Skip to main content
Disciplines
Automotive Business IT + Informatics Construction + Real Estate Electrical Engineering + Electronics Energy + Sustainability Insurance + Risk Finance + Banking Management + Leadership Marketing + Sales Mechanical Engineering + Materials
Events
DE
EN
Books
Journals
Topic Page
Marketing
Start single access now
Access for companies
EXTENDED SEARCH
Log in
Top
Published in:
BeatLex: Summarizing and Forecasting Time Series with Patterns Read chapter Read first chapter

2017 | OriginalPaper | Chapter

ALADIN: A New Approach for Drug–Target Interaction Prediction

Authors : Krisztian Buza, Ladislav Peska

Published in: Machine Learning and Knowledge Discovery in Databases

Publisher: Springer International Publishing

Log in

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

search-config
loading …

Abstract

Due to its pharmaceutical applications, one of the most prominent machine learning challenges in bioinformatics is the prediction of drug–target interactions. State-of-the-art approaches are based on various techniques, such as matrix factorization, restricted Boltzmann machines, network-based inference and bipartite local models (BLM). In this paper, we extend BLM by the incorporation of a hubness-aware regression technique coupled with an enhanced representation of drugs and targets in a multi-modal similarity space. Additionally, we propose to build a projection-based ensemble. Our https://static-content.springer.com/image/chp%3A10.1007%2F978-3-319-71246-8_20/457129_1_En_20_IEq1_HTML.gif technique (ALADIN) is evaluated on publicly available real-world drug–target interaction datasets. The results show that our approach statistically significantly outperforms BLM-NII, a recent version of BLM, as well as NetLapRLS and WNN-GIP.
Code related to this chapter is available at: https://​github.​com/​lpeska/​ALADIN
Data related to this chapter are available at: https://​zenodo.​org/​record/​556337#.​WPiAzIVOIdV
Supplementary material is available at: http://​www.​biointelligence.​hu/​dti/​

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 Adaptive Skip-Train Structured Regression for Temporal Networks
next chapter Co-Regularised Support Vector Regression
Footnotes
3
In our initial experiments, we observed that increasing the number of base models results in asymptotically increasing performance. For example, we obtained AUPR of 0.835, 0.867 and 0.871 with 5, 25 and 100 base models on the Ion Channel dataset. We made similar observations on the other datasets both in terms of AUC and AUPR. Therefore, using \(N=25\) base models seems to be a fair compromise between runtime and prediction quality.
 
4
\(\beta =\beta _{drug}=\beta _{target}\) and \(\gamma =\gamma _{drug}=\gamma _{target}\).
 
8
These results are in accordance with our further observations: considering the input data of the local models, the skewness of the distribution of bad k-nearest neighbor occurrences (with \(k=3\)), which is often used to quantify the presence of bad hubs [33], is remarkably high, between 1.61 and 11.13.
 
Literature
1.
Barabási, A.L., Gulbahce, N., Loscalzo, J.: Network medicine: a network-based approach to human disease. Nat. Rev. Genet. 12(1), 56–68 (2011)CrossRef
2.
3.
Biau, G., Cérou, F., Guyader, A.: On the rate of convergence of the bagged nearest neighbor estimate. J. Mach. Learn. Res. 11, 687–712 (2010)MathSciNetMATH
4.
Bleakley, K., Yamanishi, Y.: Supervised prediction of drug-target interactions using bipartite local models. Bioinformatics 25(18), 2397–2403 (2009)CrossRef
5.
Bolgar, B., Antal, P.: Bayesian matrix factorization with non-random missing data using informative Gaussian process priors and soft evidences. J. Mach. Learn. Res. 52, 25–36 (2016)
6.
Buza, K., Nanopoulos, A., Nagy, G.: Nearest neighbor regression in the presence of bad hubs. Knowl.-Based Syst. 86, 250–260 (2015)CrossRef
7.
8.
Chen, X., Liu, M.X., Yan, G.Y.: Drug-target interaction prediction by random walk on the heterogeneous network. Mol. BioSyst. 8(7), 1970–1978 (2012)CrossRef
9.
Cheng, A.C., Coleman, R.G., Smyth, K.T., Cao, Q., Soulard, P., Caffrey, D.R., Salzberg, A.C., Huang, E.S.: Structure-based maximal affinity model predicts small-molecule druggability. Nat. Biotechnol. 25(1), 71–75 (2007)CrossRef
10.
Cheng, F., Liu, C., Jiang, J., Lu, W., Li, W., Liu, G., Zhou, W., Huang, J., Tang, Y.: Prediction of drug-target interactions and drug repositioning via network-based inference. PLoS Comput. Biol. 8(5), e1002503 (2012)CrossRef
11.
Davis, J., Santos Costa, V., Ray, S., Page, D.: An integrated approach to feature invention and model construction for drug activity prediction. In: Proceedings of the 24th International Conference on Machine Learning, pp. 217–224 (2007)
12.
Davis, M.I., Hunt, J.P., Herrgard, S., Ciceri, P., Wodicka, L.M., Pallares, G., Hocker, M., Treiber, D.K., Zarrinkar, P.P.: Comprehensive analysis of kinase inhibitor selectivity. Nat. Biotechnol. 29(11), 1046–1051 (2011)CrossRef
13.
Fayruzov, T., De Cock, M., Cornelis, C., Hoste, V.: Linguistic feature analysis for protein interaction extraction. BMC Bioinform. 10(1), 374 (2009)CrossRef
14.
Gönen, M.: Predicting drug-target interactions from chemical and genomic kernels using Bayesian matrix factorization. Bioinformatics 28(18), 2304–2310 (2012)CrossRef
15.
Halperin, I., Ma, B., Wolfson, H., Nussinov, R.: Principles of docking: an overview of search algorithms and a guide to scoring functions. Proteins: Struct. Func. Bioinform. 47(4), 409–443 (2002)CrossRef
16.
Hattori, M., Okuno, Y., Goto, S., Kanehisa, M.: Development of a chemical structure comparison method for integrated analysis of chemical and genomic information in the metabolic pathways. J. Am. Chem. Soc. 125(39), 11853–11865 (2003)CrossRef
17.
Morgan, S., Grootendorst, P., Lexchin, J., Cunningham, C., Greyson, D.: The cost of drug development: a systematic review. Health Policy 100(1), 4–17 (2011)CrossRef
18.
Hu, C., Jain, G., Zhang, P., Schmidt, C., Gomadam, P., Gorka, T.: Data-driven method based on particle swarm optimization and k-nearest neighbor regression for estimating capacity of lithium-ion battery. Appl. Energy 129, 49–55 (2014)CrossRef
19.
Jamali, A.A., Ferdousi, R., Razzaghi, S., Li, J., Safdari, R., Ebrahimie, E.: Drugminer: comparative analysis of machine learning algorithms for prediction of potential druggable proteins. Drug Discov. Today 21(5), 718–724 (2016)CrossRef
20.
21.
Keiser, M.J., Roth, B.L., Armbruster, B.N., Ernsberger, P., Irwin, J.J., Shoichet, B.K.: Relating protein pharmacology by ligand chemistry. Nat. Biotechnol. 25(2), 197–206 (2007)CrossRef
22.
van Laarhoven, T., Nabuurs, S.B., Marchiori, E.: Gaussian interaction profile kernels for predicting drug-target interaction. Bioinformatics 27(21), 3036–3043 (2011)CrossRef
23.
Mei, J.P., Kwoh, C.K., Yang, P., Li, X.L., Zheng, J.: Drug-target interaction prediction by learning from local information and neighbors. Bioinformatics 29(2), 238–245 (2013)CrossRef
24.
Pahikkala, T., Airola, A., Pietilä, S., Shakyawar, S., Szwajda, A., Tang, J., Aittokallio, T.: Toward more realistic drug-target interaction predictions. Briefings Bioinform. 16(2), 325–337 (2015)CrossRef
25.
Pérot, S., Regad, L., Reynès, C., Spérandio, O., Miteva, M.A., Villoutreix, B.O., Camproux, A.C.: Insights into an original pocket-ligand pair classification: a promising tool for ligand profile prediction. PloS One 8(6), e63730 (2013)CrossRef
26.
27.
Pilászy, I., Tikk, D.: Recommending new movies: even a few ratings are more valuable than metadata. In: 3rd ACM Conference on Recommender Systems, pp. 93–100 (2009)
28.
Plantevit, M., Charnois, T., Klema, J., Rigotti, C., Crémilleux, B.: Combining sequence and itemset mining to discover named entities in biomedical texts: a new type of pattern. Int. J. Data Min. Model. Manag. 1(2), 119–148 (2009)MATH
29.
Radovanović, M., Nanopoulos, A., Ivanović, M.: Hubs in space: popular nearest neighbors in high-dimensional data. J. Mach. Learn. Res. 11, 2487–2531 (2010)MathSciNetMATH
30.
Sönströd, C., Johansson, U., Norinder, U., Boström, H.: Comprehensible models for predicting molecular interaction with heart-regulating genes. In: 7th IEEE International Conference on Machine Learning and Applications, pp. 559–564 (2008)
31.
Stensbo-Smidt, K., Igel, C., Zirm, A., Pedersen, K.S.: Nearest neighbour regression outperforms model-based prediction of specific star formation rate. In: IEEE International Conference on Big Data, pp. 141–144 (2013)
32.
Stražar, M., Žitnik, M., Zupan, B., Ule, J., Curk, T.: Orthogonal matrix factorization enables integrative analysis of multiple RNA binding proteins. Bioinformatics 32(10), 1527–1535 (2016)CrossRef
33.
Tomašev, N., Buza, K., Marussy, K., Kis, P.B.: Hubness-aware classification, instance selection and feature construction: survey and extensions to time-series. In: Stańczyk, U., Jain, L.C. (eds.) Feature Selection for Data and Pattern Recognition. SCI, vol. 584, pp. 231–262. Springer, Heidelberg (2015). https://​doi.​org/​10.​1007/​978-3-662-45620-0_​11
34.
Ullrich, K., Kamp, M., Gärtner, T., Vogt, M., Wrobel, S.: Ligand-based virtual screening with co-regularised support vector regression. In: 16th IEEE International Conference on Data Mining Workshops, pp. 261–268 (2016)
35.
36.
van Laarhoven, T., Marchiori, E.: Predicting drug-target interactions for new drug compounds using a weighted nearest neighbor profile. PloS One 8(6), e66952 (2013)
37.
Wang, Y., Zeng, J.: Predicting drug-target interactions using restricted Boltzmann machines. Bioinformatics 29(13), i126–i134 (2013)CrossRef
38.
Xia, Z., Wu, L.Y., Zhou, X., Wong, S.T.: Semi-supervised drug-protein interaction prediction from heterogeneous biological spaces. BMC Syst. Biol. 4(Suppl 2), S6 (2010)CrossRef
39.
Yamanishi, Y., Araki, M., Gutteridge, A., Honda, W., Kanehisa, M.: Prediction of drug-target interaction networks from the integration of chemical and genomic spaces. Bioinformatics 24(13), i232–i240 (2008)CrossRef
40.
41.
Zheng, X., Ding, H., Mamitsuka, H., Zhu, S.: Collaborative matrix factorization with multiple similarities for predicting drug-target interactions. In: 19th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 1025–1033 (2013)
42.
Zhu, S., Okuno, Y., Tsujimoto, G., Mamitsuka, H.: A probabilistic model for mining implicit chemical compound-gene relations from literature. Bioinformatics 21(Suppl. 2), ii245–ii251 (2005)
Metadata
Title
ALADIN: A New Approach for Drug–Target Interaction Prediction
Authors
Krisztian Buza
Ladislav Peska
Copyright Year
2017
Book
Machine Learning and Knowledge Discovery in Databases

Print ISBN: 978-3-319-71245-1

Electronic ISBN: 978-3-319-71246-8

Copyright Year: 2017

DOI
https://doi.org/10.1007/978-3-319-71246-8_20

Premium Partner

    Image Credits