ArticleSaudi journal of biological sciences2017
Margin based ontology sparse vector learning algorithm and applied in biology science.
Article in Saudi journal of biological sciences, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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12 citing papers in PubMed.
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- Asperosaponin VI inhibits LPS-induced inflammatory response by activating PPAR-γ pathway in primary microglia.Saudi journal of biological sciences · 2020Article
- Analgesic effect of the midazolam-induced anesthesia in different doses on the patients after the thoracoscopic resection of lung cancer.Saudi journal of biological sciences · 2019Article
- Anti-CD47 antibody eliminates bone tumors in rats.Saudi journal of biological sciences · 2019Article
- Functional nano-catalyzed pyrolyzates from branch ofSaudi journal of biological sciences · 2019Article
- Clinical significance of the expression of miRNA-21, miRNA-31 and miRNA-let7 in patients with lung cancer.Saudi journal of biological sciences · 2019Article
- Isolation, genetic identification and degradation characteristics of COD-degradingSaudi journal of biological sciences · 2018Article
- Study of biological networks using graph theory.Saudi journal of biological sciences · 2018Article
- The first multiplication atom-bond connectivity index of molecular structures in drugs.Saudi pharmaceutical journal : SPJ : the official publication of the Saudi Pharmaceutical Society · 2017Article
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5 authors.
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Abstract
In biology field, the ontology application relates to a large amount of genetic information and chemical information of molecular structure, which makes knowledge of ontology concepts convey much information. Therefore, in mathematical notation, the dimension of vector which corresponds to the ontology concept is often very large, and thus improves the higher requirements of ontology algorithm. Under this background, we consider the designing of ontology sparse vector algorithm and application in biology. In this paper, using knowledge of marginal likelihood and marginal distribution, the optimized strategy of marginal based ontology sparse vector learning algorithm is presented. Finally, the new algorithm is applied to gene ontology and plant ontology to verify its efficiency.
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