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dc.contributor.authorLiu, Yong
dc.contributor.authorTang, Shaoxun
dc.contributor.authorFernández-Lozano, Carlos
dc.contributor.authorMunteanu, Cristian-Robert
dc.contributor.authorPazos, A.
dc.contributor.authorYu, Yi-zun
dc.contributor.authorTan, Zhiliang
dc.contributor.authorGonzález-Díaz, Humberto
dc.date.accessioned2017-04-03T08:29:53Z
dc.date.issued2016-11-09
dc.identifier.citationLiu Y, Tang S, Fernández-Lozano C, et al. Experimental study and random forest prediction model of microbiome cell surface hydrophobicity. Expert Syst Appl. 2017; 72:306-16es_ES
dc.identifier.issn0957-4174
dc.identifier.urihttp://hdl.handle.net/2183/18357
dc.description.abstract[Abstract] The cell surface hydrophobicity (CSH) is an assessable physicochemical property used to evaluate the microbial adhesion to the surface of biomaterials, which is an essential step in the microbial biofilm formation and pathogenesis. For the present in vitro fermentation experiment, the CSH of ruminal mixed microbes was considered, along with other data records of pH, ammonia-nitrogen concentration, and neutral detergent fibre digestibility, conditions of surface tension and specific surface area in two different time scales. A dataset of 170,707 perturbations of input variables, grouped into two blocks of data, was constructed. Next, Expected Measurement Moving Average – Machine Learning (EMMA-ML) models were developed in order to predict CSH after perturbations of all input variables. EMMA-ML is a Perturbation Theory method that combines the ideas of Expected Measurement, Box-Jenkins Operators/Moving Average, and Time Series Analysis. Seven regression methods have been tested: Multiple Linear regression, Generalized Linear Model with Stepwise Feature Selection, Partial Least Squares regression, Lasso regression, Elastic Net regression, Neural Networks regression, and Random Forests (RF). The best regression performance has been obtained with RF (EMMA-RF model) with an R-squared of 0.992. The model analysis has shown that CSH values were highly dependent on the in vitro fermentation parameters of detergent fibre digestibility, ammonia – nitrogen concentration, and the expected values of cell surface hydrophobicity in the first time scale.es_ES
dc.description.sponsorshipMinisterio de Economía y Competitividad; FJCI-2015- 26071
dc.description.sponsorshipMinisterio de Economía y Competitividad; UNLC08-1E-002
dc.description.sponsorshipMinisterio de Economía y Competitividad; UNLC13-13-3503
dc.description.sponsorshipGalicia. Consellería de Cultura, Educación e Ordenación Universitaria; GRC2014/049
dc.description.sponsorshipNational Natural Science Foundation of China; Grant No. 31172234
dc.description.sponsorshipNational Natural Science Foundation of China; Grant No. 31260556
dc.description.sponsorshipChinese Academy of Science; Grant No. XDA05020700
dc.language.isoenges_ES
dc.publisherElsevieres_ES
dc.relation.urihttp://dx.doi.org/10.1016/j.eswa.2016.10.058es_ES
dc.rightsAtribución-NoComercial-SinDerivadas 3.0 Españaes_ES
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/3.0/es/*
dc.subjectMachine learninges_ES
dc.subjectExpected valueses_ES
dc.subjectMoving averageses_ES
dc.subjectCell propertieses_ES
dc.subjectPerturbation theoryes_ES
dc.subjectTime series analysises_ES
dc.titleExperimental study and random forest prediction model of microbiome cell surface hydrophobicityes_ES
dc.typeinfo:eu-repo/semantics/articlees_ES
dc.rights.accessinfo:eu-repo/semantics/embargoedAccesses_ES
dc.date.embargoEndDate2018-11-09es_ES
dc.date.embargoLift2018-11-09
UDC.journalTitleExpert Systems with Applicationses_ES
UDC.volume72es_ES
UDC.startPage306es_ES
UDC.endPage316es_ES


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