the higher these values, the more powerful the functional similarity measure is. Different values found for different measures are shown in Figure 2 and Table 3. These results indicate that independently of the approaches, the Avg measure, which is the earliest proposal suggested by Lord et al. [16] in the context of the IC-based functional similarity, performs better than any other functional similarity measure.


Information content-based Gene Ontology functional similarity measures: which one to use for a given biological data type?

Mazandu GK, Mulder NJ - PLoS ONE (2014)

Performance evaluation in terms of clustering power (RI and NI) and Area Under the Curve (AUC) values.Different x-axis labels are the same as in Fig. 1, where different prefixes and suffixes stand for different term semantic similarity approaches and functional similarity measures.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4256219&req=5

pone-0113859-g002: Performance evaluation in terms of clustering power (RI and NI) and Area Under the Curve (AUC) values.Different x-axis labels are the same as in Fig. 1, where different prefixes and suffixes stand for different term semantic similarity approaches and functional similarity measures.
Mentions: We used human PPI and co-expressed networks to assess the performance of different functional similarity measures. In the case of the PPI network, we are using the AUC values computed using the ROCR package under the R programming language as a measure of classification power. The larger the upper AUC value, the more efficient the functional similarity measure is. For the co-expression network, we computed the NI and RI values as measures of clustering power, the higher these values, the more powerful the functional similarity measure is. Different values found for different measures are shown in Figure 2 and Table 3. These results indicate that independently of the approaches, the Avg measure, which is the earliest proposal suggested by Lord et al. [16] in the context of the IC-based functional similarity, performs better than any other functional similarity measure.

Bottom Line: However, it is not clear whether a specific functional similarity measure associated with a given approach is the most appropriate, given a biological data set or an application, i.e., achieving the best performance compared to other functional similarity measures for the biological application under consideration.We have conducted a performance evaluation of a number of different functional similarity measures using different types of biological data in order to infer the best functional similarity measure for each different term IC and semantic similarity approach.The comparisons of different protein functional similarity measures should help researchers choose the most appropriate measure for the biological application under consideration.

View Article: PubMed Central - PubMed

Affiliation: Computational Biology Group, Department of clinical Laboratory Sciences, IDM, University of Cape Town Faculty of Health Sciences, Cape Town, South Africa; African Institute for Mathematical Sciences (AIMS), Cape Town, South Africa, and Cape Coast, Ghana.

ABSTRACT
The current increase in Gene Ontology (GO) annotations of proteins in the existing genome databases and their use in different analyses have fostered the improvement of several biomedical and biological applications. To integrate this functional data into different analyses, several protein functional similarity measures based on GO term information content (IC) have been proposed and evaluated, especially in the context of annotation-based measures. In the case of topology-based measures, each approach was set with a specific functional similarity measure depending on its conception and applications for which it was designed. However, it is not clear whether a specific functional similarity measure associated with a given approach is the most appropriate, given a biological data set or an application, i.e., achieving the best performance compared to other functional similarity measures for the biological application under consideration. We show that, in general, a specific functional similarity measure often used with a given term IC or term semantic similarity approach is not always the best for different biological data and applications. We have conducted a performance evaluation of a number of different functional similarity measures using different types of biological data in order to infer the best functional similarity measure for each different term IC and semantic similarity approach. The comparisons of different protein functional similarity measures should help researchers choose the most appropriate measure for the biological application under consideration.

Show MeSH