<p>Physical interactions (dashed blue line), metabolic flux correlations (dashed green line), functional interactions (red continuous line) and an integrated interactome generated by the sum of all other interactomes (black continuous line). ROC curves were computed to assess the signal of pathophenotypic similarities for biological interactions. True positives (TP) were those interactions that where found in the intersection between PSGN and each biomolecular interactome (PIN, MGN and FSGN). The dataset of false positives (FP) was calculated from intersected gene pairs between PSGN and randomizations of each biomolecular interactome. We obtained severals different FP datasets to calculate the average area under the curve (AUC), it was 0.77...
<p>ROC curve analysis for evaluating the effectiveness of the selected experiments at improving the ...
MOTIVATION: Interactions between proteins help us understand how genes are functionally related and ...
Proteins with strong functional annotations were divided into functional groups (Table 3) and on thi...
<p>We compare the performance of predicting protein-protein interactions using all available annotat...
<p>Each solid colored line represents Receiver Operator Characteristics (ROC) curve of prediction me...
<p>We compare the performance of predicting BioGRID’s interactions using all available annotations f...
<p>(A) Boxplots of the correlation estimates between the Ribosome gene sets and random gene sets, an...
<p>We compare the performance of predicting genetic interactions using all available annotations fro...
<p>Each solid colored line represents Receiver Operator Characteristics (ROC) curve of methods. Gray...
<p>We compare the performance of predicting STRING’s protein-protein interactions using all availabl...
<p>The plots illustrate the performance in predicting protein interaction partners. The left panels ...
<p>(A) The ROC curve for FLN. (B) The ROC curve for FLNhm. (C) The ROC curve for PPI network. (D) Th...
<p>Subfigure A: The ROC curves for three data sources (“Chem”: chemical structure, “Inter”: target p...
<p><b>Copyright information:</b></p><p>Taken from "High-throughput identification of interacting pro...
The currently known network of human protein-protein interactions (PPIs) is providing new insights i...
<p>ROC curve analysis for evaluating the effectiveness of the selected experiments at improving the ...
MOTIVATION: Interactions between proteins help us understand how genes are functionally related and ...
Proteins with strong functional annotations were divided into functional groups (Table 3) and on thi...
<p>We compare the performance of predicting protein-protein interactions using all available annotat...
<p>Each solid colored line represents Receiver Operator Characteristics (ROC) curve of prediction me...
<p>We compare the performance of predicting BioGRID’s interactions using all available annotations f...
<p>(A) Boxplots of the correlation estimates between the Ribosome gene sets and random gene sets, an...
<p>We compare the performance of predicting genetic interactions using all available annotations fro...
<p>Each solid colored line represents Receiver Operator Characteristics (ROC) curve of methods. Gray...
<p>We compare the performance of predicting STRING’s protein-protein interactions using all availabl...
<p>The plots illustrate the performance in predicting protein interaction partners. The left panels ...
<p>(A) The ROC curve for FLN. (B) The ROC curve for FLNhm. (C) The ROC curve for PPI network. (D) Th...
<p>Subfigure A: The ROC curves for three data sources (“Chem”: chemical structure, “Inter”: target p...
<p><b>Copyright information:</b></p><p>Taken from "High-throughput identification of interacting pro...
The currently known network of human protein-protein interactions (PPIs) is providing new insights i...
<p>ROC curve analysis for evaluating the effectiveness of the selected experiments at improving the ...
MOTIVATION: Interactions between proteins help us understand how genes are functionally related and ...
Proteins with strong functional annotations were divided into functional groups (Table 3) and on thi...