Top

Published in:

2018 | OriginalPaper | Chapter

ISaaC: Identifying Structural Relations in Biological Data with Copula-Based Kernel Dependency Measures

Authors : Hossam Al Meer, Raghvendra Mall, Ehsan Ullah, Nasreddine Megrez, Halima Bensmail

Published in: Bioinformatics and Biomedical Engineering

Publisher: Springer International Publishing

Activate our intelligent search to find suitable subject content or patents.

search-config

AI-assisted search

Off

Abstract

The goal of this paper is to develop a novel statistical framework for inferring dependence between distributions of variables in omics data. We propose the concept of building a dependence network using a copula-based kernel dependency measures to reconstruct the underlying association network between the distributions. ISaaC is utilized for reverse-engineering gene regulatory networks and is competitive with several state-of-the-art gene regulatory inferrence methods on DREAM3 and DREAM4 Challenge datasets. An open-source implementation of ISaaC is available at https://bitbucket.org/HossamAlmeer/isaac/.

Dont have a licence yet? Then find out more about our products and how to get one now:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

über 102.000 Bücher
über 537 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Finance + Banking
Management + Führung
Marketing + Vertrieb
Maschinenbau + Werkstoffe
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 390 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Maschinenbau + Werkstoffe

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 340 Zeitschriften

aus folgenden Fachgebieten:

Bauwesen + Immobilien
Business IT + Informatik
Finance + Banking
Management + Führung
Marketing + Vertrieb
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

inform now

previous chapter Effects of External Voltage in the Dynamics of Pancreatic β-Cells: Implications for the Treatment of Diabetes

next chapter Inspecting the Role of PI3K/AKT Signaling Pathway in Cancer Development Using an In Silico Modeling and Simulation Approach

Bach, F.R., Jordan, M.I.: Kernel independent component analysis. J. Mach. Learn. Res. 3(Jul), 1–48 (2002)MathSciNetMATH

Borgwardt, K.M., Gretton, A., Rasch, M.J., Kriegel, H.P., Schölkopf, B., Smola, A.J.: Integrating structured biological data by kernel maximum mean discrepancy. Bioinformatics 22(14), e49–e57 (2006)CrossRef

Bosq, D.: Contribution à la théorie de l’estimation fonctionnelle. Institut de statistique de l’Université de Paris, Paris (1971)MATH

Dedecker, J., Doukhan, P., Lang, G., José Rafael, L., Louhichi, S., Prieur, C.: The empirical process. In: Dedecker, J., Doukhan, P., Lang, G., José Rafael, L., Louhichi, S., Prieur, C. (eds.) Weak Dependence: With Examples and Applications, pp. 223–246. Springer, New York (2007). https://doi.org/10.1007/978-0-387-69952-3_10CrossRef

Evangelista, P.F., Embrechts, M.J., Szymanski, B.K.: Some properties of the gaussian kernel for one class learning. In: de Sá, J.M., Alexandre, L.A., Duch, W., Mandic, D. (eds.) ICANN 2007. LNCS, vol. 4668, pp. 269–278. Springer, Heidelberg (2007). https://doi.org/10.1007/978-3-540-74690-4_28CrossRef

Fortet, R., Mourier, E.: Convergence de la répartition empirique vers la répartition théorique. Annales scientifiques de l’École Normale Supérieure 70(3), 267–285 (1953)CrossRefMATH

Gretton, A., Borgwardt, K.M., Rasch, M., Schölkopf, B., Smola, A.J.: A kernel method for the two-sample-problem. In: Advances in Neural Information Processing Systems, pp. 513–520 (2007)

Gretton, A., Bousquet, O., Smola, A., Schölkopf, B.: Measuring statistical dependence with Hilbert-Schmidt norms. In: Jain, S., Simon, H.U., Tomita, E. (eds.) ALT 2005. LNCS (LNAI), vol. 3734, pp. 63–77. Springer, Heidelberg (2005). https://doi.org/10.1007/11564089_7CrossRef

Gretton, A., Herbrich, R., Smola, A.J.: The kernel mutual information. In: 2003 IEEE International Conference on Acoustics, Speech, and Signal Processing, Proceedings, ICASSP 2003, vol. 4, pp. IV-880. IEEE (2003)

10.

Hyvärinen, A., Karhunen, J., Oja, E.: Independent Component Analysis, vol. 46. Wiley, New Jersy (2004)

11.

Irrthum, A., Wehenkel, L., Geurts, P., et al.: Inferring regulatory networks from expression data using tree-based methods. PLoS ONE 5(9), e12776 (2010)CrossRef

12.

Karlebach, G., Shamir, R.: Modelling and analysis of gene regulatory networks. Nat. Rev. Mol. Cell Biol. 9(10), 770–780 (2008)CrossRef

13.

Krus, D.J., Blackman, H.S.: Test reliability and homogeneity from the perspective of the ordinal test theory. Appl. Measur. Educ. 1(1), 79–88 (1988)CrossRef

14.

Mall, R., Cerulo, L., Garofano, L., Frattini, V., Kunji, K., Bensmail, H., Sabedot, T.S., Noushmehr, H., Lasorella, A., Iavarone, A., Ceccarelli, M.: RGBM: regularized gradient boosting machines for identification of the transcriptional regulators of discrete glioma subtypes. Nucleic Acids Res. gky015 (2018). https://doi.org/10.1093/nar/gky015

15.

Mall, R., Jumutc, V., Langone, R., Suykens, J.A.: Representative subsets for big data learning using k-NN graphs. In: 2014 IEEE International Conference on Big Data (Big Data), pp. 37–42. IEEE (2014)

16.

Mall, R., Suykens, J.A.: Very sparse LSSVM reductions for large-scale data. IEEE Trans. Neural Netw. Learn. Syst. 26(5), 1086–1097 (2015)MathSciNetCrossRef

17.

Marbach, D., Costello, J.C., Küffner, R., Vega, N.M., Prill, R.J., Camacho, D.M., Allison, K.R., Kellis, M., Collins, J.J., Stolovitzky, G., et al.: Wisdom of crowds for robust gene network inference. Nat. Methods 9(8), 796–804 (2012)CrossRef

18.

Marbach, D., Prill, R.J., Schaffter, T., Mattiussi, C., Floreano, D., Stolovitzky, G.: Revealing strengths and weaknesses of methods for gene network inference. Proc. Nat. Acad. Sci. 107(14), 6286–6291 (2010)CrossRef

19.

Marbach, D., Schaffter, T., Mattiussi, C., Floreano, D.: Generating realistic in silico gene networks for performance assessment of reverse engineering methods. J. Comput. Biol. 16(2), 229–239 (2009)CrossRef

20.

Margolin, A.A., Nemenman, I., Basso, K., Wiggins, C., Stolovitzky, G., Dalla Favera, R., Califano, A.: Aracne: an algorithm for the reconstruction of gene regulatory networks in a mammalian cellular context. BMC Bioinform. 7(1), S7 (2006)CrossRef

21.

Nelsen, R.B.: An Introduction to Copulas. Springer, Heidelberg (2007). https://doi.org/10.1007/0-387-28678-0MATH

22.

Pál, D., Póczos, B., Szepesvári, C.: Estimation of rényi entropy and mutual information based on generalized nearest-neighbor graphs. In: Advances in Neural Information Processing Systems, pp. 1849–1857 (2010)

23.

Petralia, F., Wang, P., Yang, J., Tu, Z.: Integrative random forest for gene regulatory network inference. Bioinformatics 31(12), i197–i205 (2015)CrossRef

24.

Pinna, A., Soranzo, N., De La Fuente, A.: From knockouts to networks: establishing direct cause-effect relationships through graph analysis. PLoS ONE 5(10), e12912 (2010)CrossRef

25.

Plaisier, C.L., O’Brien, S., Bernard, B., Reynolds, S., Simon, Z., Toledo, C.M., Ding, Y., Reiss, D.J., Paddison, P.J., Baliga, N.S.: Causal mechanistic regulatory network for glioblastoma deciphered using systems genetics network analysis. Cell Syst. 3(2), 172–186 (2016)CrossRef

26.

Póczos, B., Ghahramani, Z., Schneider, J.: Copula-based kernel dependency measures. arXiv preprint arXiv:1206.4682 (2012)

27.

Prill, R.J., Marbach, D., Saez-Rodriguez, J., Sorger, P.K., Alexopoulos, L.G., Xue, X., Clarke, N.D., Altan-Bonnet, G., Stolovitzky, G.: Towards a rigorous assessment of systems biology models: the DREAM3 challenges. PLoS ONE 5(2), e9202 (2010)CrossRef

28.

Rényi, A., et al.: On measures of entropy and information. In: Proceedings of the Fourth Berkeley Symposium on Mathematical Statistics and Probability, vol. 1, pp. 547–561 (1961)

29.

Sarmanov, O.: The maximum correlation coefficient (symmetrical case). Dokl. Akad. Nauk SSSR 120(4), 715–718 (1958)MathSciNetMATH

30.

Schaffter, T., Marbach, D., Floreano, D.: Genenetweaver: in silico benchmark generation and performance profiling of network inference methods. Bioinformatics 27(16), 2263–2270 (2011)CrossRef

31.

Schweizer, B., Wolff, E.F.: On nonparametric measures of dependence for random variables. Ann. Stat. 9(4), 879–885 (1981)MathSciNetCrossRefMATH

32.

Shannon, C.W., Weaver, W.: The Mathematical Theory of Communication. Press UoI, Urbana (1949)MATH

33.

Sławek, J., Arodź, T.: Ennet: inferring large gene regulatory networks from expression data using gradient boosting. BMC Syst. Biol. 7(1), 1 (2013)CrossRef

34.

van Someren, E., Wessels, L., Backer, E., Reinders, M.: Genetic network modeling. Pharmacogenomics 3(4), 507–525 (2002)CrossRefMATH

35.

Steinwart, I.: On the influence of the kernel on the consistency of support vector machines. J. Mach. Learn. Res. 2(Nov), 67–93 (2001)MathSciNetMATH

36.

Sun, X., Janzing, D., Schölkopf, B., Fukumizu, K.: A kernel-based causal learning algorithm. In: Proceedings of the 24th International Conference on Machine Learning, pp. 855–862. ACM (2007)

37.

Székely, G.J., Rizzo, M.L., Bakirov, N.K., et al.: Measuring and testing dependence by correlation of distances. Ann. Stat. 35(6), 2769–2794 (2007)MathSciNetCrossRefMATH

38.

Tsallis, C.: Possible generalization of Boltzmann-gibbs statistics. J. Stat. Phys. 52(1), 479–487 (1988)MathSciNetCrossRefMATH

39.

Weisstein, E.: Sklar’s theorem. Retrieved 4, 15 (2011)

40.

Yip, K.Y., Alexander, R.P., Yan, K.K., Gerstein, M.: Improved reconstruction of in silico gene regulatory networks by integrating knockout and perturbation data. PLoS ONE 5(1), e8121 (2010)CrossRef

Title: ISaaC: Identifying Structural Relations in Biological Data with Copula-Based Kernel Dependency Measures
Authors: Hossam Al Meer
Raghvendra Mall
Ehsan Ullah
Nasreddine Megrez
Halima Bensmail
Publisher: Springer International Publishing
Book: Bioinformatics and Biomedical Engineering
Print ISBN: 978-3-319-78722-0

Electronic ISBN: 978-3-319-78723-7

Copyright Year: 2018
DOI: https://doi.org/10.1007/978-3-319-78723-7_6

Springer Professional

Abstract

Please log in to get access to your license.

Dont have a licence yet? Then find out more about our products and how to get one now:

Springer Professional "Wirtschaft+Technik"

Springer Professional "Technik"

Springer Professional "Wirtschaft"

Premium Partner