Gao Wei, Qudair Baig Abdul, Ali Haidar, Sajjad Wasim, Reza Farahani Mohammad
School of Information Science and Technology, Yunnan Normal University, Kunming 650500, China.
Department of Mathematics, COMSATS Institute of Information Technology, Attock, Pakistan.
Saudi J Biol Sci. 2017 Jan;24(1):132-138. doi: 10.1016/j.sjbs.2016.09.001. Epub 2016 Sep 9.
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.
在生物学领域，本体应用涉及大量的遗传信息和分子结构的化学信息，这使得本体概念的知识传达了很多信息。因此，在数学表示中，与本体概念相对应的向量维度通常非常大，从而提高了对本体算法的更高要求。在此背景下，我们考虑本体稀疏向量算法的设计及其在生物学中的应用。本文利用边际似然和边际分布的知识，提出了基于边际的本体稀疏向量学习算法的优化策略。最后，将新算法应用于基因本体和植物本体以验证其有效性。
