Matches in SemOpenAlex for { <https://semopenalex.org/work/W2765388300> ?p ?o ?g. }
Showing items 1 to 82 of
82
with 100 items per page.
- W2765388300 abstract "Drug Combination is one of the effective approaches for treating complex diseases. However, determining combinative drug pairs in clinical trials is still costly. Thus, computational approaches are used to identify potential drug pairs in advance. Existing computational approaches have the following shortcomings: (i) the lack of an effective integration of heterogeneous features leads to a time-consuming training and even results in an over-fitted classifier; and (ii) the narrow consideration of predicting potential drug combinations only among known drugs having known combinations cannot meet the demand of realistic screenings, which pay more attention to potential combinative pairs among newly-coming drugs that have no approved combination with other drugs at all.In this paper, to tackle the above two problems, we propose a novel drug-driven approach for predicting potential combinative pairs on a large scale. We define four new features based on heterogeneous data and design an efficient fusion scheme to integrate these feature. Moreover importantly, we elaborate appropriate cross-validations towards realistic screening scenarios of drug combinations involving both known drugs and new drugs. In addition, we perform an extra investigation to show how each kind of heterogeneous features is related to combinative drug pairs. The investigation inspires the design of our approach. Experiments on real data demonstrate the effectiveness of our fusion scheme for integrating heterogeneous features and its predicting power in three scenarios of realistic screening. In terms of both AUC and AUPR, the prediction among known drugs achieves 0.954 and 0.821, that between known drugs and new drugs achieves 0.909 and 0.635, and that among new drugs achieves 0.809 and 0.592 respectively.Our approach provides not only an effective tool to integrate heterogeneous features but also the first tool to predict potential combinative pairs among new drugs." @default.
- W2765388300 created "2017-11-10" @default.
- W2765388300 creator A5000609872 @default.
- W2765388300 creator A5017061461 @default.
- W2765388300 creator A5020095072 @default.
- W2765388300 creator A5043565689 @default.
- W2765388300 creator A5062947742 @default.
- W2765388300 date "2017-10-01" @default.
- W2765388300 modified "2023-10-16" @default.
- W2765388300 title "Predicting combinative drug pairs towards realistic screening via integrating heterogeneous features" @default.
- W2765388300 cites W146257638 @default.
- W2765388300 cites W1998352087 @default.
- W2765388300 cites W2054768549 @default.
- W2765388300 cites W2061136308 @default.
- W2765388300 cites W2082699616 @default.
- W2765388300 cites W2098924854 @default.
- W2765388300 cites W2112243395 @default.
- W2765388300 cites W2126726407 @default.
- W2765388300 cites W2131835644 @default.
- W2765388300 cites W2138427043 @default.
- W2765388300 cites W2145578524 @default.
- W2765388300 cites W2146416540 @default.
- W2765388300 cites W2165863045 @default.
- W2765388300 cites W2170146596 @default.
- W2765388300 cites W2174203236 @default.
- W2765388300 doi "https://doi.org/10.1186/s12859-017-1818-2" @default.
- W2765388300 hasPubMedCentralId "https://www.ncbi.nlm.nih.gov/pmc/articles/5657064" @default.
- W2765388300 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/29072137" @default.
- W2765388300 hasPublicationYear "2017" @default.
- W2765388300 type Work @default.
- W2765388300 sameAs 2765388300 @default.
- W2765388300 citedByCount "17" @default.
- W2765388300 countsByYear W27653883002018 @default.
- W2765388300 countsByYear W27653883002019 @default.
- W2765388300 countsByYear W27653883002020 @default.
- W2765388300 countsByYear W27653883002021 @default.
- W2765388300 countsByYear W27653883002022 @default.
- W2765388300 crossrefType "journal-article" @default.
- W2765388300 hasAuthorship W2765388300A5000609872 @default.
- W2765388300 hasAuthorship W2765388300A5017061461 @default.
- W2765388300 hasAuthorship W2765388300A5020095072 @default.
- W2765388300 hasAuthorship W2765388300A5043565689 @default.
- W2765388300 hasAuthorship W2765388300A5062947742 @default.
- W2765388300 hasBestOaLocation W27653883001 @default.
- W2765388300 hasConcept C119857082 @default.
- W2765388300 hasConcept C124101348 @default.
- W2765388300 hasConcept C154945302 @default.
- W2765388300 hasConcept C2780035454 @default.
- W2765388300 hasConcept C41008148 @default.
- W2765388300 hasConcept C71924100 @default.
- W2765388300 hasConcept C95623464 @default.
- W2765388300 hasConcept C98274493 @default.
- W2765388300 hasConceptScore W2765388300C119857082 @default.
- W2765388300 hasConceptScore W2765388300C124101348 @default.
- W2765388300 hasConceptScore W2765388300C154945302 @default.
- W2765388300 hasConceptScore W2765388300C2780035454 @default.
- W2765388300 hasConceptScore W2765388300C41008148 @default.
- W2765388300 hasConceptScore W2765388300C71924100 @default.
- W2765388300 hasConceptScore W2765388300C95623464 @default.
- W2765388300 hasConceptScore W2765388300C98274493 @default.
- W2765388300 hasIssue "S12" @default.
- W2765388300 hasLocation W27653883001 @default.
- W2765388300 hasLocation W27653883002 @default.
- W2765388300 hasLocation W27653883003 @default.
- W2765388300 hasLocation W27653883004 @default.
- W2765388300 hasOpenAccess W2765388300 @default.
- W2765388300 hasPrimaryLocation W27653883001 @default.
- W2765388300 hasRelatedWork W2556319748 @default.
- W2765388300 hasRelatedWork W2961085424 @default.
- W2765388300 hasRelatedWork W3046775127 @default.
- W2765388300 hasRelatedWork W3170094116 @default.
- W2765388300 hasRelatedWork W3200179079 @default.
- W2765388300 hasRelatedWork W4285260836 @default.
- W2765388300 hasRelatedWork W4286629047 @default.
- W2765388300 hasRelatedWork W4306321456 @default.
- W2765388300 hasRelatedWork W4306674287 @default.
- W2765388300 hasRelatedWork W4224009465 @default.
- W2765388300 hasVolume "18" @default.
- W2765388300 isParatext "false" @default.
- W2765388300 isRetracted "false" @default.
- W2765388300 magId "2765388300" @default.
- W2765388300 workType "article" @default.