Transition probability matrix of the ontology augmented network example.

<p>Transition probability matrix of the ontology augmented network example.</p

Construction of Ontology Augmented Networks for Protein Complex Prediction

<div><p>Protein complexes are of great importance in understanding the principles of cellular organization and function. The increase in available protein-protein interaction data, gene ontology and other resources make it possible to develop computational methods for protein complex prediction. Most existing methods focus mainly on the topological structure of protein-protein interaction networks, and largely ignore the gene ontology annotation information. In this article, we constructed ontology augmented networks with protein-protein interaction data and gene ontology, which effectively unified the topological structure of protein-protein interaction networks and the similarity of gene ontology annotations into unified distance measures. After constructing ontology augmented networks, a novel method (clustering based on ontology augmented networks) was proposed to predict protein complexes, which was capable of taking into account the topological structure of the protein-protein interaction network, as well as the similarity of gene ontology annotations. Our method was applied to two different yeast protein-protein interaction datasets and predicted many well-known complexes. The experimental results showed that (i) ontology augmented networks and the unified distance measure can effectively combine the structure closeness and gene ontology annotation similarity; (ii) our method is valuable in predicting protein complexes and has higher F1 and accuracy compared to other competing methods.</p></div

Ontology augmented graph with GO slims.

<p>Ontology augmented graph with GO slims.</p

The effect of <i>extend_thres</i> on the performance of COAN on the DIP database.

<p>The word ‘size’ refers to the size of the largest predicted complex with different <i>extend_thres</i>. The highest value in each row is in bold.</p

The impact of relation expansion, noise filtering and redundant removal.

<p>The impact of relation expansion, noise filtering and redundant removal.</p

Semantic relation network for “Angina Pectoris” after relation retrieval.

<p>Semantic relation network for “Angina Pectoris” after relation retrieval.</p

Performance comparison of protein complex prediction methods using the DIP dataset.

<p>The ‘#Complexes’ refers to the number of predicted complexes, and ‘Size’ refers to the size of the largest predicted complex. <i>extend_thres</i> was set at 0.6 for COAN. The highest score is in bold.</p

An example of relation and sentence retrieval.

<p>An example of relation and sentence retrieval.</p

Comparison of summarization performance on ROUGE-1.

<p>Comparison of summarization performance on ROUGE-1.</p

The pseudo-codes of the COAN algorithm.

<p>The pseudo-codes of the COAN algorithm.</p