Network Analysis and Applications in Pediatric Research

  • Hailong Li
  • Zhaowei Ren
  • Sheng Ren
  • Xinyu Guo
  • Xiaoting Zhu
  • Long Jason Lu
Chapter
Part of the Translational Bioinformatics book series (TRBIO, volume 10)

Abstract

Networks, where nodes denote entities and links denote associations, provide a unified representation for a variety of complex systems, from social relationships to molecular interactions. In an era of big data, network analysis has been proved useful in biological applications such as predicting functions of proteins, guiding the design of wet-lab experiments, and discovering biomarkers of diseases. Driven by the availability of large-scale data sets and rapid development of bioinformatics’ tools, the research community has applied network analysis to define underlying causes of pediatric diseases. This will almost certainly lead to more effective strategies for prevention and treatment of diseases. In this chapter, we will introduce classic and the state-of-the-art network analysis methodologies, approaches and their applications. We then provide four examples of recent research, where network analysis is being applied in pediatrics. These include the identification of high-density lipoprotein particles that underlie the development of cardiovascular disease using protein-protein interaction networks, alternative splicing analysis by splicing interaction network, construction and network analysis of pediatric brain functional atlas, and disease relationship exploration using diagnosis association networks constructed by electronic health record.

Keywords

Network analysis Network applications Network prediction Pediatric diseases 

References

  1. Albert R, Jeong H, Barabasi AL. Error and attack tolerance of complex networks. Nature. 2000;406:378–82.CrossRefPubMedGoogle Scholar
  2. Arnold LD, Bachmann GA, Kelly S, Rosen R, Rhoads GG. Vulvodynia: characteristics and associations with co-morbidities and quality of life. Obstetrics and Gynecology. 2006;107:617.CrossRefPubMedPubMedCentralGoogle Scholar
  3. Arnold LD, Bachmann GA, Rosen R, Rhoads GG. Assessment of vulvodynia symptoms in a sample of US women: a prevalence survey with a nested case control study. Am J Obstet Gynecol. 2007;196: 28. e1-28. e6.Google Scholar
  4. Association, American Psychiatric Association. Diagnostic and statistical manual of mental disorders (DSM-5®). American Psychiatric Pub, 2013.Google Scholar
  5. Barabasi AL, Oltvai ZN. Network biology: understanding the cell’s functional organization. Nat Rev Genet. 2004;5:101–13.CrossRefPubMedGoogle Scholar
  6. Barter PJ, Nicholls S, Rye KA, Anantharamaiah GM, Navab M, Fogelman AM. Antiinflammatory properties of HDL. Circ Res. 2004;95:764–72.CrossRefPubMedGoogle Scholar
  7. Bastian M, Heymann S, Jacomy M. Gephi: an open source software for exploring and manipulating networks. In International AAAI conference on Weblogs and Social Media. 2009.Google Scholar
  8. Batagelj V, Mrvar A. Pajek – analysis and visualization of large networks. Graph Drawing Software. 2004:77–103.Google Scholar
  9. Berardini TZ, Khodiyar VK, Lovering RC, Talmud P. The gene ontology in 2010: extensions and refinements. Nucleic Acids Res. 2010;38:D331–5.CrossRefGoogle Scholar
  10. Boden WE. High-density lipoprotein cholesterol as an independent risk factor in cardiovascular disease: assessing the data from Framingham to the Veterans Affairs High – Density Lipoprotein Intervention Trial. Am J Cardiol. 2000;86:19L–22L.CrossRefPubMedGoogle Scholar
  11. Brun C, Herrmann C, Guenoche A. Clustering proteins from interaction networks for the prediction of cellular functions. BMC Bioinformatics. 2004;5:95.CrossRefPubMedPubMedCentralGoogle Scholar
  12. Chen C-A, Chung W-C, Chiou Y-Y, Yang Y-J, Lin Y-C, Ochs HD, Shieh CC. Quantitative analysis of tissue inflammation and responses to treatment in immune dysregulation, polyendocrinopathy, enteropathy, X-linked syndrome, and review of literature. J Microbiol, Immunol Infect. 2015.Google Scholar
  13. Colby JB, Rudie JD, Brown JA, Douglas PK, Cohen MS, Shehzad Z. Insights into multimodal imaging classification of ADHD. Front Syst Neurosci. 2012;6:59.CrossRefPubMedPubMedCentralGoogle Scholar
  14. Corominas R, Yang X, Lin GN, Kang S, Shen Y, Ghamsari L, Broly M, Rodriguez M, Tam S, Trigg SA, Fan C, Yi S, Tasan M, Lemmens I, Kuang X, Zhao N, Malhotra D, Michaelson JJ, Vacic V, Calderwood MA, Roth FP, Tavernier J, Horvath S, Salehi-Ashtiani K, Korkin D, Sebat J, Hill DE, Hao T, Vidal M, Iakoucheva LM. Protein interaction network of alternatively spliced isoforms from brain links genetic risk factors for autism. Nat Commun. 2014;5:3650.CrossRefPubMedPubMedCentralGoogle Scholar
  15. Croft D, O’Kelly G, Wu G, Haw R, Gillespie M, Matthews L, Caudy M, Garapati P, Gopinath G, Jassal B, Jupe S, Kataskaya I, Mahajan S, May B, Ndegwa N, Schmidt E, Shamovsky V, Yung C, Birney E, Hermjakob H, D’Eustachio P, Stein L. Reactome: a database of reactions, pathways and biological processes. Nucleic Acids Research. 2010;39:691–97.CrossRefGoogle Scholar
  16. Cuchel M, Rader DJ. Macrophage reverse cholesterol transport key to the regression of atherosclerosis? Circulation. 2006;113:2548–55.CrossRefPubMedGoogle Scholar
  17. Dai D, Wang J, Hua J, He H. Classification of ADHD children through multimodal magnetic resonance imaging. Front Syst Neurosci. 2012;6:63.CrossRefPubMedPubMedCentralGoogle Scholar
  18. Davidson WS, Gangani RA, Silva D, Chantepie S, Lagor WR, Chapman MJ, Kontush A. Proteomic analysis of defined HDL subpopulations reveals particle-specific protein clusters relevance to antioxidative function. Arteriosclerosis, Thrombosis, and Vascular Biology. 2009;29:870–76.CrossRefPubMedPubMedCentralGoogle Scholar
  19. Dick RS, Steen EB, Detmer DE. The computer-based patient record:: an essential technology for health care. Washington, DC: National Academies Press; 1997.Google Scholar
  20. Emig D, Salomonis N, Baumbach J, Lengauer T, Conklin BR, Albrecht M. AltAnalyze and DomainGraph: analyzing and visualizing exon expression data. Nucleic Acids Research. 2010;38:W755–W62.CrossRefPubMedPubMedCentralGoogle Scholar
  21. Franceschini G, Maderna P, Sirtori CR. Reverse cholesterol transport: physiology and pharmacology. Atherosclerosis. 1991;88:99–107.CrossRefPubMedGoogle Scholar
  22. Gan Z, Wang J, Salomonis N, Stowe JC, Haddad GG, McCulloch AD, Altintas I, Zambon AC. MAAMD: a workflow to standardize meta-analyses and comparison of affymetrix microarray data. BMC Bioinformatics. 2014;15:1–11.CrossRefGoogle Scholar
  23. Girvan M, Newman ME. Community structure in social and biological networks. Proc Natl Acad Sci U S A. 2002;99:7821–6.CrossRefPubMedPubMedCentralGoogle Scholar
  24. Goel R, Harsha HC, Pandey A, Prasad TS. Human protein reference database and human proteinpedia as resources for phosphoproteome analysis. Mol Biosyst. 2012;8:453–63.CrossRefPubMedGoogle Scholar
  25. Gordon S, Durairaj A, Jason LL, Sean Davidson W. High-density lipoprotein proteomics: identifying new drug targets and biomarkers by understanding functionality. Current Cardiovascular Risk Reports. 2010a;4:1–8.CrossRefPubMedPubMedCentralGoogle Scholar
  26. Gordon SM, Deng J, Jason Lu L, Sean Davidson W. Proteomic characterization of human plasma high density lipoprotein fractionated by gel filtration chromatography. Journal of Proteome Research. 2010b;9:5239–49.CrossRefPubMedPubMedCentralGoogle Scholar
  27. Gordon SM, Li H, Zhu X, Shah AS, Lu LJ, Sean Davidson W. A comparison of the mouse and human lipoproteome: suitability of the mouse model for studies of human lipoproteins. Journal of Proteome Research. 2015;14:2686–95.CrossRefPubMedPubMedCentralGoogle Scholar
  28. Guelzim N, Bottani S, Bourgine P, Kepes F. Topological and causal structure of the yeast transcriptional regulatory network. Nat Genet. 2002;31:60–3.CrossRefPubMedGoogle Scholar
  29. Gunter TD, Terry NP. The emergence of national electronic health record architectures in the United States and Australia: models, costs, and questions. Journal of Medical Internet Research. 2005;7:e3.CrossRefPubMedPubMedCentralGoogle Scholar
  30. Hanauer DA, Rhodes DR, Chinnaiyan AM. Exploring clinical associations using ‘-omics’ based enrichment analyses. PLoS One. 2009;4:e5203.CrossRefPubMedPubMedCentralGoogle Scholar
  31. Hartwell LH, Hopfield JJ, Leibler S, Murray AW. From molecular to modular cell biology. Nature. 1999;402:C47–52.CrossRefPubMedGoogle Scholar
  32. Heller M, Stalder D, Schlappritzi E, Hayn G, Matter U, Haeberli A. Mass spectrometry – based analytical tools for the molecular protein characterization of human plasma lipoproteins. Proteomics. 2005;5:2619–30.CrossRefPubMedGoogle Scholar
  33. Homsy J, Zaidi S, Shen Y, Ware JS, Samocha KE, Karczewski KJ, DePalma SR, McKean D, Wakimoto H, Gorham J, Jin SC, Deanfield J, Giardini A, Porter GA, Kim R, Bilguvar K, López-Giráldez F, Tikhonova I, Mane S, Romano-Adesman A, Qi H, Vardarajan B, Ma L, Daly M, Roberts AE, Russell MW, Mital S, Newburger JW, William Gaynor J, Breitbart RE, Iossifov I, Ronemus M, Sanders SJ, Kaltman JR, Seidman JG, Brueckner M, Gelb BD, Goldmuntz E, Lifton RP, Seidman CE, Chung WK. De novo mutations in congenital heart disease with neurodevelopmental and other congenital anomalies. Science. 2015;350:1262–66.CrossRefPubMedPubMedCentralGoogle Scholar
  34. Hu Z, Hung JH, Wang Y, Chang YC, Huang CL, Huyck M, DeLisi C. VisANT 3.5: multi-scale network visualization, analysis and inference based on the gene ontology. Nucleic Acids Res. 2009;37:W115–21.CrossRefPubMedPubMedCentralGoogle Scholar
  35. Hull J, Campino S, Rowlands K, Chan M-S, Copley RR, Taylor MS, Rockett K, Elvidge G, Keating B, Knight J, Kwiatkowski D. Identification of common genetic variation that modulates alternative splicing. PLoS Genet. 2007;3:e99.CrossRefPubMedPubMedCentralGoogle Scholar
  36. Jeong H, Mason SP, Barabasi AL, Oltvai ZN. Lethality and centrality in protein networks. Nature. 2001;411:41–2.CrossRefPubMedGoogle Scholar
  37. Kao HL, Gunsalus KC. Browsing multidimensional molecular networks with the generic network browser (N-Browse). Curr Protoc Bioinformatics, Chapter 9: Unit 9 11. 2008.Google Scholar
  38. Karlsson H, Leanderson P, Tagesson C, Lindahl M. Lipoproteomics II: Mapping of proteins in high – density lipoprotein using two – dimensional gel electrophoresis and mass spectrometry. Proteomics. 2005;5:1431–45.CrossRefPubMedGoogle Scholar
  39. Kerstjens-Frederikse WS, van de Laar IMBH, Vos YJ, Verhagen JMA, Berger RMF, Lichtenbelt KD,Wassink-Ruiter JSK, van der Zwaag PA, du Marchie Sarvaas GJ, Bergman KA, Bilardo CM, Roos-Hesselink JW, Janssen JHP, Frohn-Mulder IM, van Spaendonck-Zwarts KY, van Melle JP, Hofstra RMW, Wessels MW. Cardiovascular malformations caused by NOTCH1 mutations do not keep left: data on 428 probands with left-sided CHD and their families. Genet Med. 2016.Google Scholar
  40. King AD, Przulj N, Jurisica I. Protein complex prediction via cost-based clustering. Bioinformatics. 2004;20:3013–20.CrossRefPubMedGoogle Scholar
  41. Koh K-N, Im HJ, Chung N-G, Cho B, Kang HJ, Shin HY, Lyu CJ, Yoo KH, Koo HH, Kim H-J, Baek HJ, Kook H, Yoon HS, Lim YT, Kim HS, Ryu KH, Seo JJ, Party the Korea Histiocytosis Working. Clinical features, genetics, and outcome of pediatric patients with hemophagocytic lymphohistiocytosis in Korea: report of a nationwide survey from Korea Histiocytosis Working Party. European Journal of Haematology. 2015;94:51–9.CrossRefPubMedGoogle Scholar
  42. Kutmon M, Riutta A, Nunes N, Hanspers K, Willighagen EL, Bohler A, Mélius J, Waagmeester A, Sinha SR, Miller R, Coort SL, Cirillo E, Smeets B, Evelo CT, Pico AR. WikiPathways: capturing the full diversity of pathway knowledge. Nucleic Acids Research. 2016;44:D488–D94.CrossRefPubMedGoogle Scholar
  43. Lewis GF, Rader DJ. New insights into the regulation of HDL metabolism and reverse cholesterol transport. Circulation research. 2005;96:1221–32.CrossRefPubMedGoogle Scholar
  44. Li H, Gordon SM, Zhu X, Deng J, Swertfeger DK, Davidson WS, Lu LJ. Network-based analysis on orthogonal separation of human plasma uncovers distinct high density lipoprotein complexes. J Proteome Res. 2015;14:3082–94.CrossRefPubMedGoogle Scholar
  45. Lu CX, Gong HR, Liu XY, Wang J, Zhao CM, Huang RT, Xue S, Yang YQ. A novel HAND2 loss-of-function mutation responsible for tetralogy of Fallot. International Journal of Molecular Medicine. 2016;37:445–51.PubMedGoogle Scholar
  46. Lucas CL, Yu Z, Venida A, Wang Y, Hughes J, McElwee J, Butrick M, Matthews H, Price S, Biancalana M, Wang X, Richards M, Pozos T, Barlan I, Ahmet O, Koneti Rao V, Su HC, Lenardo MJ. Heterozygous splice mutation in PIK3R1 causes human immunodeficiency with lymphoproliferation due to dominant activation of PI3K. The Journal of Experimental Medicine. 2014;211:2537–47.CrossRefPubMedPubMedCentralGoogle Scholar
  47. Maslov S, Sneppen K. Specificity and stability in topology of protein networks. Science. 2002;296:910–3.CrossRefPubMedGoogle Scholar
  48. Mineo C, Deguchi H, Griffin JH, Shaul PW. Endothelial and antithrombotic actions of HDL. Circulation Research. 2006;98:1352–64.CrossRefPubMedGoogle Scholar
  49. Naqvi TZ, Shah PK, Ivey PA, Molloy MD, Thomas AM, Panicker S, Ahmed A, Cercek B, Kaul S. Evidence that high-density lipoprotein cholesterol is an independent predictor of acute platelet-dependent thrombus formation. The American Journal of Cardiology. 1999;84:1011–17.CrossRefPubMedGoogle Scholar
  50. Negre-Salvayre A, Dousset N, Ferretti G, Bacchetti T, Curatola G, Salvayre R. Antioxidant and cytoprotective properties of high-density lipoproteins in vascular cells. Free Radical Biology and Medicine. 2006;41:1031–40.CrossRefPubMedGoogle Scholar
  51. Nightingale F. Notes on hospitals (Longman, Green, Longman, Roberts, and Green). 1863.Google Scholar
  52. Nofer J-R, Kehrel B, Fobker M, Levkau B, Assmann G, von Eckardstein A. HDL and arteriosclerosis: beyond reverse cholesterol transport. Atherosclerosis. 2002;161:1–16.CrossRefPubMedGoogle Scholar
  53. O’Madadhain J, Fisher D, White S, Boey YB. The JUNG (Java Universal Network/Graph) framework. In: Technical report UCI-ICS 03–17. Irvine: UC Irvine; 2003. p. 03–17.Google Scholar
  54. Palla G, Derenyi I, Farkas I, Vicsek T. Uncovering the overlapping community structure of complex networks in nature and society. Nature. 2005;435:814–8.CrossRefPubMedGoogle Scholar
  55. Ravasz E. Detecting hierarchical modularity in biological networks. Methods Mol Biol. 2009;541:145–60.CrossRefPubMedGoogle Scholar
  56. Rezaee F, Bruno C, Han J, Levels M, Speijer D, Meijers J. Proteomic analysis of high-density lipoprotein. Proteomics. 2006;6:721–30.CrossRefPubMedGoogle Scholar
  57. Rual JF, Venkatesan K, Hao T, Hirozane-Kishikawa T, Dricot A, Li N, Berriz GF, Gibbons FD, Dreze M, Ayivi-Guedehoussou N, Klitgord N, Simon C, Boxem M, Milstein S, Rosenberg J, Goldberg DS, Zhang LV, Wong SL, Franklin G, Li S, Albala JS, Lim J, Fraughton C, Llamosas E, Cevik S, Bex C, Lamesch P, Sikorski RS, Vandenhaute J, Zoghbi HY, Smolyar A, Bosak S, Sequerra R, Doucette-Stamm L, Cusick ME, Hill DE, Roth FP, Vidal M. Towards a proteome-scale map of the human protein-protein interaction network. Nature. 2005;437:1173–8.CrossRefPubMedGoogle Scholar
  58. Safran C, Meryl B, Edward Hammond W, Labkoff S, Markel-Fox S, Tang PC, Detmer DE. Toward a national framework for the secondary use of health data: an American Medical Informatics Association White Paper. Journal of the American Medical Informatics Association. 2007;14:1–9.CrossRefPubMedPubMedCentralGoogle Scholar
  59. Sato JR, Hoexter MQ, Castellanos XF, Rohde LA. Abnormal brain connectivity patterns in adults with ADHD: a coherence study. PLoS One. 2012a;7:e45671.CrossRefPubMedPubMedCentralGoogle Scholar
  60. Sato JR, Hoexter MQ, Fujita A, Rohde LA. Evaluation of pattern recognition and feature extraction methods in ADHD prediction. Front Syst Neurosci. 2012b;6:68.CrossRefPubMedPubMedCentralGoogle Scholar
  61. Sato JR, Takahashi DY, Hoexter MQ, Massirer KB, Fujita A. Measuring network’s entropy in ADHD: a new approach to investigate neuropsychiatric disorders. Neuroimage. 2013;77:44–51.CrossRefPubMedGoogle Scholar
  62. Schaffer AE, Eggens VRC, Caglayan AO, Reuter MS, Scott E, Coufal NG, Silhavy JL, Xue Y, Kayserili H, Yasuno K, Rosti RO, Abdellateef M, Caglar C, Kasher PR, Cazemier JL, Weterman MA, Cantagrel V, Cai N, Zweier C, Altunoglu U, Bilge Satkin N, Aktar F, Tuysuz B, Yalcinkaya C, Caksen H, Bilguvar K, Fu X-D, Trotta C, Gabriel S, Reis A, Gunel M, Baas F, Gleeson JG. CLP1 founder mutation links tRNA splicing and maturation to cerebellar development and neurodegeneration. Cell. 2014;157:651–63.CrossRefPubMedPubMedCentralGoogle Scholar
  63. Shannon P, Markiel A, Ozier O, Baliga NS, Wang JT, Ramage D, Amin N, Schwikowski B, Ideker T. Cytoscape: a software environment for integrated models of biomolecular interaction networks. Genome Res. 2003;13:2498–504.CrossRefPubMedPubMedCentralGoogle Scholar
  64. Soreq L, Guffanti A, Salomonis N, Simchovitz A, Israel Z, Bergman H, Soreq H. Long non-coding RNA and alternative splicing modulations in Parkinson’s leukocytes identified by RNA sequencing. PLoS Comput Biol. 2014;10:e1003517.CrossRefPubMedPubMedCentralGoogle Scholar
  65. Spirin V, Mirny LA. Protein complexes and functional modules in molecular networks. Proc Natl Acad Sci U S A. 2003;100:12123–8.CrossRefPubMedPubMedCentralGoogle Scholar
  66. Stuart JM, Segal E, Koller D, Kim SK. A gene-coexpression network for global discovery of conserved genetic modules. Science. 2003;302:249–55.CrossRefPubMedGoogle Scholar
  67. Tan L. Identification of disease biomarkers from brain fMRI data using machine learning techniques: applications in sensorineural hearing loss and attention deficit hyperactivity disorder. University of Cincinnati. 2015.Google Scholar
  68. Vaisar T, Pennathur S, Green PS, Gharib SA, Hoofnagle AN, Cheung MC, Byun J, Vuletic S, Kassim S, Singh P. Shotgun proteomics implicates protease inhibition and complement activation in the antiinflammatory properties of HDL. The Journal of Clinical Investigation. 2007;117:746–56.CrossRefPubMedPubMedCentralGoogle Scholar
  69. Wagner A, Fell DA. The small world inside large metabolic networks. Proc Biol Sci. 2001;268:1803–10.CrossRefPubMedPubMedCentralGoogle Scholar
  70. Wang G-S, Cooper TA. Splicing in disease: disruption of the splicing code and the decoding machinery. Nat Rev Genet. 2007;8:749–61.CrossRefPubMedGoogle Scholar
  71. Watson AD, Berliner JA, Hama SY, La Du BN, Faull KF, Fogelman AM, Navab MOHAMAD. Protective effect of high density lipoprotein associated paraoxonase. Inhibition of the biological activity of minimally oxidized low density lipoprotein. Journal of Clinical Investigation. 1995;96:2882.CrossRefPubMedPubMedCentralGoogle Scholar
  72. Wu J, Vallenius T, Ovaska K, Westermarck J, Makela TP, Hautaniemi S. Integrated network analysis platform for protein-protein interactions. Nat Methods. 2009;6:75–7.CrossRefPubMedGoogle Scholar
  73. Xiong HY, Alipanahi B, Lee LJ, Bretschneider H, Merico D, Yuen RKC, Hua Y, Gueroussov S, Najafabadi HS, Hughes TR, Morris Q, Barash Y, Krainer AR, Jojic N, Scherer SW, Blencowe BJ, Frey BJ. The human splicing code reveals new insights into the genetic determinants of disease. Science. 2015;347:1254806.Google Scholar
  74. Yang X, Coulombe-Huntington J, Kang S, Sheynkman GM, Hao T, Richardson A, Sun S, Yang F, Shen YA, Murray RR, Spirohn K, Begg BE, Duran-Frigola M, MacWilliams A, Pevzner SJ, Zhong Q, Trigg SA, Tam S, Ghamsari L, Sahni N, Yi S, Rodriguez MD, Balcha D, Tan G, Costanzo M, Andrews B, Boone C, Zhou XJ, Salehi-Ashtiani K, Charloteaux B, Chen AA, Calderwood MA, Aloy P, Roth FP, Hill DE, Iakoucheva LM, Xia Y, Vidal M. Widespread expansion of protein interaction capabilities by alternative splicing. Cell. 2016;164:805–17.CrossRefPubMedGoogle Scholar
  75. Yip KY, Yu H, Kim PM, Schultz M, Gerstein M. The tYNA platform for comparative interactomics: a web tool for managing, comparing and mining multiple networks. Bioinformatics. 2006;22:2968–70.CrossRefPubMedGoogle Scholar
  76. Yu H, Kim PM, Sprecher E, Trifonov V, Gerstein M. The importance of bottlenecks in protein networks: correlation with gene essentiality and expression dynamics. PLoS Comput Biol. 2007;3:e59.CrossRefPubMedPubMedCentralGoogle Scholar
  77. Zhang M, Deng J, Fang C, Zhang X, Lu LJ. Molecular network analysis and applications. In: Alterovitz G, Ramoni M, editors. Knowledge-based bioinformatics: from analysis to interpretation. Chichester: Wiley; 2010.Google Scholar
  78. Zhang X, Joehanes R, Chen BH, Huan T, Ying S, Munson PJ, Johnson AD, Levy D, O’Donnell CJ. Identification of common genetic variants controlling transcript isoform variation in human whole blood. Nat Genet. 2015;47:345–52.CrossRefPubMedGoogle Scholar

Copyright information

© Springer Science+Business Media Singapore 2016

Authors and Affiliations

  • Hailong Li
    • 1
  • Zhaowei Ren
    • 1
    • 2
  • Sheng Ren
    • 1
    • 3
  • Xinyu Guo
    • 1
    • 2
  • Xiaoting Zhu
    • 1
    • 2
  • Long Jason Lu
    • 2
    • 4
    • 5
    • 1
  1. 1.Division of Biomedical InformaticsCincinnati Children’s Hospital Medical CenterCincinnatiUSA
  2. 2.Department of Electrical Engineering and Computing SystemsUniversity of CincinnatiCincinnatiUSA
  3. 3.Department of StatisticsUniversity of CincinnatiCincinnatiUSA
  4. 4.Department of Environmental HealthUniversity of CincinnatiCincinnatiUSA
  5. 5.Departments of Pediatrics and Biomedical Informatics, Division of Biomedical InformaticsCincinnati Children’s Hospital Research FoundationCincinnatiUSA

Personalised recommendations