title: Network based integrated analysis of phenotype-genotype data for prioritization of candidate symptom genes.
abstract: BACKGROUND
  Symptoms and signs (symptoms in brief) are the essential clinical manifestations for individualized diagnosis and treatment in traditional Chinese medicine (TCM). To gain insights into the molecular mechanism of symptoms, we develop a computational approach to identify the candidate genes of symptoms.
  
  
  METHODS
  This paper presents a network-based approach for the integrated analysis of multiple phenotype-genotype data sources and the prediction of the prioritizing genes for the associated symptoms. The method first calculates the similarities between symptoms and diseases based on the symptom-disease relationships retrieved from the PubMed bibliographic database. Then the disease-gene associations and protein-protein interactions are utilized to construct a phenotype-genotype network. The PRINCE algorithm is finally used to rank the potential genes for the associated symptoms.
  
  
  RESULTS
  The proposed method gets reliable gene rank list with AUC (area under curve) 0.616 in classification. Some novel genes like CALCA, ESR1, and MTHFR were predicted to be associated with headache symptoms, which are not recorded in the benchmark data set, but have been reported in recent published literatures.
  
  
  CONCLUSIONS
  Our study demonstrated that by integrating phenotype-genotype relationships into a complex network framework it provides an effective approach to identify candidate genes of symptoms.
authors.affiliation: School of Computer and Information Technology and Beijing Key Lab of Traffic Data Analysis and Mining, Beijing Jiaotong University, Beijing 100044, China.
authors.forename: Xing
authors.identifier: 0000-0001-9170-7180
authors.initials: X
authors.lastname: Li
authors.affiliation: School of Computer and Information Technology and Beijing Key Lab of Traffic Data Analysis and Mining, Beijing Jiaotong University, Beijing 100044, China.
authors.forename: Xuezhong
authors.identifier: 0000-0002-4713-3594
authors.initials: X
authors.lastname: Zhou
authors.affiliation: School of Engineering and Informatics, University of Bradford, West Yorkshire BD7 1DP, UK.
authors.forename: Yonghong
authors.initials: Y
authors.lastname: Peng
authors.affiliation: China Academy of Chinese Medical Sciences, Beijing 100700, China.
authors.forename: Baoyan
authors.identifier: 0000-0001-9075-4102
authors.initials: B
authors.lastname: Liu
authors.affiliation: Guang'anmen Hospital, China Academy of Chinese Medical Sciences, Beijing 100053, China.
authors.forename: Runshun
authors.identifier: 0000-0002-7127-8865
authors.initials: R
authors.lastname: Zhang
authors.affiliation: Institute of Basic Theory of Traditional Chinese Medicine, China Academy of Chinese Medical Sciences, Beijing 100700, China.
authors.forename: Jingqing
authors.initials: J
authors.lastname: Hu
authors.affiliation: School of Computer and Information Technology and Beijing Key Lab of Traffic Data Analysis and Mining, Beijing Jiaotong University, Beijing 100044, China.
authors.forename: Jian
authors.initials: J
authors.lastname: Yu
authors.affiliation: School of Computer and Information Technology and Beijing Key Lab of Traffic Data Analysis and Mining, Beijing Jiaotong University, Beijing 100044, China.
authors.forename: Caiyan
authors.initials: C
authors.lastname: Jia
authors.affiliation: Liaoning Provincial Key Laboratory of Cerebral Diseases, Institute for Brain Disorders, Dalian Medical University, Dalian 116044, China.
authors.forename: Changkai
authors.identifier: 0000-0002-8830-6105
authors.initials: C
authors.lastname: Sun
mesh_terms: D000465:Algorithms; D019295:Computational Biology; D053263:Gene Regulatory Networks; D056726:Genetic Association Studies; D055106:Genome-Wide Association Study; D006801:Humans; D008516:Medicine, Chinese Traditional; D039781:PubMed