HPOLabeler: improving prediction of human protein-phenotype associations by learning to rank
HPOLabeler: improving prediction of human protein-phenotype associations by learning to rank
复制标题
HPOLabeler：通过学习排序改进人类蛋白质表型关联的预测
DOI：
10.1093/bioinformatics/btaa284
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发表时间：
2020-08-15
期刊：
影响因子：
5.8
通讯作者：
Zhu, Shanfeng
中科院分区：
文献类型：
--
作者：
Liu, Lizhi;Huang, Xiaodi;Zhu, Shanfeng
动机
MOTIVATION
Annotating human proteins by abnormal phenotypes has become an important topic. Human Phenotype Ontology (HPO) is a standardized vocabulary of phenotypic abnormalities encountered in human diseases. As of Nov. 2019, only less than 4,000 proteins have been annotated with HPO. Thus a computational approach for accurately predicting protein-HPO associations would be important, while no methods have outperformed a simple Naive approach in the CAFA2 (second Critical Assessment of Functional Annotation, 2013-14).
RESULTS
We present HPOLabeler, which is able to use a wide variety of evidence, such as protein-protein interaction networks (PPI), Gene Ontology (GO), InterPro, trigram frequency and HPO term frequency, in the framework of learning to rank (LTR). LTR has been proved to be powerful for solving large-scale, multi-label ranking problems in bioinformatics. Given an input protein, LTR outputs the ranked list of HPO terms from a series of input scores given to the candidate HPO terms by component learning models (logistic regression, nearest neighbor and a Naive method), which are trained from given multiple evidence. We empirically evaluate HPOLabeler extensively through mainly two experiments of cross-validation and temporal validation, for which HPOLabeler significantly outperformed all component models and competing methods including the current state-of-the-art method. We further found that 1) PPI is most informative for prediction among diverse data sources, and 2) low prediction performance of temporal validation might be caused by incomplete annotation of new proteins.
AVAILABILITY
http://issubmission.sjtu.edu.cn/hpolabeler/.
SUPPLEMENTARY INFORMATION
Supplementary data are available at Bioinformatics online.