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Extracting Disease-Gene-Drug Correlations Based on Data Cube |
Wei Xing1,2, Hu Dehua1(), Yi Minhan1, Zhu Qizhen1, Zhu Wenjie2 |
1Institute of Information Security and Big Data, Central South University, Changsha 410083, China 2School of Basic Courses, Bengbu Medical College, Bengbu 233003, China |
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Abstract [Objective] This study aims to construct a disease-gene-drug correlation network for diabetes mellitus (DM). [Methods] First, we proposed a new data cube-based approach to construct a disease-gene-drug correlations network for the DM. Then, we measured the associations among the biological entities. [Results] We retrieved the needed data from the PubMed database and constructed three 1-D vertex cubes, three 2-D square cubes and one 3-D disease-gene-drug network, which revealed 411 associations among the 14 subclasses of DM, 23 genes, and 24 drugs. We also constructed 8 optimal disease-gene-drug subnetworks of DM. [Limitations] There were some subjective issues with the data analysis. The changing of user behaviors may also influence the results. [Conclusions] The proposed algorithm is better than the existing ones, which provides new directions for research on customized medical treatments.
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Received: 03 July 2017
Published: 08 November 2017
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[1] |
Moreau Y, Tranchevent L C.Computational Tools for Prioritizing Candidate Genes: Boosting Disease Gene Discovery[J]. Nature Reviews Genetics, 2012, 13(8): 523-536.
doi: 10.1038/nrg3253
pmid: 22751426
|
[2] |
Fundel K, Kuffner R R.RelEx——Relation Extraction Using Dependency Parse Trees[J]. Bioinformatics, 2007, 23(3): 365-371.
doi: 10.1093/bioinformatics/btl616
pmid: 17142812
|
[3] |
Bui Q C, Sloot P M, van Mulligen E M, et al. A Novel Feature-Based Approach to Extract Drug-Drug Interactions from Biomedical Text[J]. Bioinformatics, 2014, 30(23): 3365-3371.
doi: 10.1093/bioinformatics/btu557
pmid: 25143286
|
[4] |
Xu R, Wang Q Q.Large-scale Extraction of Accurate Drug-Disease Treatment Pairs from Biomedical Literature for Drug Repurposing[J]. BMC Bioinformatics, 2013, 14(13): 1-11.
doi: 10.1186/1471-2105-14-1
pmid: 23323762
|
[5] |
Gray J, Bosworth A, Layman A, et al.Data Cube. A Relational Aggregation Operator Generalizing Group-By, Cross-Tab, and Sub-Total[J]. Data Mining & Knowledge Discovery, 1997, 1(1): 29-53.
doi: 10.1023/A:1009726021843
|
[6] |
Piro R M.Computational Approaches to Disease-Gene Prediction: Rationale, Classification and Successes[J]. Febs Journal, 2012, 279(5): 678-696.
doi: 10.1111/j.1742-4658.2012.08471.x
pmid: 22221742
|
[7] |
Goh K I, Cusick M E, Valle D, et al.The Human Disease Network[J]. Proceedings of the National Academy of Sciences of the United States of America, 2007, 104(21): 8685-8690.
doi: 10.1073/pnas.0701361104
|
[8] |
Suthram S.Network-Based Elucidation of Human Disease Similarities Reveals Common Functional Modules Enriched for Pluripotent Drug Targets[J]. PLoS Computational Biology, 2010, 6(2): e1000662.
doi: 10.1371/journal.pcbi.1000662
|
[9] |
Arrell D K, Terzic A.Network Systems Biology for Drug Discovery[J]. Clinical Pharmacology & Therapeutics, 2010, 88(1): 120-125.
doi: 10.1038/clpt.2010.91
pmid: 20520604
|
[10] |
Lamb J, Craeford E D, Peck D, et al.The Connectivity Map: Using Gene-Expression Signatures to Connect Small Molecules, Genes, and Disease[J]. Science, 2006, 313(5795): 1929-1935.
doi: 10.1126/science.1132939
pmid: 17008526
|
[11] |
Natarajan N.Inductive Matrix Completion for Predicting Gene-Disease Associations[J]. Bioinformatics, 2014, 30(12): 60-68.
doi: 10.1093/bioinformatics/btu269
pmid: 4058925
|
[12] |
Odibat O, Reddy C K.Efficient Mining of Discriminative Co-clusters from Gene Expression Data[J]. Knowledge & Information Systems, 2014, 41(3): 667-696.
doi: 10.1007/s10115-013-0684-0
pmid: 4308820
|
[13] |
Li J, Edwards S M, Bo T, et al.A Random Set Scoring Model for Prioritization of Disease Candidate Genes Using Protein Complexes and Data-Mining of GeneRIF, OMIM and PubMed Records[J]. BMC Bioinformatics, 2014, 15(22): 3946-3959.
doi: 10.1186/1471-2105-15-315
pmid: 154876224409799996603
|
[14] |
Frijters R, Vugt M V, Smeets R, et al.Literature Mining for the Discovery of Hidden Connections Between Drugs, Genes and Diseases[J]. PLoS Computational Biology, 2010, 6(9): e10000943.
|
[15] |
Jenssen T K, Laegreid A, Komorowski J, et al.A Literature Network of Human Genes for High-Throughput Analysis of Gene Expression[J]. Nature Genetics, 2001, 28(1): 21-28.
doi: 10.1038/ng0501-21
pmid: 11326270
|
[16] |
Li C, Ooi B C, Tung A K H, et al. DADA: A Data Cube for Dominant Relationship Analysis[C]// Proceedings of the 2006 ACM SIGMOD International Conference on Management of Data. 2006: 659-670.
|
[17] |
Fang M, Shivakumar N, Garcia-Molina H, et al.Computing Iceberg Queries Efficiently[C]// Proceedings of the 24th International Conference on Very Large Data Bases. 1998: 299-310.
|
[18] |
Beyer K S, Ramakrishnan R.Bottom-Up Computation of Sparse and Iceberg CUBEs[C]// Proceedings of the 1999 ACM SIGMOD International Conference on Management of Data. 1999.
|
[19] |
Gonzalez G H, Tahsin T, Goodale B C, et al.Recent Advances and Emerging Applications in Text and Data Mining for Biomedical Discovery[J]. Briefings in Bioinformatics, 2016, 17(1): 33-42.
doi: 10.1093/bib/bbv087
pmid: 4719073
|
[20] |
Development Core R Team. R: A Language and Environment for Statistical Computing[J]. Computing, 2013, 14: 12-21.
doi: 10.1890/0012-9658(2002)083[3097:CFHIWS]2.0.CO;2
|
[21] |
Hanley J A, Mcneil B J.The Meaning and Use of the Area Under a Receiver Operating Characteristic (ROC) Curve[J]. Radiology, 1982, 143(1): 29-36.
doi: 10.1148/radiology.143.1.7063747
pmid: 7063747
|
[22] |
Donna M, Jim O, Pruitt K D, et al.Entrez Gene: Gene-Centered Information at NCBI[J]. Nucleic Acids Research, 2007, 39(2): 54-58.
doi: 10.1093/nar/gki031
pmid: 17148475
|
[23] |
Pruitt K D, Tatiana T, Maglott D R. NCBI Reference Sequences (RefSeq): A Curated Non-Redundant Sequence Database of Genomes Transcripts and Proteins[J]. Nucleic Acids Research, 2008, 33: 501-504.
doi: 10.1093/nar/gki025
pmid: 15608248
|
[24] |
Ashburner M, Ball C A, Blake J A, et al.Gene Ontology: Tool for the Unification of Biology[J]. Nature Genetics, 2000, 25(1): 25-29.
doi: 10.1038/75556
|
[25] |
Hamosh A, Scott A F, Amberger J S, et al.Online Mendelian Inheritance in Man (OMIM), A Knowledgebase of Human Genes and Genetic Disorders[J]. Nucleic Acids Research, 2005, 33(1): 514-517.
doi: 10.1093/nar/gki033
pmid: 15608251
|
[26] |
Knox C, Law V, Jewison T, et al.DrugBank 3.0: A Comprehensive Resource for ‘Omics’ Research on Drugs[J]. Nucleic Acids Research, 2011, 39(S1): 1035-1041.
doi: 10.1093/nar/gkq1126
pmid: 3013709
|
[27] |
Lang V Y, Fatehi M, Light P E.Pharmacogenomic Analysis of ATP-Sensitive Potassium Channels Coexpressing the Common Type 2 Diabetes Risk Variants E23K and S1369A[J]. Pharmacogenetics & Genomics, 2012, 22(3): 206-214.
doi: 10.1097/FPC.0b013e32835001e7
pmid: 22209866
|
[28] |
Tenenbaum A, Fisman E Z.Balanced Pan-PPAR Activator Bezafibrate in Combination with Statin: Comprehensive Lipids Control and Diabetes Prevention?[J]. Cardiovascular Diabetology, 2012, 11(2): 140.
doi: 10.1186/1475-2840-11-140
pmid: 3502168
|
[29] |
Ke J T, Li M, Xu S Q, et al.Gliquidone Decreases Urinary Protein by Promoting Tubular Reabsorption in Diabetic Goto- Kakizaki Rats[J]. Journal of Endocrinology, 2014, 220(2): 129-141.
doi: 10.1530/JOE-13-0199
pmid: 24254365
|
[30] |
Hui Z, Min G, Zhou T, et al.An Isogenic Human ESC Platform for Functional Evaluation of Genome-wide- Association-Study-Identified Diabetes Genes and Drug Discovery[J]. Cell Stem Cell, 2016, 9: 326-340.
doi: 10.1016/j.stem.2016.07.002
pmid: 27524441
|
[31] |
Nichols C G, Koster J C, Remedi M S.Beta-cell Hyperexcitability: From Hyperinsulinism to Diabetes[J]. Diabetes Obesity & Metabolism, 2007, 9(S2): 81-88.
doi: 10.1111/j.1463-1326.2007.00778.x
pmid: 17919182
|
[32] |
张闻. 英汉人类基因词典[M]. 北京: 人民卫生出版社, 2011.
|
[32] |
(Zhang Wen.English Chinese Dictionary of Human Genes [M]. Beijing: People’s Medical Publishing House, 2011.)
|
[33] |
Rudofsky G, Schlotterer A, Humpert P M, et al.A M55V Polymorphism in the SUMO4 Gene is Associated with a Reduced Prevalence of Diabetic Retinopathy in Patients with Type 1 Diabetes[J]. Experimental & Clinical Endocrinology & Diabetes, 2007, 116(1): 14-17.
doi: 10.1055/s-2007-985357
pmid: 17926234
|
[34] |
Esmatjes E, Jimenez A, Diaz G, et al.Neonatal Diabetes with End-Stage Nephropathy Pancreas Transplantation Decision[J]. Diabetes Care, 2008, 31(11): 2116-2117.
doi: 10.2337/dc08-0823
|
[35] |
Stefanski A, Majkowska L, Ciechanowicz A, et al.The Common C49620T Polymorphism in the Sulfonylurea Receptor Gene (ABCC8), Pancreatic Beta Cell Function and Long-Term Diabetic Complications in Obese Patients with Long-Lasting Type 2 Diabetes Mellitus[J]. Experimental & Clinical Endocrinology & Diabetes, 2007, 115(5): 317-321.
|
[36] |
Sun K, Liu H, Yeganova L, et al.Extracting Drug-Drug Interactions from Literature Using a Rich Feature-Based Linear Kernel Approach[J]. Journal of Biomedical Informatics, 2015, 55: 23-30.
doi: 10.1016/j.jbi.2015.03.002
pmid: 25796456
|
[37] |
Rong X, Wang Q Q.Large-scale Automatic Extraction of Side Effects Associated with Targeted Anticancer Drugs from Full-Text Oncological Articles[J]. Journal of Biomedical Informatics, 2015, 55: 64-72.
doi: 10.1016/j.jbi.2015.03.009
pmid: 25817969
|
[38] |
Gonzalez G H, Tahsin T, Goodale B C, et al.Recent Advances and Emerging Applications in Text and Data Mining for Biomedical Discovery[J]. Briefings in Bioinformatics, 2015, 29: 1-10.
doi: 10.1093/bib/bbv087
pmid: 4719073
|
[39] |
Boulil K, Bimonte S, Pinet F.Conceptual Model for Spatial Data Cubes: A UML Profile and Its Automatic Implementation[J]. Computer Standards & Interfaces, 2014, 38: 113-132.
doi: 10.1016/j.csi.2014.06.004
|
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