FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W1996851544> ?p ?o ?g. }

• W1996851544 endingPage "2578" @default.
• W1996851544 startingPage "2570" @default.
• W1996851544 abstract "Prior to using a quantitative structure activity relationship (QSAR) model for external predictions, its predictive power should be established and validated. In the absence of a true external data set, the best way to validate the predictive ability of a model is to perform its statistical external validation. In statistical external validation, the overall data set is divided into training and test sets. Commonly, this splitting is performed using random division. Rational splitting methods can divide data sets into training and test sets in an intelligent fashion. The purpose of this study was to determine whether rational division methods lead to more predictive models compared to random division. A special data splitting procedure was used to facilitate the comparison between random and rational division methods. For each toxicity end point, the overall data set was divided into a modeling set (80% of the overall set) and an external evaluation set (20% of the overall set) using random division. The modeling set was then subdivided into a training set (80% of the modeling set) and a test set (20% of the modeling set) using rational division methods and by using random division. The Kennard-Stone, minimal test set dissimilarity, and sphere exclusion algorithms were used as the rational division methods. The hierarchical clustering, random forest, and k-nearest neighbor (kNN) methods were used to develop QSAR models based on the training sets. For kNN QSAR, multiple training and test sets were generated, and multiple QSAR models were built. The results of this study indicate that models based on rational division methods generate better statistical results for the test sets than models based on random division, but the predictive power of both types of models are comparable." @default.
• W1996851544 created "2016-06-24" @default.
• W1996851544 creator A5026329292 @default.
• W1996851544 creator A5028184276 @default.
• W1996851544 creator A5029916489 @default.
• W1996851544 creator A5049701126 @default.
• W1996851544 creator A5052755397 @default.
• W1996851544 creator A5078536199 @default.
• W1996851544 creator A5080032083 @default.
• W1996851544 date "2012-10-03" @default.
• W1996851544 modified "2023-10-11" @default.
• W1996851544 title "Does Rational Selection of Training and Test Sets Improve the Outcome of QSAR Modeling?" @default.
• W1996851544 cites W1571439140 @default.
• W1996851544 cites W1977351276 @default.
• W1996851544 cites W1977573154 @default.
• W1996851544 cites W1983464445 @default.
• W1996851544 cites W1986453686 @default.
• W1996851544 cites W1995427895 @default.
• W1996851544 cites W2002097322 @default.
• W1996851544 cites W2010524461 @default.
• W1996851544 cites W2011779808 @default.
• W1996851544 cites W2011785226 @default.
• W1996851544 cites W2012540065 @default.
• W1996851544 cites W2017422910 @default.
• W1996851544 cites W2021133788 @default.
• W1996851544 cites W2031895393 @default.
• W1996851544 cites W2043076340 @default.
• W1996851544 cites W2057868137 @default.
• W1996851544 cites W2063530812 @default.
• W1996851544 cites W2069203035 @default.
• W1996851544 cites W2070246294 @default.
• W1996851544 cites W2070442000 @default.
• W1996851544 cites W2072720531 @default.
• W1996851544 cites W2073876332 @default.
• W1996851544 cites W2076147571 @default.
• W1996851544 cites W2086190989 @default.
• W1996851544 cites W2091589454 @default.
• W1996851544 cites W2095138748 @default.
• W1996851544 cites W2101436897 @default.
• W1996851544 cites W2114914962 @default.
• W1996851544 cites W2127644822 @default.
• W1996851544 cites W2157851318 @default.
• W1996851544 cites W2170644559 @default.
• W1996851544 cites W2213612645 @default.
• W1996851544 cites W2333833573 @default.
• W1996851544 cites W2911964244 @default.
• W1996851544 cites W2999197824 @default.
• W1996851544 cites W302009716 @default.
• W1996851544 cites W4292199333 @default.
• W1996851544 doi "https://doi.org/10.1021/ci300338w" @default.
• W1996851544 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/23030316" @default.
• W1996851544 hasPublicationYear "2012" @default.
• W1996851544 type Work @default.
• W1996851544 sameAs 1996851544 @default.
• W1996851544 citedByCount "215" @default.
• W1996851544 countsByYear W19968515442013 @default.
• W1996851544 countsByYear W19968515442014 @default.
• W1996851544 countsByYear W19968515442015 @default.
• W1996851544 countsByYear W19968515442016 @default.
• W1996851544 countsByYear W19968515442017 @default.
• W1996851544 countsByYear W19968515442018 @default.
• W1996851544 countsByYear W19968515442019 @default.
• W1996851544 countsByYear W19968515442020 @default.
• W1996851544 countsByYear W19968515442021 @default.
• W1996851544 countsByYear W19968515442022 @default.
• W1996851544 countsByYear W19968515442023 @default.
• W1996851544 crossrefType "journal-article" @default.
• W1996851544 hasAuthorship W1996851544A5026329292 @default.
• W1996851544 hasAuthorship W1996851544A5028184276 @default.
• W1996851544 hasAuthorship W1996851544A5029916489 @default.
• W1996851544 hasAuthorship W1996851544A5049701126 @default.
• W1996851544 hasAuthorship W1996851544A5052755397 @default.
• W1996851544 hasAuthorship W1996851544A5078536199 @default.
• W1996851544 hasAuthorship W1996851544A5080032083 @default.
• W1996851544 hasConcept C119857082 @default.
• W1996851544 hasConcept C124101348 @default.
• W1996851544 hasConcept C154945302 @default.
• W1996851544 hasConcept C164126121 @default.
• W1996851544 hasConcept C169258074 @default.
• W1996851544 hasConcept C169903167 @default.
• W1996851544 hasConcept C177264268 @default.
• W1996851544 hasConcept C199360897 @default.
• W1996851544 hasConcept C33923547 @default.
• W1996851544 hasConcept C41008148 @default.
• W1996851544 hasConcept C51632099 @default.
• W1996851544 hasConcept C58489278 @default.
• W1996851544 hasConcept C60798267 @default.
• W1996851544 hasConcept C73555534 @default.
• W1996851544 hasConcept C94375191 @default.
• W1996851544 hasConceptScore W1996851544C119857082 @default.
• W1996851544 hasConceptScore W1996851544C124101348 @default.
• W1996851544 hasConceptScore W1996851544C154945302 @default.
• W1996851544 hasConceptScore W1996851544C164126121 @default.
• W1996851544 hasConceptScore W1996851544C169258074 @default.
• W1996851544 hasConceptScore W1996851544C169903167 @default.
• W1996851544 hasConceptScore W1996851544C177264268 @default.
• W1996851544 hasConceptScore W1996851544C199360897 @default.
• W1996851544 hasConceptScore W1996851544C33923547 @default.

• next