02302nas a2200253   4500008004100000022001400041245009500055210006900150260011100219300001400330490000700344520138000351653001801731653002901749653002401778653000801802653000801810100001501818700001601833700003301849700001801882700002101900856012701921       2014                        eng d  a1570-180800aPharmacokinetic modeling of caco-2 cell permeability using genetic programming (GP) method0 aPharmacokinetic modeling of caco2 cell permeability using geneti  aEXECUTIVE STE Y-2, PO BOX 7917, SAIF ZONE, 1200 BR SHARJAH, U ARAB EMIRATESbBENTHAM SCIENCE PUBL LTDcNOV  a1112-11180 v113 a<p>An accurate prediction of the pharmacokinetic properties of orally administered drugs is of paramount importance in pharmaceutical industry. Caco-2 cell permeability is a well established parameter for assessing the drug absorption profiles of lead molecules. Due to the restrictions on animal testing, prohibitive in situ models and ethical issues, the development of predictive models is essential. Genetic programming (GP) is an artificial intelligence (AI)-based exclusively data driven modeling paradigm. Given an example input-output data, it searches and optimizes, both the structure and parameters of a well fitting linear/non-linear input-output model. Despite this novelty, GP has not been widely exploited in drug design. Accordingly, in this study we propose a GP based approach for the in silico prediction of Caco-2 cell permeability using a diverse set of molecules. The predictions yielded a high magnitude for the training and test set correlation coefficient with low RMSE, indicating accurate Caco-2 permeability prediction and generalization performance by the GP model. The predictions were better or comparable to artificial neural networks (ANN) and support vector regression (SVR) methods. The GP based modeling approach illustrated will find diverse applications in (QSAR, QSPR and QSTR) modeling for the virtual screening of large libraries.</p>10aADME modeling10aCaco-2 cell permeability10agenetic programming10aMLP10aSVR1 aVyas, Renu1 aGoel, Purva1 aKarthikeyan, Muthukumarasamy1 aTambe, S., S.1 aKulkarni, B., D.  uhttps://library.ncl.res.in/content/pharmacokinetic-modeling-caco-2-cell-permeability-using-genetic-programming-gp-method-003061nas a2200265   4500008004100000022001400041245012400055210006900179260007300248300001100321490000900332520205300341653002802394653002302422653002402445653002702469653002602496653005102522100001602573700001802589700001502607700001802622700002302640856013202663       2015                        eng d  a0021-967300aGenetic programming based quantitative structure-retention relationships for the prediction of Kovats retention indices0 aGenetic programming based quantitative structureretention relati  aPO BOX 211, 1000 AE AMSTERDAM, NETHERLANDSbELSEVIER SCIENCE BVcNOV  a98-1090 v14203 a<p>The development of quantitative structure-retention relationships (QSRR) aims at constructing an appropriate linear/nonlinear model for the prediction of the retention behavior (such as Kovats retention index) of a solute on a chromatographic column. Commonly, multi-linear regression and artificial neural networks are used in the QSRR development in the gas chromatography (GC). In this study, an artificial intelligence based data-driven modeling formalism, namely genetic programming (GP), has been introduced for the development of quantitative structure based models predicting Kovats retention indices (KRI). The novelty of the GP formalism is that given an example dataset, it searches and optimizes both the form (structure) and the parameters of an appropriate linear/nonlinear data-fitting model. Thus, it is not necessary to pre-specify the form of the data-fitting model in the GP-based modeling. These models are also less complex, simple to understand, and easy to deploy. The effectiveness of GP in constructing QSRRs has been demonstrated by developing models predicting KRIs of light hydrocarbons (case study-I) and adamantane derivatives (case study-II). In each case study, two-, three- and four-descriptor models have been developed using the KRI data available in the literature. The results of these studies clearly indicate that the GP-based models possess an excellent KRI prediction accuracy and generalization capability. Specifically, the best performing four-descriptor models in both the case studies have yielded high (&gt;0.9) values of the coefficient of determination (R-2) and low values of root mean squared error (RMSE) and mean absolute percent error (MAPE) for training, test and validation set data. The characteristic feature of this study is that it introduces a practical and an effective GP-based method for developing QSRRs in gas chromatography that can be gainfully utilized for developing other types of data-driven models in chromatography science. (C) 2015 Elsevier B.V. All rights reserved.</p>10aArtificial intelligence10aGas chromatography10agenetic programming10aKovats retention index10aMolecular descriptors10aQuantitative structure-retention relationships1 aGoel, Purva1 aBapat, Sanket1 aVyas, Renu1 aTambe, Amruta1 aTambe, Sanjeev, S.  uhttps://library.ncl.res.in/content/genetic-programming-based-quantitative-structure-retention-relationships-prediction-kovats-002333nas a2200229   4500008004100000020002200041245010100063210006900164260013600233300001200369520139300381653001801774653002401792653002301816653002701839653002501866100002001891700001601911700002401927700002301951856012901974       2016                        eng d  a978-1-4673-7993-900aUse genetic programming for selecting predictor variables and modeling in process identification0 aUse genetic programming for selecting predictor variables and mo  a345 E 47th ST, New York, NY 10017 USAbIEEE Control Syst Soc; Honeywell; Mathworks; ABB R & D Div; GE; Mahindra Ecole CentralecJAN  a230-2373 a<p>Availability of an accurate and robust dynamic model is essential for implementing the model dependent process control. When first principles based modeling becomes difficult, tedious and/or costly, a dynamic model in the black-box form is obtained (process identification) by using the measured input-output process data. Such a dynamic model frequently contains a number of time delayed inputs and outputs as predictor variables. The determination of the specific predictor variables is usually done via a trial and error approach that requires an extensive computational effort. The computational intelligence (CI) based data-driven modeling technique, namely, genetic programming (GP) can search and optimize both the structure and parameters of a linear/nonlinear dynamic process model. It is also capable of choosing those predictor variables that significantly influence the model output. Thus usage of GP for process identification helps in avoiding the extensive time and efforts involved in the selection of the time delayed input-output variables. This advantageous GP feature has been illustrated in this study by conducting process identification of two chemical engineering systems. The results of the GP-based identification when compared with those obtained using the transfer function based identification clearly indicates the outperformance by the former method.</p>10adynamic model10agenetic programming10apredictor variable10aprocess identification10aSensitivity analysis1 aVerma, Devendra1 aGoel, Purva1 aPatil-Shinde, Veena1 aTambe, Sanjeev, S.  uhttps://library.ncl.res.in/content/use-genetic-programming-selecting-predictor-variables-and-modeling-process-identification02420nas a2200169   4500008004100000245015100041210006900192260000800261300001200269490000700281520175000288100001602038700001902054700002402073700002302097856013002120       2017                        eng d00aPrediction of degrees API values of crude oils by use of saturates/aromatics/resins/ asphaltenes analysis: computational-intelligence-based models0 aPrediction of degrees API values of crude oils by use of saturat  cJUN  a817-8530 v223 aThe degrees API value is an important physicochemical characteristic of crude oils often used in determining their properties and quality. There exist models-predominantly linear ones-for predicting the degrees API magnitude from the molecular composition of a crude oil. This approach is tedious and time-consuming because it requires quantitative determination of numerous crude-oil components. Usually, the hydrocarbons present in a crude oil are grouped according to their molecular average structures into saturates, aromatics, resins, and asphaltenes (SARA) fractions. An degrees API-value prediction model dependent on these four fractions is relatively easier to develop, although this approach has been rarely used. A rigorous scrutiny suggests that some of the dependencies between the individual SARA fractions and the corresponding degrees API value could be nonlinear. Accordingly, in this study, SARA-fraction-based nonlinear models have been developed for the prediction of values using three computational-intelligence (CI) formalisms: genetic programming (GP), artificialneural networks (ANNs), and support-vector regression (SVR). The SARA analyses and degrees API values of 403 crude-oil samples covering wide ranges have been used in developing these models. A comparison of the CI-based models with an existing linear model indicates that all the former class of models possess a significantly better degrees API-value prediction and generalization performance than those exhibited by the linear model. Also, the SVR-based model has been found to be the most accurate degrees API-value predictor. Because of their better prediction accuracy, CI-based models can be gainfully used to predict degrees API values of crude oils.1 aGoel, Purva1 aSaurabh, Kumar1 aPatil-Shinde, Veena1 aTambe, Sanjeev, S.  uhttps://library.ncl.res.in/content/prediction-degrees-api-values-crude-oils-use-saturatesaromaticsresins-asphaltenes-analysis02246nas a2200289   4500008004100000022001400041245013800055210006900193260000800262300001000270490000700280520126700287653001901554653001101573653001201584653002401596653002101620653003301641653002401674100001501698700001801713700001601731700003301747700002301780700002601803856012701829       2018                        eng d  a1545-596300aApplication of genetic programming (GP) formalism for building disease predictive models from protein-protein interactions (PPI) data0 aApplication of genetic programming GP formalism for building dis  cFEB  a27-370 v153 a<p>Protein-protein interactions (PPIs) play a vital role in the biological processes involved in the cell functions and disease pathways. The experimental methods known to predict PPIs require tremendous efforts and the results are often hindered by the presence of a large number of false positives. Herein, we demonstrate the use of a new Genetic Programming (GP) based Symbolic Regression (SR) approach for predicting PPIs related to a disease. In this case study, a dataset consisting of 135 PPI complexes related to cancer was used to construct a generic PPI predicting model with good PPI prediction accuracy and generalization ability. A high correlation coefficient (CC) magnitude of 0.893, and low root mean square error (RMSE), and mean absolute percentage error (MAPE) values of 478.221 and 0.239, respectively, were achieved for both the training and test set outputs. To validate the discriminatory nature of the model, it was applied on a dataset of diabetes complexes where it yielded significantly low CC values. Thus, the GP model developed here serves a dual purpose: (a) a predictor of the binding energy of cancer related PPI complexes, and (b) a classifier for discriminating PPI complexes related to cancer from those of other diseases.</p>10aBinding energy10acancer10aDisease10agenetic programming10amachine learning10aprotein-protein interactions10asymbolic regression1 aVyas, Renu1 aBapat, Sanket1 aGoel, Purva1 aKarthikeyan, Muthukumarasamy1 aTambe, Sanjeev, S.1 aKulkarni, Bhaskar, D.  uhttps://library.ncl.res.in/content/application-genetic-programming-gp-formalism-building-disease-predictive-models-protein