Predicting Protein-protein interaction using co-occurring Aligned Pattern Clusters

Sze-To, Antonio; Fung, Sanderz; Lee, En-Shiun Annie; Wong, Andrew K. C.

doi:10.1109/bibm.2015.7359655

Cited by 1 publication

(1 citation statement)

References 30 publications

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“…The co-occurrence-based method is a graphical representation of relationships between terms [ 21 , 22 ]. Antono et al [ 23 ] proposed new method known as WeMine-P2P based on WeMine Aligned Pattern Clustering algorithm which discovers and identifies the localized and co-occurring conserved patterns and regions allowing variable length and pattern variations.…”

Section: Introductionmentioning

confidence: 99%

Multiple kernels learning-based biological entity relationship extraction method

Pan

Wang

2017

J Biomed Semant

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BackgroundAutomatic extracting protein entity interaction information from biomedical literature can help to build protein relation network and design new drugs. There are more than 20 million literature abstracts included in MEDLINE, which is the most authoritative textual database in the field of biomedicine, and follow an exponential growth over time. This frantic expansion of the biomedical literature can often be difficult to absorb or manually analyze. Thus efficient and automated search engines are necessary to efficiently explore the biomedical literature using text mining techniques.ResultsThe P, R, and F value of tag graph method in Aimed corpus are 50.82, 69.76, and 58.61%, respectively. The P, R, and F value of tag graph kernel method in other four evaluation corpuses are 2–5% higher than that of all-paths graph kernel. And The P, R and F value of feature kernel and tag graph kernel fuse methods is 53.43, 71.62 and 61.30%, respectively. The P, R and F value of feature kernel and tag graph kernel fuse methods is 55.47, 70.29 and 60.37%, respectively. It indicated that the performance of the two kinds of kernel fusion methods is better than that of simple kernel.ConclusionIn comparison with the all-paths graph kernel method, the tag graph kernel method is superior in terms of overall performance. Experiments show that the performance of the multi-kernels method is better than that of the three separate single-kernel method and the dual-mutually fused kernel method used hereof in five corpus sets.

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Section: Introductionmentioning

confidence: 99%