Values for the AUC for the functional annotation with each clustering algorithm and graph representations for the PIN and the average AUC values per algorithm and per representation.

Values for the sensitivity (sens.) and the false positive rate (fpr), for the functional annotation for each graph representation using Infomap, at different threshold values (ω).

Kire Trivodaliev (590008)
Aleksandra Bogojeska (590009)
Ljupco Kocarev (577406)

June 2014

Values for the sensitivity (sens.) and the false positive rate (fpr), for the functional annotati...

Area under the curve (AUC), Rand Index (RI) and Normalized Mutual Information (NI) values of different measures.

Gaston K. Mazandu (224132)
Nicola J. Mulder (57602)

December 2014

Comparing performance of 57 different functional similarity measures in terms of AUC values for C...

AUC values of the six subgraphs shown in figure 3.

Qian-Ming Zhang (284215)
Linyuan Lü (215946)
Wen-Qiang Wang (284216)
Tao Zhou (117050)

February 2013

The best performance for each network is emphasized in bold. Each number is obtained by averaging...

Values for the sensitivity (sens.) and the false positive rate (fpr), for the functional annotation for each graph representation using each of the clustering algorithms, at a fixed threshold value (ω = 0).

Kire Trivodaliev (590008)
Aleksandra Bogojeska (590009)
Ljupco Kocarev (577406)

June 2014

Values for the sensitivity (sens.) and the false positive rate (fpr), for the functional annotati...

Results for the functional annotation for each graph representation using edgeCluster, showing the ROC curve (A) with H indicating the corresponding entropy value and the corresponding AUC values (B).

Kire Trivodaliev (590008)
Aleksandra Bogojeska (590009)
Ljupco Kocarev (577406)

June 2014

Results for the functional annotation for each graph representation using edgeCluster, showing th...

Results for the functional annotation for each graph representation using FC, showing the ROC curve (A) with H indicating the corresponding entropy value and the corresponding AUC values (B).

Kire Trivodaliev (590008)
Aleksandra Bogojeska (590009)
Ljupco Kocarev (577406)

June 2014

Results for the functional annotation for each graph representation using FC, showing the ROC cur...

Values for the sensitivity (sens.) and the false positive rate (fpr), for the functional annotation for each graph representation using FC, at different threshold values (ω).

Kire Trivodaliev (590008)
Aleksandra Bogojeska (590009)
Ljupco Kocarev (577406)

June 2014

Values for the sensitivity (sens.) and the false positive rate (fpr), for the functional annotati...

List of enriched functional annotation groups in 8 clusters by functional annotation clustering analysis.

Mingoo Kim (179186)
Heewon Seo (828266)
Yohan Choi (391111)
Inkyu Yoo (828267)
Minseok Seo (316795)
Chang-Kyu Lee (48245)
Heebal Kim (9991)
Hakhyun Ka (348057)

November 2015

List of enriched functional annotation groups in 8 clusters by functional annotation clustering a...

Average AUC for each data set and algorithm over all p-value thresholds.

Florian Mittag (324327)
Michael Römer (564122)
Andreas Zell (81517)

August 2015

All values are AUCs averaged over all p-value thresholds for each data set and algorithm. ...

Results for the functional annotation for each graph representation using BGLL, showing the ROC curve (A) with H indicating the corresponding entropy value and the corresponding AUC values (B).

Kire Trivodaliev (590008)
Aleksandra Bogojeska (590009)
Ljupco Kocarev (577406)

June 2014

Results for the functional annotation for each graph representation using BGLL, showing the ROC c...

Results for the functional annotation for each graph representation using timeBGLL, showing the ROC curve (A) with H indicating the corresponding entropy value and the corresponding AUC values (B).

Kire Trivodaliev (590008)
Aleksandra Bogojeska (590009)
Ljupco Kocarev (577406)

June 2014

Results for the functional annotation for each graph representation using timeBGLL, showing the R...

Results for the functional annotation for each graph representation using Infomap, showing the ROC curve (A) with H indicating the corresponding entropy value and the corresponding AUC values (B).

Kire Trivodaliev (590008)
Aleksandra Bogojeska (590009)
Ljupco Kocarev (577406)

June 2014

Results for the functional annotation for each graph representation using Infomap, showing the RO...

Clustering coefficient and degree assortativity.

Jenny Lennartsson (312965)
Nina Håkansson (312966)
Uno Wennergren (106344)
Annie Jonsson (312967)

February 2013

Clustering coefficient (A) and degree assortativity (B) values generated by the CMext and SpecNet...

AUC values of different combination of feature scores for training and test dataset in a generic predictor.

Md. Mehedi Hasan (755447)
Hiroyuki Kurata (35718)

October 2018

AUC values of different combination of feature scores for training and test dataset in a generic pre...

Average clustering coefficient for each book & clustering coefficient for a similar random network.

Michaël C. Waumans (748882)
Thibaut Nicodème (748883)
Hugues Bersini (111089)

June 2015

Average clustering coefficient for each book & clustering coefficient for a similar random networ...

Values for the sensitivity (sens.) and the false positive rate (fpr), for the functional annotation for each graph representation using Infomap, at different threshold values (ω).

Kire Trivodaliev (590008)
Aleksandra Bogojeska (590009)
Ljupco Kocarev (577406)

June 2014

Values for the sensitivity (sens.) and the false positive rate (fpr), for the functional annotati...

Area under the curve (AUC), Rand Index (RI) and Normalized Mutual Information (NI) values of different measures.

Gaston K. Mazandu (224132)
Nicola J. Mulder (57602)

December 2014

Comparing performance of 57 different functional similarity measures in terms of AUC values for C...

AUC values of the six subgraphs shown in figure 3.

Qian-Ming Zhang (284215)
Linyuan Lü (215946)
Wen-Qiang Wang (284216)
Tao Zhou (117050)

February 2013

The best performance for each network is emphasized in bold. Each number is obtained by averaging...

Values for the sensitivity (sens.) and the false positive rate (fpr), for the functional annotation for each graph representation using each of the clustering algorithms, at a fixed threshold value (ω = 0).

Kire Trivodaliev (590008)
Aleksandra Bogojeska (590009)
Ljupco Kocarev (577406)

June 2014

Values for the sensitivity (sens.) and the false positive rate (fpr), for the functional annotati...

Results for the functional annotation for each graph representation using edgeCluster, showing the ROC curve (A) with H indicating the corresponding entropy value and the corresponding AUC values (B).

Kire Trivodaliev (590008)
Aleksandra Bogojeska (590009)
Ljupco Kocarev (577406)

June 2014

Results for the functional annotation for each graph representation using edgeCluster, showing th...

Results for the functional annotation for each graph representation using FC, showing the ROC curve (A) with H indicating the corresponding entropy value and the corresponding AUC values (B).

Kire Trivodaliev (590008)
Aleksandra Bogojeska (590009)
Ljupco Kocarev (577406)

June 2014

Results for the functional annotation for each graph representation using FC, showing the ROC cur...

Values for the sensitivity (sens.) and the false positive rate (fpr), for the functional annotation for each graph representation using FC, at different threshold values (ω).

Kire Trivodaliev (590008)
Aleksandra Bogojeska (590009)
Ljupco Kocarev (577406)

June 2014

Values for the sensitivity (sens.) and the false positive rate (fpr), for the functional annotati...

List of enriched functional annotation groups in 8 clusters by functional annotation clustering analysis.

Mingoo Kim (179186)
Heewon Seo (828266)
Yohan Choi (391111)
Inkyu Yoo (828267)
Minseok Seo (316795)
Chang-Kyu Lee (48245)
Heebal Kim (9991)
Hakhyun Ka (348057)

November 2015

List of enriched functional annotation groups in 8 clusters by functional annotation clustering a...

Average AUC for each data set and algorithm over all p-value thresholds.

Florian Mittag (324327)
Michael Römer (564122)
Andreas Zell (81517)

August 2015

All values are AUCs averaged over all p-value thresholds for each data set and algorithm. ...

Results for the functional annotation for each graph representation using BGLL, showing the ROC curve (A) with H indicating the corresponding entropy value and the corresponding AUC values (B).

Kire Trivodaliev (590008)
Aleksandra Bogojeska (590009)
Ljupco Kocarev (577406)

June 2014

Results for the functional annotation for each graph representation using BGLL, showing the ROC c...

Results for the functional annotation for each graph representation using timeBGLL, showing the ROC curve (A) with H indicating the corresponding entropy value and the corresponding AUC values (B).

Kire Trivodaliev (590008)
Aleksandra Bogojeska (590009)
Ljupco Kocarev (577406)

June 2014

Results for the functional annotation for each graph representation using timeBGLL, showing the R...

Results for the functional annotation for each graph representation using Infomap, showing the ROC curve (A) with H indicating the corresponding entropy value and the corresponding AUC values (B).

Kire Trivodaliev (590008)
Aleksandra Bogojeska (590009)
Ljupco Kocarev (577406)

June 2014

Results for the functional annotation for each graph representation using Infomap, showing the RO...

Clustering coefficient and degree assortativity.

Jenny Lennartsson (312965)
Nina Håkansson (312966)
Uno Wennergren (106344)
Annie Jonsson (312967)

February 2013

Clustering coefficient (A) and degree assortativity (B) values generated by the CMext and SpecNet...

AUC values of different combination of feature scores for training and test dataset in a generic predictor.

Md. Mehedi Hasan (755447)
Hiroyuki Kurata (35718)

October 2018

AUC values of different combination of feature scores for training and test dataset in a generic pre...

Average clustering coefficient for each book & clustering coefficient for a similar random network.

Michaël C. Waumans (748882)
Thibaut Nicodème (748883)
Hugues Bersini (111089)

June 2015

Average clustering coefficient for each book & clustering coefficient for a similar random networ...

Values for the sensitivity (sens.) and the false positive rate (fpr), for the functional annotation for each graph representation using Infomap, at different threshold values (ω).

Kire Trivodaliev (590008)
Aleksandra Bogojeska (590009)
Ljupco Kocarev (577406)

June 2014

Values for the sensitivity (sens.) and the false positive rate (fpr), for the functional annotati...

Area under the curve (AUC), Rand Index (RI) and Normalized Mutual Information (NI) values of different measures.

Gaston K. Mazandu (224132)
Nicola J. Mulder (57602)

December 2014

Comparing performance of 57 different functional similarity measures in terms of AUC values for C...

AUC values of the six subgraphs shown in figure 3.

Qian-Ming Zhang (284215)
Linyuan Lü (215946)
Wen-Qiang Wang (284216)
Tao Zhou (117050)

February 2013

The best performance for each network is emphasized in bold. Each number is obtained by averaging...

Values for the AUC for the functional annotation with each clustering algorithm and graph representations for the PIN and the average AUC values per algorithm and per representation.

Abstract

Extracted data

Values for the AUC for the functional annotation with each clustering algorithm and graph representations for the PIN and the average AUC values per algorithm and per representation.

Abstract

Extracted data

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