title: A Comprehensive Evaluation of Disease Phenotype Networks for Gene Prioritization.
abstract: Identification of disease-causing genes is a fundamental challenge for human health studies. The phenotypic similarity among diseases may reflect the interactions at the molecular level, and phenotype comparison can be used to predict disease candidate genes. Online Mendelian Inheritance in Man (OMIM) is a database of human genetic diseases and related genes that has become an authoritative source of disease phenotypes. However, disease phenotypes have been described by free text; thus, standardization of phenotypic descriptions is needed before diseases can be compared. Several disease phenotype networks have been established in OMIM using different standardization methods. Two of these networks are important for phenotypic similarity analysis: the first and most commonly used network (mimMiner) is standardized by medical subject heading, and the other network (resnikHPO) is the first to be standardized by human phenotype ontology. This paper comprehensively evaluates for the first time the accuracy of these two networks in gene prioritization based on protein-protein interactions using large-scale, leave-one-out cross-validation experiments. The results show that both networks can effectively prioritize disease-causing genes, and the approach that relates two diseases using a logistic function improves prioritization performance. Tanimoto, one of four methods for normalizing resnikHPO, generates a symmetric network and it performs similarly to mimMiner. Furthermore, an integration of these two networks outperforms either network alone in gene prioritization, indicating that these two disease networks are complementary.
authors.affiliation: Department of Biomedical Informatics, Sino-Dutch Biomedical and Information Engineering School, Northeastern University, Shenyang, Liaoning, China.|Key Laboratory of Medical Image Computing of Northeastern University, Ministry of Education, Shenyang, Liaoning, China.
authors.forename: Jianhua
authors.initials: J
authors.lastname: Li
authors.affiliation: Department of Biomedical Informatics, Sino-Dutch Biomedical and Information Engineering School, Northeastern University, Shenyang, Liaoning, China.
authors.forename: Xiaoyan
authors.initials: X
authors.lastname: Lin
authors.affiliation: Department of Biomedical Imaging, Sino-Dutch Biomedical and Information Engineering School, Northeastern University, Shenyang, Liaoning, China.
authors.forename: Yueyang
authors.initials: Y
authors.lastname: Teng
authors.affiliation: Key Laboratory of Medical Image Computing of Northeastern University, Ministry of Education, Shenyang, Liaoning, China.|Department of Biomedical Imaging, Sino-Dutch Biomedical and Information Engineering School, Northeastern University, Shenyang, Liaoning, China.
authors.forename: Shouliang
authors.initials: S
authors.lastname: Qi
authors.affiliation: Department of Biomedical Imaging, Sino-Dutch Biomedical and Information Engineering School, Northeastern University, Shenyang, Liaoning, China.
authors.forename: Dayu
authors.initials: D
authors.lastname: Xiao
authors.affiliation: Department of Biomedical Informatics, Sino-Dutch Biomedical and Information Engineering School, Northeastern University, Shenyang, Liaoning, China.|Border Biomedical Research Center, Department of Biological Sciences, The University of Texas at El Paso, El Paso, Texas, United States of America.
authors.forename: Jianying
authors.initials: J
authors.lastname: Zhang
authors.affiliation: Key Laboratory of Medical Image Computing of Northeastern University, Ministry of Education, Shenyang, Liaoning, China.|Department of Biomedical Imaging, Sino-Dutch Biomedical and Information Engineering School, Northeastern University, Shenyang, Liaoning, China.
authors.forename: Yan
authors.initials: Y
authors.lastname: Kang
mesh_terms: D000465:Algorithms; D053263:Gene Regulatory Networks / Q000235:genetics*; D005796:Genes / Q000235:genetics; D030342:Genetic Diseases, Inborn / Q000235:genetics*; D006292:Health Priorities; D006801:Humans; D008954:Models, Biological; D010641:Phenotype