ArticleBMC genomics2018
InfAcrOnt: calculating cross-ontology term similarities using information flow by a random walk.
Article in BMC genomics, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 43 papers.
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43 citing papers in PubMed.
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- Its2vec: Fungal Species Identification Using Sequence Embedding and Random Forest Classification.BioMed research international · 2020Article
- Identifying Protein Biomarkers in Blood for Alzheimer's Disease.Frontiers in cell and developmental biology · 2020Article
- A Brief Survey for MicroRNA Precursor Identification Using Machine Learning Methods.Current genomics · 2020Review
- Prediction of G Protein-Coupled Receptors With CTDC Extraction and MRMD2.0 Dimension-Reduction Methods.Frontiers in bioengineering and biotechnology · 2020Article
- Identifying Cancer-Related lncRNAs Based on a Convolutional Neural Network.Frontiers in cell and developmental biology · 2020Article
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7 authors.
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Abstract
backgroundSince the establishment of the first biomedical ontology Gene Ontology (GO), the number of biomedical ontology has increased dramatically. Nowadays over 300 ontologies have been built including extensively used Disease Ontology (DO) and Human Phenotype Ontology (HPO). Because of the advantage of identifying novel relationships between terms, calculating similarity between ontology terms is one of the major tasks in this research area. Though similarities between terms within each ontology have been studied with in silico methods, term similarities across different ontologies were not investigated as deeply. The latest method took advantage of gene functional interaction network (GFIN) to explore such inter-ontology similarities of terms. However, it only used gene interactions and failed to make full use of the connectivity among gene nodes of the network. In addition, all existent methods are particularly designed for GO and their performances on the extended ontology community remain unknown.
resultsWe proposed a method InfAcrOnt to infer similarities between terms across ontologies utilizing the entire GFIN. InfAcrOnt builds a term-gene-gene network which comprised ontology annotations and GFIN, and acquires similarities between terms across ontologies through modeling the information flow within the network by random walk. In our benchmark experiments on sub-ontologies of GO, InfAcrOnt achieves a high average area under the receiver operating characteristic curve (AUC) (0.9322 and 0.9309) and low standard deviations (1.8746e-6 and 3.0977e-6) in both human and yeast benchmark datasets exhibiting superior performance. Meanwhile, comparisons of InfAcrOnt results and prior knowledge on pair-wise DO-HPO terms and pair-wise DO-GO terms show high correlations.
conclusionsThe experiment results show that InfAcrOnt significantly improves the performance of inferring similarities between terms across ontologies in benchmark set.
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