Network-Guided Biomarker Discovery

  • Chloé-Agathe AzencottEmail author
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9605)


Identifying measurable genetic indicators (or biomarkers) of a specific condition of a biological system is a key element of precision medicine. Indeed it allows to tailor diagnostic, prognostic and treatment choice to individual characteristics of a patient. In machine learning terms, biomarker discovery can be framed as a feature selection problem on whole-genome data sets. However, classical feature selection methods are usually underpowered to process these data sets, which contain orders of magnitude more features than samples. This can be addressed by making the assumption that genetic features that are linked on a biological network are more likely to work jointly towards explaining the phenotype of interest. We review here three families of methods for feature selection that integrate prior knowledge in the form of networks.


Biological networks Structured sparsity Feature selection Biomarker discovery 


  1. 1.
    Spear, B.B., Heath-Chiozzi, M., Huff, J.: Clinical application of pharmacogenetics. Trends Mol. Med. 7(5), 201–204 (2001)CrossRefGoogle Scholar
  2. 2.
    Reuter, J., Spacek, D.V., Snyder, M.: High-throughput sequencing technologies. Molecular Cell 58(4), 586–597 (2015)CrossRefGoogle Scholar
  3. 3.
    Van Allen, E.M., Wagle, N., Levy, M.A.: Clinical analysis and interpretation of cancer genome data. J. Clin. Oncol. 31(15), 1825–1833 (2013)CrossRefGoogle Scholar
  4. 4.
    Manolio, T.A., Collins, F.S., Cox, N.J., Goldstein, D.B., et al.: Finding the missing heritability of complex diseases. Nature 461(7265), 747–753 (2009)CrossRefGoogle Scholar
  5. 5.
    Holzinger, A.: Interactive machine learning for health informatics: when do we need the human-in-the-loop? Brain Inf. 3(2), 119–131 (2016)CrossRefGoogle Scholar
  6. 6.
    Hund, M., Böhm, D., Sturm, W., Sedlmair, M., et al.: Visual analytics for concept exploration in subspaces of patient groups. Brain Inf. 3(4), 233–247 (2016). doi: 10.1007/s40708-016-0043-5 Google Scholar
  7. 7.
    Szklarczyk, D., Franceschini, A., Wyder, S., Forslund, K., et al.: STRING v10: protein-protein interaction networks, integrated over the tree of life. Nucleic Acids Res. 43(Database issue), D447–452 (2015)CrossRefGoogle Scholar
  8. 8.
    Chatr-Aryamontri, A., Breitkreutz, B.J., Oughtred, R., Boucher, L., Heinicke, S., et al.: The BioGRID interaction database: 2015 update. Nucleic Acids Res. 43(Database issue), D470–478 (2015)CrossRefGoogle Scholar
  9. 9.
    Kuperstein, I., Bonnet, E., Nguyen, H.A., Cohen, D., et al.: Atlas of cancer signalling network: a systems biology resource for integrative analysis of cancer data with Google Maps. Oncogenesis 4(7), e160 (2015)CrossRefGoogle Scholar
  10. 10.
    Azencott, C.A., Grimm, D., Sugiyama, M., Kawahara, Y., Borgwardt, K.M.: Efficient network-guided multi-locus association mapping with graph cuts. Bioinformatics 29(13), i171–i179 (2013)CrossRefGoogle Scholar
  11. 11.
    Guyon, I., Elisseeff, A.: An introduction to variable and feature selection. J. Mach. Learn Res. 3, 1157–1182 (2003)zbMATHGoogle Scholar
  12. 12.
    Hastie, T., Tibshirani, R., Wainwright, M.: Statistical Learning with Sparsity: The Lasso and Generalizations. CRC Press, Boca Raton (2015)zbMATHGoogle Scholar
  13. 13.
    Bush, W.S., Moore, J.H.: Chapter 11: genome-wide association studies. PLoS Comput. Biol. 8(12), e1002822 (2012)CrossRefGoogle Scholar
  14. 14.
    Merris, R.: Laplacian matrices of graphs: a survey. Linear Algebra Appl. 197, 143–176 (1994)MathSciNetCrossRefzbMATHGoogle Scholar
  15. 15.
    Smola, A.J., Kondor, R.: Kernels and regularization on graphs. In: Schölkopf, B., Warmuth, M.K. (eds.) COLT-Kernel 2003. LNCS (LNAI), vol. 2777, pp. 144–158. Springer, Heidelberg (2003). doi: 10.1007/978-3-540-45167-9_12 CrossRefGoogle Scholar
  16. 16.
    Fujishige, S.: Submodular Functions and Optimization. Elsevier, Amsterdam (2005)zbMATHGoogle Scholar
  17. 17.
    Bach, F.: Learning with submodular functions: a convex optimization perspective. Found. Trends Mach. Learn. 6(2–3), 145–373 (2013)CrossRefzbMATHGoogle Scholar
  18. 18.
    Thornton, T.: Statistical methods for genome-wide and sequencing association studies of complex traits in related samples. Curr. Protoc. Hum. Genet. 84, 1.28.1–1.28.9 (2015)CrossRefGoogle Scholar
  19. 19.
    Liu, J., Wang, K., Ma, S., Huang, J.: Accounting for linkage disequilibrium in genome-wide association studies: a penalized regression method. Statist. Interface 6(1), 99–115 (2013)MathSciNetCrossRefzbMATHGoogle Scholar
  20. 20.
    Lee, S., Abecasis, G., Boehnke, M., Lin, X.: Rare-variant association analysis: study designs and statistical tests. Am. J. Hum. Genet. 95(1), 5–23 (2014)CrossRefGoogle Scholar
  21. 21.
    Liu, J.Z., Mcrae, A.F., Nyholt, D.R., Medland, S.E., et al.: A versatile gene-based test for genome-wide association studies. Am. J. Hum. Genet. 87(1), 139–145 (2010)CrossRefGoogle Scholar
  22. 22.
    Jia, P., Wang, L., Fanous, A.H., Pato, C.N., Edwards, T.L., Zhao, Z.: The International Schizophrenia Consortium: network-assisted investigation of combined causal signals from Genome-Wide Association Studies in schizophrenia. PLoS Comput. Biol. 8(7), e1002587 (2012)CrossRefGoogle Scholar
  23. 23.
    Chuang, H.Y., Lee, E., Liu, Y.T., Lee, D., Ideker, T.: Network-based classification of breast cancer metastasis. Mol. Syst. Biol. 3, 140 (2007)CrossRefGoogle Scholar
  24. 24.
    Baranzini, S.E., Galwey, N.W., Wang, J., Khankhanian, P., et al.: Pathway and network-based analysis of genome-wide association studies in multiple sclerosis. Hum. Mol. Genet. 18(11), 2078–2090 (2009)CrossRefGoogle Scholar
  25. 25.
    Wang, L., Matsushita, T., Madireddy, L., Mousavi, P., Baranzini, S.E.: PINBPA: Cytoscape app for network analysis of GWAS data. Bioinformatics 31(2), 262–264 (2015)CrossRefGoogle Scholar
  26. 26.
    Ideker, T., Ozier, O., Schwikowski, B., Siegel, A.F.: Discovering regulatory and signalling circuits in molecular interaction networks. Bioinformatics 18(suppl 1), S233–S240 (2002)CrossRefGoogle Scholar
  27. 27.
    Taşan, M., Musso, G., Hao, T., Vidal, M., MacRae, C.A., Roth, F.P.: Selecting causal genes from genome-wide association studies via functionally coherent subnetworks. Nat. Methods 12(2), 154–159 (2015)Google Scholar
  28. 28.
    Mitra, K., Carvunis, A.R., Ramesh, S.K., Ideker, T.: Integrative approaches for finding modular structure in biological networks. Nat. Rev. Genet. 14(10), 719–732 (2013)CrossRefGoogle Scholar
  29. 29.
    Akula, N., Baranova, A., Seto, D., Solka, J., et al.: A network-based approach to prioritize results from genome-wide association studies. PLoS ONE 6(9), e24220 (2011)CrossRefGoogle Scholar
  30. 30.
    Marchini, J., Donnelly, P., Cardon, L.R.: Genome-wide strategies for detecting multiple loci that influence complex diseases. Nat. Genet. 37(4), 413–417 (2005)CrossRefGoogle Scholar
  31. 31.
    Tibshirani, R.: Regression shrinkage and selection via the lasso. J. Roy. Stat. Soc. B 58, 267–288 (1994)MathSciNetzbMATHGoogle Scholar
  32. 32.
    Wu, T.T., Chen, Y.F., Hastie, T., Sobel, E., Lange, K.: Genome-wide association analysis by lasso penalized logistic regression. Bioinformatics 25(6), 714–721 (2009)CrossRefGoogle Scholar
  33. 33.
    Zhou, H., Sehl, M.E., Sinsheimer, J.S., Lange, K.: Association screening of common and rare genetic variants by penalized regression. Bioinformatics 26(19), 2375–2382 (2010)CrossRefGoogle Scholar
  34. 34.
    Chen, L.S., Hutter, C.M., Potter, J.D., Liu, Y., Prentice, R.L., Peters, U., Hsu, L.: Insights into colon cancer etiology via a regularized approach to gene set analysis of GWAS data. Am. J. Hum. Genet. 86(6), 860–871 (2010)CrossRefGoogle Scholar
  35. 35.
    Zhao, J., Gupta, S., Seielstad, M., Liu, J., Thalamuthu, A.: Pathway-based analysis using reduced gene subsets in genome-wide association studies. BMC Bioinf. 12, 17 (2011)CrossRefGoogle Scholar
  36. 36.
    Silver, M., Montana, G.: Alzheimer’s disease neuroimaging initiative: fast identification of biological pathways associated with a quantitative trait using group lasso with overlaps. Stat. Appl. Genet. Mol. Biol. 11(1), 7 (2012)MathSciNetzbMATHGoogle Scholar
  37. 37.
    Huang, J., Zhang, T., Metaxas, D.: Learning with structured sparsity. J. Mach. Learn. Res. 12, 3371–3412 (2011)MathSciNetzbMATHGoogle Scholar
  38. 38.
    Micchelli, C.A., Morales, J.M., Pontil, M.: Regularizers for structured sparsity. Adv. Comput. Math. 38(3), 455–489 (2013)MathSciNetCrossRefzbMATHGoogle Scholar
  39. 39.
    Jacob, L., Obozinski, G., Vert, J.P.: Group lasso with overlap and graph lasso. In: Proceedings of the 26th Annual International Conference on Machine Learning, pp. 433–440. ACM (2009)Google Scholar
  40. 40.
    Tibshirani, R., Saunders, M., Rosset, S., Zhu, J., Knight, K.: Sparsity and smoothness via the fused lasso. J. Roy. Stat. Soc. B 67(1), 91–108 (2005)MathSciNetCrossRefzbMATHGoogle Scholar
  41. 41.
    Beck, A., Teboulle, M.: A fast iterative shrinkage-thresholding algorithm for linear inverse problems. SIAM J. Imag. Sci. 2(1), 183–202 (2009)MathSciNetCrossRefzbMATHGoogle Scholar
  42. 42.
    Xin, B., Kawahara, Y., Wang, Y., Gao, W.: Efficient generalized fused lasso and its application to the diagnosis of Alzheimer’s disease. In: Twenty-Eighth AAAI Conference on Artificial Intelligence (2014)Google Scholar
  43. 43.
    Li, C., Li, H.: Network-constrained regularization and variable selection for analysis of genomic data. Bioinformatics 24(9), 1175–1182 (2008)CrossRefGoogle Scholar
  44. 44.
    Li, C., Li, H.: Variable selection and regression analysis for graph-structured covariates with an application to genomics. Ann. Appl. Stat. 4(3), 1498–1516 (2010)MathSciNetCrossRefzbMATHGoogle Scholar
  45. 45.
    Sokolov, A., Carlin, D.E., Paull, E.O., Baertsch, R., Stuart, J.M.: Pathway-based genomics prediction using generalized elastic net. PLoS Comput. Biol. 12(3), e1004790 (2016)CrossRefGoogle Scholar
  46. 46.
    Friedman, J., Hastie, T., Höfling, H., Tibshirani, R.: Pathwise coordinate optimization. Ann. Appl. Stat. 1(2), 302–332 (2007)MathSciNetCrossRefzbMATHGoogle Scholar
  47. 47.
    Yang, S., Yuan, L., Lai, Y.C., Shen, X., et al.: Feature grouping and selection over an undirected graph. In: Proceedings of the 18th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 922–930. ACM (2012)Google Scholar
  48. 48.
    Gabay, D., Mercier, B.: A dual algorithm for the solution of nonlinear variational problems via finite element approximation. Comput. Math. Appl. 2(1), 17–40 (1976)CrossRefzbMATHGoogle Scholar
  49. 49.
    Boyd, S., Parikh, N., Chu, E., Peleato, B., Eckstein, J.: Distributed optimization and statistical learning via the alternating direction method of multipliers. Found. Trends Mach. Learn. 3(1), 1–122 (2011)CrossRefzbMATHGoogle Scholar
  50. 50.
    Wang, Z., Montana, G.: The graph-guided group lasso for genome-wide association studies. In: Regularization, Optimization, Kernels, and Support Vector Machines, pp. 131–157 (2014)Google Scholar
  51. 51.
    Dernoncourt, D., Hanczar, B., Zucker, J.D.: Analysis of feature selection stability on high dimension and small sample data. Comput. Stat. Data Anal. 71, 681–693 (2014)MathSciNetCrossRefGoogle Scholar
  52. 52.
    Haury, A.C., Gestraud, P., Vert, J.P.: The influence of feature selection methods on accuracy, stability and interpretability of molecular signatures. PLoS ONE 6(12), e28210 (2011)CrossRefGoogle Scholar
  53. 53.
    Kuncheva, L., Smith, C., Syed, Y., Phillips, C., Lewis, K.: Evaluation of feature ranking ensembles for high-dimensional biomedical data: a case study. In: 2012 IEEE 12th International Conference on Data Mining Workshops, pp. 49–56 (2012)Google Scholar
  54. 54.
    Bach, F.: Structured sparsity-inducing norms through submodular functions. In: 24th Annual Conference on Neural Information Processing Systems 2010 (2010)Google Scholar
  55. 55.
    Orlin, J.B.: A faster strongly polynomial time algorithm for submodular function minimization. Math. Prog. 118(2), 237–251 (2009)MathSciNetCrossRefzbMATHGoogle Scholar
  56. 56.
    Greig, D.M., Porteous, B.T., Seheult, A.H.: Exact maximum a posteriori estimation for binary images. J. Roy. Stat. Soc. B 51(2), 271–279 (1989)Google Scholar
  57. 57.
    Kolmogorov, V., Zabin, R.: What energy functions can be minimized via graph cuts? IEEE Trans. Pattern Anal. Mach. Intell. 26(2), 147–159 (2004)CrossRefGoogle Scholar
  58. 58.
    Wu, M.C., Lee, S., Cai, T., Li, Y., Boehnke, M., Lin, X.: Rare-variant association testing for sequencing data with the sequence kernel association test. Am. J. Hum. Genet. 89(1), 82–93 (2011)CrossRefGoogle Scholar
  59. 59.
    Kuncheva, L.I.: A stability index for feature selection. In: Proceedings of the 25th Conference on Proceedings of the 25th IASTED International Multi-Conference: Artificial Intelligence and Applications, pp. 390–395. ACTA Press (2007)Google Scholar
  60. 60.
    Park, S.H., Lee, J.Y., Kim, S.: A methodology for multivariate phenotype-based genome-wide association studies to mine pleiotropic genes. BMC Syst. Biol. 5(2), 1–14 (2011)Google Scholar
  61. 61.
    O’Reilly, P.F., Hoggart, C.J., Pomyen, Y., Calboli, F.C.F., Elliott, P., Jarvelin, M.R., Coin, L.J.M.: MultiPhen: joint model of multiple phenotypes can increase discovery in GWAS. PLoS ONE 7(5), e34861 (2012)CrossRefGoogle Scholar
  62. 62.
    Eduati, F., Mangravite, L.M., Wang, T., Tang, H., et al.: Prediction of human population responses to toxic compounds by a collaborative competition. Nat. Biotechnol. 33(9), 933–940 (2015)CrossRefGoogle Scholar
  63. 63.
    Cheng, W., Zhang, X., Guo, Z., Shi, Y., Wang, W.: Graph-regularized dual lasso for robust eQTL mapping. Bioinformatics 30(12), i139–i148 (2014)CrossRefGoogle Scholar
  64. 64.
    Obozinski, G., Taskar, B., Jordan, M.I.: Multi-task feature selection. Technical report, UC Berkeley (2006)Google Scholar
  65. 65.
    Sugiyama, M., Azencott, C., Grimm, D., Kawahara, Y., Borgwardt, K.: Multi-task feature selection on multiple networks via maximum flows. In: Proceedings of the 2014 SIAM International Conference on Data Mining, pp. 199–207 (2014)Google Scholar
  66. 66.
    Kim, S., Xing, E.P.: Statistical estimation of correlated genome associations to a quantitative trait network. PLoS Genet. 5(8), e1000587 (2009)CrossRefGoogle Scholar
  67. 67.
    Wang, Z., Curry, E., Montana, G.: Network-guided regression for detecting associations between DNA methylation and gene expression. Bioinformatics 30(19), 2693–2701 (2014)CrossRefGoogle Scholar
  68. 68.
    Fei, H., Huan, J.: Structured feature selection and task relationship inference for multi-task learning. Knowl. Inf. Syst. 35(2), 345–364 (2013)CrossRefGoogle Scholar
  69. 69.
    Swirszcz, G., Lozano, A.C.: Multi-level lasso for sparse multi-task regression. In: Proceedings of the 29th International Conference on Machine Learning (ICML 2012), pp. 361–368 (2012)Google Scholar
  70. 70.
    Bellon, V., Stoven, V., Azencott, C.A.: Multitask feature selection with task descriptors. In: Pacific Symposium on Biocomputing, vol. 21, pp. 261–272 (2016)Google Scholar
  71. 71.
    Ritchie, M.D., Hahn, L.W., Roodi, N., Bailey, L.R., et al.: Multifactor-dimensionality reduction reveals high-order interactions among estrogen-metabolism genes in sporadic breast cancer. Am. J. Hum. Genet. 69(1), 138–147 (2001)CrossRefGoogle Scholar
  72. 72.
    Larson, N.B., Jenkins, G.D., Larson, M.C., Sellers, T.A., Sellers, T.A., et al.: Kernel canonical correlation analysis for assessing genegene interactions and application to ovarian cancer. Eur. J. Hum. Genet. 22(1), 126–131 (2014)CrossRefGoogle Scholar
  73. 73.
    Williams, S.M., Ritchie, M.D., Phillips, J.A., Dawson, E., et al.: Multilocus analysis of hypertension: a hierarchical approach. Hum. Hered. 57(1), 28–38 (2004)CrossRefGoogle Scholar
  74. 74.
    Cho, Y.M., Ritchie, M.D., Moore, J.H., Park, J.Y., et al.: Multifactor-dimensionality reduction shows a two-locus interaction associated with type 2 diabetes mellitus. Diabetologia 47(3), 549–554 (2004)CrossRefGoogle Scholar
  75. 75.
    Niel, C., Sinoquet, C., Dina, C., Rocheleau, G.: A survey about methods dedicated to epistasis detection. J. Bioinf. Comput. Biol. 6, 285 (2015)Google Scholar
  76. 76.
    Yoshida, M., Koike, A.: SNPInterForest: a new method for detecting epistatic interactions. BMC Bioinf. 12(1), 469 (2011)CrossRefGoogle Scholar
  77. 77.
    Stephan, J., Stegle, O., Beyer, A.: A random forest approach to capture genetic effects in the presence of population structure. Nat. Commun. 6, 7432 (2015)CrossRefGoogle Scholar
  78. 78.
    Beam, A.L., Motsinger-Reif, A., Doyle, J.: Bayesian neural networks for detecting epistasis in genetic association studies. BMC Bioinf. 15(1), 368 (2014)CrossRefGoogle Scholar
  79. 79.
    Drouin, A., Giguère, S., Sagatovich, V., Déraspe, M., et al.: Learning interpretable models of phenotypes from whole genome sequences with the Set Covering Machine (2014). arXiv:1412.1074 [cs, q-bio, stat]
  80. 80.
    Marchand, M., Shawe-Taylor, J.: The set covering machine. J. Mach. Learn. Res. 3, 723–746 (2002)MathSciNetzbMATHGoogle Scholar
  81. 81.
    He, Z., Yu, W.: Stable feature selection for biomarker discovery. Comput. Biol. Chem. 34(4), 215–225 (2010)CrossRefGoogle Scholar
  82. 82.
    Ma, S., Huang, J., Moran, M.S.: Identification of genes associated with multiple cancers via integrative analysis. BMC Genom. 10, 535 (2009)CrossRefGoogle Scholar
  83. 83.
    Yu, L., Ding, C., Loscalzo, S.: Stable feature selection via dense feature groups. In: Proceedings of the 14th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 803–811. ACM (2008)Google Scholar
  84. 84.
    Meinshausen, N., Bühlmann, P.: Stability selection. J. Roy. Stat. Soc. B 72(4), 417–473 (2010)MathSciNetCrossRefGoogle Scholar
  85. 85.
    Shah, R.D., Samworth, R.J.: Variable selection with error control: another look at stability selection. J. Roy. Stat. Soc. B 75(1), 55–80 (2013)MathSciNetCrossRefGoogle Scholar
  86. 86.
    Han, Y., Yu, L.: A variance reduction framework for stable feature selection. Stat. Anal. Data Min. 5(5), 428–445 (2012)MathSciNetCrossRefGoogle Scholar
  87. 87.
    Llinares-López, F., Grimm, D.G., Bodenham, D.A., Gieraths, U., et al.: Genome-wide detection of intervals of genetic heterogeneity associated with complex traits. Bioinformatics 31(12), i240–i249 (2015)CrossRefGoogle Scholar
  88. 88.
    Belilovsky, E., Varoquaux, G., Blaschko, M.B.: Testing for differences in Gaussian graphical models: applications to brain connectivity. In: Lee, D.D., Luxburg, U.V., Guyon, I., Garnett, R. (eds.) Advances in Neural Information Processing Systems 29 (2016)Google Scholar
  89. 89.
    Tur, I., Roverato, A., Castelo, R.: Mapping eQTL networks with mixed graphical markov models. Genetics 198(4), 1377–1393 (2014)CrossRefGoogle Scholar
  90. 90.
    Sandhu, K., Li, G., Poh, H., Quek, Y., et al.: Large-scale functional organization of long-range chromatin interaction networks. Cell. Rep. 2(5), 1207–1219 (2012)CrossRefGoogle Scholar

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© Springer International Publishing AG 2016

Authors and Affiliations

  1. 1.CBIO-Centre for Computational BiologyMINES ParisTech, PSL-Research UniversityFontainebleauFrance
  2. 2.Institut CurieParis Cedex 05France
  3. 3.INSERM, U900Paris Cedex 05France

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