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Carson MB, Liu C, Lu Y, et al. A disease similarity matrix based on the uniqueness of shared genes. BMC Med Genomics. 2017;10:26doi: 10.1186/s12920-017-0265-2.
Carson, M. B., Liu, C., Lu, Y., Jia, C., & Lu, H. (2017). A disease similarity matrix based on the uniqueness of shared genes. BMC medical genomics, 1026. https://doi.org/10.1186/s12920-017-0265-2
Carson, Matthew B, et al. "A disease similarity matrix based on the uniqueness of shared genes." BMC medical genomics vol. 10 (2017): 26. doi: https://doi.org/10.1186/s12920-017-0265-2
Carson MB, Liu C, Lu Y, Jia C, Lu H. A disease similarity matrix based on the uniqueness of shared genes. BMC Med Genomics. 2017 May 24;10:26. doi: 10.1186/s12920-017-0265-2. PMID: 28589854; PMCID: PMC5461528.
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BMC Med Genomics. 2017 May 24;10:26. doi: 10.1186/s12920-017-0265-2.

A disease similarity matrix based on the uniqueness of shared genes.

BMC medical genomics

Matthew B Carson, Cong Liu, Yao Lu, Caiyan Jia, Hui Lu

Affiliations

  1. Department of Preventive Medicine, Feinberg School of Medicine, Northwestern University, 680 N Lake Shore Dr, Suite 1400, Chicago, IL, 60611, USA.
  2. Department of Bioengineering, University of Illinois at Chicago, 851 S Morgan St, Chicago, IL, 60607, USA.
  3. Center for Biomedical Informatics, Shanghai Children's Hospital, 24 W Beijing Rd, Suite 1400, Shanghai, 200000, China.
  4. Department of Computer Science, Beijing Jiaotong University, No.3 Shangyuancun, Haidian District, Beijing, 100044, China.
  5. Department of Bioengineering, University of Illinois at Chicago, 851 S Morgan St, Chicago, IL, 60607, USA. [email protected].
  6. Center for Biomedical Informatics, Shanghai Children's Hospital, 24 W Beijing Rd, Suite 1400, Shanghai, 200000, China. [email protected].
  7. SJTU-Yale Joint Center for Biostatistics, Department of Bioinformatics and Biostatistics, Shanghai Jiaotong University, 800 Dongchuan Road, Shanghai, 200000, China. [email protected].

PMID: 28589854 PMCID: PMC5461528 DOI: 10.1186/s12920-017-0265-2

Abstract

BACKGROUND: Complex diseases involve many genes, and these genes are often associated with several different illnesses. Disease similarity measurement can be based on shared genotype or phenotype. Quantifying relationships between genes can reveal previously unknown connections and form a reference base for therapy development and drug repurposing.

METHODS: Here we introduce a method to measure disease similarity that incorporates the uniqueness of shared genes. For each disease pair, we calculated the uniqueness score and constructed disease similarity matrices using OMIM and Disease Ontology annotation.

RESULTS: Using the Disease Ontology-based matrix, we identified several interesting connections between cancer and other disease and conditions such as malaria, along with studies to support our findings. We also found several high scoring pairwise relationships for which there was little or no literature support, highlighting potentially interesting connections warranting additional study.

CONCLUSIONS: We developed a co-occurrence matrix based on gene uniqueness to examine the relationships between diseases from OMIM and DORIF data. Our similarity matrix can be used to identify potential disease relationships and to motivate further studies investigating the causal mechanisms in diseases.

Keywords: Clustering; Disease-disease similarity; Disease-related genes

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