Abstract
In this work, we used an adaptive feature-selection and function approximation model, called, flexible neural tree (FNT) for predicting Poly (lactic-co-glycolic acid) (PLGA) micro- and nanoparticle’s dissolution-rates that bears significant role in the pharmaceutical, medical, and drug manufacturing industries. Several factor influences PLGA nanoparticles dissolution-rate prediction. FNT model enable us to deal with feature-selection and prediction simultaneously. However, a single FNT model may or may not offer a generalized solution. Hence, to build a generalized model, we used an ensemble of FNTs. In this work, we have provided a comprehensive study for examining the most significant (influencing) features that influences dissolution rate prediction.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Astete, C.E., Sabliov, C.M.: Synthesis and characterization of PLGA nanoparticles. J. Biomater. Sci. Polym. Ed. 17(3), 247–289 (2006)
Szlęk, J., Paclawski, A., Lau, R., Jachowicz, R., Mendyk, A.: Heuristic modeling of macromolecule release from PLGA microspheres. Int. J. Nanomed. 8(1), 4601–4611 (2013)
van der Maaten, L.J, Postma, E.O, van den Herik, H.J.: Dimensionality reduction: a comparative review. Technical report TiCC TR 2009-005
Langer, R., Tirrell, D.A.: Designing materials for biology and medicine. Nature 428, 487–492 (2004)
Brodbeck, K.J., DesNoyer, J.R., McHugh, A.J.: Phase inversion dynamics of PLGA solutions related to drug delivery. Part II. The role of solution thermodynamics and bath-side mass transfer. J. Controlled Release 62(3), 333–344 (1999)
Makadia, H.K., Siegel, S.J.: Poly lactic-co-glycolic acid (PLGA) as biodegradable controlled drug delivery carrier. Polymers (Basel) 3(3), 1377–1397 (2011)
Fredenberg, S., Wahlgren, M., Reslow, M., Axelsson, A.: The mechanisms of drug release in poly (lactic-co-glycolic acid)-based drug delivery systems: a review. Int. J. Pharm. 415(1–2), 34–52 (2011)
Kang, J., Schwendeman, S.P.: Pore closing and opening in biodegradable polymers and their effect on the controlled release of proteins. Mol. Pharm. 4(1), 104–118 (2007)
Kang, J., Lambert, O., Ausborn, M., Schwendeman, S.P.: Stability of proteins encapsulated in injectable and biodegradable poly(lactide-co-glycolide)-glucose millicylinders. Int. J. Pharm. 357(1), 235–243 (2008)
Blanco, M.D., Alonso, M.J.: Development and characterization of protein loaded poly(lactide-co-glycolide) nanospheres. Eur. J. Pharm. Biopharm. 43(3), 287–294 (1997)
Mainardes, R.M., Evangelista, R.C.: PLGA nanoparticles containing praziquantel: effect of formulation variables on size distribution. Int. J. Pharm. 290(1–2), 137–144 (2005)
Zygourakis, K., Markenscoff, P.A.: Computer-aided design of bioerodible devices with optimal release characteristics: a cellular automata approach. Biomaterials 17(2), 125–135 (1996)
Gopferich, A.: Mechanisms of polymer degradation and erosion. Biomaterials 17(2), 103–114 (1996)
Siepmann, J., Faisant, N., Benoit, J.P.: A new mathematical model quantifying drug release from bioerodible microparticles using Monte Carlo simulations. Pharm. Res. 19(12), 1885–1893 (2002)
Ojha, V.K., Jackowski, K., Abraham, A., Snášel, V.: Dimensionality reduction, and function approximation of poly(lactic-co-glycolic acid) micro- and nanoparticle dissolution rate. Int. J. Nanomed. 10, 1119–1129 (2015)
Haykin, S.: Neural Networks: A Comprehensive Foundation, 1st edn. Prentice Hall PRT, Upper Saddle River (1994)
Goldberg, D.E.: Genetic Algorithms in Search, Optimization, and Machine Learning. Addison-Wesley, Boston (1989)
Yao, X.: Evolving artificial neural networks. Proc. IEEE 87(9), 1423–1447 (1999)
Chen, Y., Yang, B., Dong, J., Abraham, A.: Time-series forecasting using flexible neural tree model. Inform. Sci. 174(3), 219–235 (2005)
Goldberg, D.E., Holland, J.H.: Genetic algorithms and machine learning. Mach. Learn. 3(2), 95–99 (1988)
Kennedy, J.: Particle swarm optimization. In: Encyclopedia of Machine Learning, pp. 760–766. Springer, US (2010)
Dietterich, T.G.: Ensemble methods in machine learning. In: Multiple Classifier systems, pp. 1−15. Springer, Berlin (2000)
Mendes-Moreira, J., Soares, C., Jorge, A.M., et al.: Ensemble approaches for regression: a survey. ACM Comput. Surv. (CSUR) 45(1), 10 (2012)
Polikar, R.: Ensemble based systems in decision making. IEEE Circuits Syst. Mag. 6(3), 21–45 (2006)
Breiman, L.: Bagging predictors. Mach. Learn. 24(2), 123–140 (1996)
Quinlan, J.R.: Simplifying decision trees. Int. J. Man Mach. Stud. 27(3), 221–234 (1987)
Rasmussen, C.E., Williams, C.K.: Gaussian Processes for Machine Learning (Adaptive Computation and Machine Learning). MIT Press, Cambridge (2005)
Acknowledgment
This work was supported by the IPROCOM Marie Curie Initial Training Network, funded through the People Programme (Marie Curie Actions) of the European Union’s Seventh Framework Programme FP7/2007–2013/, under REA grant agreement number 316555.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2016 Springer International Publishing Switzerland
About this paper
Cite this paper
Ojha, V.K., Abraham, A., Snasel, V. (2016). Ensemble of Heterogeneous Flexible Neural Tree for the Approximation and Feature-Selection of Poly (Lactic-co-glycolic Acid) Micro- and Nanoparticle. In: Abraham, A., Wegrzyn-Wolska, K., Hassanien, A., Snasel, V., Alimi, A. (eds) Proceedings of the Second International Afro-European Conference for Industrial Advancement AECIA 2015. Advances in Intelligent Systems and Computing, vol 427. Springer, Cham. https://doi.org/10.1007/978-3-319-29504-6_16
Download citation
DOI: https://doi.org/10.1007/978-3-319-29504-6_16
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-29503-9
Online ISBN: 978-3-319-29504-6
eBook Packages: EngineeringEngineering (R0)