dc.contributor.author | Chakrabarti, Swapan | |
dc.contributor.author | Svojanovsky, Stan R. | |
dc.contributor.author | Slavik, Romana | |
dc.contributor.author | Georg, Gunda I. | |
dc.contributor.author | Wilson, George S. | |
dc.contributor.author | Smith, Peter G. | |
dc.date.accessioned | 2017-03-17T19:52:11Z | |
dc.date.available | 2017-03-17T19:52:11Z | |
dc.date.issued | 2009-12-14 | |
dc.identifier.citation | Chakrabarti, Swapan, Stan R. Svojanovsky, Romana Slavik, Gunda I. Georg, George S. Wilson, and Peter G. Smith. "Artificial Neural Networkâ Based Analysis of High-Throughput Screening Data for Improved Prediction of Active Compounds." Journal of Biomolecular Screening 14.10 (2009): 1236-244. | en_US |
dc.identifier.uri | http://hdl.handle.net/1808/23437 | |
dc.description.abstract | Artificial Neural Networks (ANNs) are trained using High Throughput Screening (HTS) data to recover active compounds from a large data set. Improved classification performance was obtained on combining predictions made by multiple ANNs. The HTS data, acquired from a Methionine Aminopeptidases Inhibition study, consisted of a library of 43,347 compounds, and the ratio of active to non-active compounds, RA/N, was 0.0321. Back-propagation ANNs were trained and validated using Principal Components derived from the physico-chemical features of the compounds. On selecting the training parameters carefully, an ANN recovers one-third of all active compounds from the validation set with a three-fold gain in RA/N value. Further gains in RA/N values were obtained upon combining the predictions made by a number of ANNs. The generalization property of the back-propagation ANNs was utilized to train those ANNs with the same training samples, after being initialized with different sets of random weights. As a result, only 10% of all available compounds were needed for training and validation, and the rest of the data set was screened with more than a ten-fold gain of the original RA/N value. Thus, ANNs trained with limited HTS data might become useful in recovering active compounds from large data sets. | en_US |
dc.publisher | SAGE Publications | en_US |
dc.rights | Copyright SAGE Publications | en_US |
dc.subject | Patter classification | en_US |
dc.subject | Neural networks | en_US |
dc.subject | Generalization property | en_US |
dc.title | Artificial Neural Network Based Analysis of High Throughput Screening Data for Improved Prediction of Active Compounds | en_US |
dc.type | Article | en_US |
kusw.kuauthor | Chakrabarti, Swapan | |
kusw.kudepartment | Electrical Engineering and Computer Science | en_US |
dc.identifier.doi | 10.1177/1087057109351312 | en_US |
kusw.oaversion | Scholarly/refereed, author accepted manuscript | en_US |
kusw.oapolicy | This item meets KU Open Access policy criteria. | en_US |
dc.rights.accessrights | openAccess | |