Skip to main content

Introduction

  • Chapter
  • First Online:
  • 2110 Accesses

Part of the book series: Translational Bioinformatics ((TRBIO,volume 1))

Abstract

This chapter presents an overview of the current genomic field, introduces the history of using machine learning for predicative disease studies and provides highlights for all nine chapters which have been collected in this book. The authors also list the critical concepts illustrated by the authors and point out logical connections between different chapters.

This is a preview of subscription content, log in via an institution.

Buying options

Chapter
USD   29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD   129.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD   169.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD   169.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Learn about institutional subscriptions

References

  • Bureau A, Dupuis J, Falls K, et al. Identifying SNPs predictive of phenotype using random forests. Genet Epidemiol. 2005;28:171–82.

    Article  PubMed  Google Scholar 

  • Collins A, Morton NE. Mapping a disease locus by allelic association. Proc Natl Acad Sci USA. 1998;95:1741–45.

    Article  PubMed  CAS  Google Scholar 

  • Cortes C, Vapnik V. Support vector networks. Mach Learn. 1995;20:273–97.

    Google Scholar 

  • Cruz JA, Wishart DS. Applications of machine learning in cancer prediction and prognosis. Cancer Inform. 2006;2:59–77.

    Google Scholar 

  • Feng T, Elston RC, Zhu XF. Detecting rare and common variants for complex traits: sibpair and odds ratio weighted sum statistics (SPWSS, ORWSS). Genet Epi. 2011;35:398–409.

    Article  Google Scholar 

  • Fernald GH, Capriotti E, Daneshjou R, Karczewski KJ, Altman RB. Bioinformatics challenges for personalized medicine. Bioinformatics. 2011;27(13):1741–8.

    Article  PubMed  CAS  Google Scholar 

  • Guo W, Shugart YY. Detecting rare variants for quantitative traits using nuclear families. Hum Hered. 2012;73:148–58.

    Article  PubMed  Google Scholar 

  • Han F, Pan W. A data-adaptive sum test for disease association with multiple common or rare variants. Hum Hered 2010;70:42–54.

    Article  PubMed  Google Scholar 

  • Jiao Y, Chen R, Ke X, Cheng L, Chu K, Lu Z, Herskovits EH. Single nucleotide polymorphisms predict symptom severity of autism spectrum disorder. J Autism Dev Disord. 2012;42(6):971–83.

    Article  PubMed  Google Scholar 

  • Lin DY, Tang ZZ. A general framework for detecting disease associations with rare variants in sequencing studies. Am J Hum Genet. 2011;89:354–67.

    Article  PubMed  CAS  Google Scholar 

  • Listgarten J, Damaraju S, Poulin B, et al. Predictive models for breast cancer susceptibility from single nucleotide polymorphisms. Clin Cancer Res. 2004;10:2725–37.

    Article  PubMed  CAS  Google Scholar 

  • Moore JH, Asselbergs FW, William SM. Bioinformatics challenges for genome-wide association studies. Bioinformatics. 2010;26(4):445–56.

    Article  PubMed  CAS  Google Scholar 

  • Moore JH, Williams SM. Epistasis and its implications for personal genetics. Am J Hum Genet. 2009;85:309–20.

    Article  PubMed  CAS  Google Scholar 

  • Motsinger-Reif A, Dudek SM, Hahn LW, et al. Comparison of approaches for machine-learning optimization of neural networks for detecting gene-gene interactions in genetic epidemiology. Genet Epidemiol. 2008;32:325–40.

    Article  PubMed  Google Scholar 

  • Price AL, Kryukov GV, de Bakker PI, et al. Pooled association tests for rare variants in exon-resequencing studies. Am J Hum Genet. 2010;86:832–8.

    Article  PubMed  Google Scholar 

  • Ritchie MD, Hahn LW, Roodi N, et al. Multifactor-dimensionality reduction reveals high-order interactions among estrogen-metabolism genes in sporadic breast cancer. Am J Hum Genet. 2001;69:138–47.

    Article  PubMed  CAS  Google Scholar 

  • Schulte JH, Schowe B, Mestdagh P, Kaderali L, Kalaghatgi P, Schlierf S, Vermeulen J, Brockmeyer B, Pajtler K, Thor T, de Preter K, Speleman F, Morik K, Eggert A, Vandesompele J, Schramm A. Accurate prediction of neuroblastoma outcome based on miRNA expression profiles. Int J Cancer. 2010;127(10):2374–85.

    Article  PubMed  CAS  Google Scholar 

  • Somorjai RL, Nikulin A. The curse of small sample sizes in medical diagnosis via MR spectroscopy. In: Proceedings of the Society for Magnetic Resonance in Medicine. Twelfth annual scientific meeting, New York; 1993. pp. 685.

    Google Scholar 

  • Somorjai RL, Dolenko B, Baumgartner R. Class prediction and discovery using gene microarray and proteomics mass spectroscopy data: curses, caveats, cautions. Bioinformatics. 2003;19:1484–91.

    Article  PubMed  CAS  Google Scholar 

  • Szymczak S, Biernacka JM, Cordell HJ, González-Recio O, König IR, Zhang H, Sun YV. Machine learning in genome-wide association studies. Genet Epidemiol. 2009;33:S51–7.

    Article  PubMed  Google Scholar 

  • Upstll-Goddard R, Eccles D, Fliege J, Collins A. Machine learning approaches for the discovery of gene-gene interactions in disease data. Brief Bioinf. 2012; doi:10.1093.

    Google Scholar 

  • Wan XB, Zhao Y, Fan XJ, Cai HM, Zhang Y, Chen MY, Xu J, Wu XY, Li HB, Zeng YX, Hong MH, Liu QT. Molecular prognostic prediction for locally advanced nasopharyngeal carcinoma by support vector machine integrated approach. PLoS One. 2012;7(3):e31989.

    Article  PubMed  CAS  Google Scholar 

  • Wang HY, Sun BY, Zhu ZH, Chang ET, To KF, Hwang JSG, et al. Eight-signature classifier for prediction of nasopharyngeal carcinoma survival. J Clin Oncol. 2012;29(34):4516–24.

    Article  Google Scholar 

  • Wu MC, Lee S, Cai T, et al. Rare variant association testing for sequencing data using the sequence kernel association test (SKAT). Am J Hum Genet. 2011;89:82–93.

    Article  PubMed  CAS  Google Scholar 

  • Yokoyama S, Woods SL, Boyle GM, et al. A novel recurrent mutation in MITF predisposes to familial and sporadic melanoma. Nature. 2011;480:99–103.

    Article  PubMed  CAS  Google Scholar 

  • Yu W, Valdez R, Gwinn M, Khoury MJ. Application of support vector machine modeling for prediction of common diseases: the case of diabetes and pre-diabetes. BMC Med Inform Decis Mak. 2010;10:16.

    Article  PubMed  Google Scholar 

  • Zeggini E, Scott LJ, Saxena R, Voight BF, Marchini JL, Hu T, et al. Meta-analysis of genome-wide association data and large-scale replication identifies additional susceptibility loci for type 2 diabetes. Nat Genet. 2008;40(5):638–45.

    Article  PubMed  CAS  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Yin Yao Shugart .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2012 Springer Science+Business Media Dordrecht

About this chapter

Cite this chapter

Shugart, Y.Y., Collins, A. (2012). Introduction. In: Shugart, Y. (eds) Applied Computational Genomics. Translational Bioinformatics, vol 1. Springer, Dordrecht. https://doi.org/10.1007/978-94-007-5558-1_1

Download citation

Publish with us

Policies and ethics