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Exploiting uncertainty measures in compounds activity prediction using support vector machines
The great majority of molecular modeling tasks require the construction of a model that is then used to evaluate new compounds. Although various types of these models exist, at some stage, they all use knowledge about the activity of a given group of compounds, and the performance of the models is dependent on the quality of these data. Biological experiments verifying the activity of chemical compounds are often not reproducible; hence, databases containing these results often possess various activity records for a given molecule. In this study, we developed a method that incorporates the uncertainty of biological tests in machine-learning-based experiments using the Support Vector Machine as a classification model. We show that the developed methodology improves the classification effectiveness in the tested conditions.
cris.lastimport.scopus | 2024-04-07T14:35:36Z | |
cris.lastimport.wos | 2024-04-10T02:14:53Z | |
dc.abstract.en | The great majority of molecular modeling tasks require the construction of a model that is then used to evaluate new compounds. Although various types of these models exist, at some stage, they all use knowledge about the activity of a given group of compounds, and the performance of the models is dependent on the quality of these data. Biological experiments verifying the activity of chemical compounds are often not reproducible; hence, databases containing these results often possess various activity records for a given molecule. In this study, we developed a method that incorporates the uncertainty of biological tests in machine-learning-based experiments using the Support Vector Machine as a classification model. We show that the developed methodology improves the classification effectiveness in the tested conditions. | pl |
dc.affiliation | Wydział Matematyki i Informatyki : Instytut Informatyki i Matematyki Komputerowej | pl |
dc.contributor.author | Podlewska, Sabina - 149058 | pl |
dc.contributor.author | Czarnecki, Wojciech - 115076 | pl |
dc.contributor.author | Warszycki, Dawid | pl |
dc.contributor.author | Bojarski, Andrzej J. | pl |
dc.date.accessioned | 2015-06-26T11:35:23Z | |
dc.date.available | 2015-06-26T11:35:23Z | |
dc.date.issued | 2015 | pl |
dc.description.additional | Na publikacji autorka Podlewska Sabina podpisana jako Smusz Sabina. | pl |
dc.description.number | 1 | pl |
dc.description.physical | 100-105 | pl |
dc.description.volume | 25 | pl |
dc.identifier.doi | 10.1016/j.bmcl.2014.11.005 | pl |
dc.identifier.eissn | 1464-3405 | pl |
dc.identifier.issn | 0960-894X | pl |
dc.identifier.uri | http://ruj.uj.edu.pl/xmlui/handle/item/10502 | |
dc.language | eng | pl |
dc.language.container | eng | pl |
dc.rights.licence | bez licencji | |
dc.subtype | Article | pl |
dc.title | Exploiting uncertainty measures in compounds activity prediction using support vector machines | pl |
dc.title.journal | Bioorganic & Medicinal Chemistry Letters | pl |
dc.type | JournalArticle | pl |
dspace.entity.type | Publication |