Matches in SemOpenAlex for { <https://semopenalex.org/work/W4309083002> ?p ?o ?g. }
Showing items 1 to 74 of
74
with 100 items per page.
- W4309083002 abstract "Drug screening is an important step in the development of new drugs. Through appropriate experimental methods and screening models, drugs with specific bioactivity can be transferred from laboratory research to clinical application. Nowadays, traditional efficiency of drug experimentation has been unable to meet the needs of our society. With the rapid development of computer technology, computer-assisted diagnosis and treatment have been gradually accepted and recognized by clinicians and patients. Drug screening process at the cellular level was studied in this paper. We not only compared the advantages and disadvantages of deep learning models and traditional machine learning methods, but also analyzed the performance of different deep learning models. First, Hela cells injected with different anti-stress drugs were divided into groups for experiment. G3BP, TIA-1 and the nucleus were labeled, respectively. The images were obtained using a single-photon microscope. Then, we distinguished the images of Hela cells treated with different drugs through visual observation, traditional machine learning (LBP/Gabor+SVM) and deep learning algorithms (VGGNet, GoogLeNet, ResNext and DenseNet), respectively. Experimental results showed that compared with visual observation, traditional machine learning and deep learning algorithms had better objectivity. Furthermore, deep learning models all had good classification performance. The highest average correct recognition rate was 92.97%, while that of the traditional method was only 80.93%. Therefore, drug screening methods based on deep learning could assist in screening the optimal treatment drugs, which help clinicians choose appropriate therapy." @default.
- W4309083002 created "2022-11-21" @default.
- W4309083002 creator A5012687326 @default.
- W4309083002 creator A5026904351 @default.
- W4309083002 creator A5048333923 @default.
- W4309083002 creator A5050118501 @default.
- W4309083002 creator A5064317688 @default.
- W4309083002 creator A5075596275 @default.
- W4309083002 date "2022-11-15" @default.
- W4309083002 modified "2023-10-16" @default.
- W4309083002 title "Drug screening methods based on deep learning" @default.
- W4309083002 cites W2766761250 @default.
- W4309083002 cites W2810711993 @default.
- W4309083002 cites W3046220160 @default.
- W4309083002 cites W3094072188 @default.
- W4309083002 cites W3123943527 @default.
- W4309083002 cites W4245495926 @default.
- W4309083002 doi "https://doi.org/10.1117/12.2638221" @default.
- W4309083002 hasPublicationYear "2022" @default.
- W4309083002 type Work @default.
- W4309083002 citedByCount "0" @default.
- W4309083002 crossrefType "proceedings-article" @default.
- W4309083002 hasAuthorship W4309083002A5012687326 @default.
- W4309083002 hasAuthorship W4309083002A5026904351 @default.
- W4309083002 hasAuthorship W4309083002A5048333923 @default.
- W4309083002 hasAuthorship W4309083002A5050118501 @default.
- W4309083002 hasAuthorship W4309083002A5064317688 @default.
- W4309083002 hasAuthorship W4309083002A5075596275 @default.
- W4309083002 hasConcept C108583219 @default.
- W4309083002 hasConcept C111472728 @default.
- W4309083002 hasConcept C119857082 @default.
- W4309083002 hasConcept C12267149 @default.
- W4309083002 hasConcept C138885662 @default.
- W4309083002 hasConcept C1491633281 @default.
- W4309083002 hasConcept C154945302 @default.
- W4309083002 hasConcept C185592680 @default.
- W4309083002 hasConcept C2482559 @default.
- W4309083002 hasConcept C2777366897 @default.
- W4309083002 hasConcept C2780035454 @default.
- W4309083002 hasConcept C41008148 @default.
- W4309083002 hasConcept C55493867 @default.
- W4309083002 hasConcept C71924100 @default.
- W4309083002 hasConcept C98274493 @default.
- W4309083002 hasConceptScore W4309083002C108583219 @default.
- W4309083002 hasConceptScore W4309083002C111472728 @default.
- W4309083002 hasConceptScore W4309083002C119857082 @default.
- W4309083002 hasConceptScore W4309083002C12267149 @default.
- W4309083002 hasConceptScore W4309083002C138885662 @default.
- W4309083002 hasConceptScore W4309083002C1491633281 @default.
- W4309083002 hasConceptScore W4309083002C154945302 @default.
- W4309083002 hasConceptScore W4309083002C185592680 @default.
- W4309083002 hasConceptScore W4309083002C2482559 @default.
- W4309083002 hasConceptScore W4309083002C2777366897 @default.
- W4309083002 hasConceptScore W4309083002C2780035454 @default.
- W4309083002 hasConceptScore W4309083002C41008148 @default.
- W4309083002 hasConceptScore W4309083002C55493867 @default.
- W4309083002 hasConceptScore W4309083002C71924100 @default.
- W4309083002 hasConceptScore W4309083002C98274493 @default.
- W4309083002 hasLocation W43090830021 @default.
- W4309083002 hasOpenAccess W4309083002 @default.
- W4309083002 hasPrimaryLocation W43090830021 @default.
- W4309083002 hasRelatedWork W2803710604 @default.
- W4309083002 hasRelatedWork W3136979370 @default.
- W4309083002 hasRelatedWork W4205958290 @default.
- W4309083002 hasRelatedWork W4223943233 @default.
- W4309083002 hasRelatedWork W4285106639 @default.
- W4309083002 hasRelatedWork W4309045103 @default.
- W4309083002 hasRelatedWork W4311106074 @default.
- W4309083002 hasRelatedWork W4312200629 @default.
- W4309083002 hasRelatedWork W4360585206 @default.
- W4309083002 hasRelatedWork W4364306694 @default.
- W4309083002 isParatext "false" @default.
- W4309083002 isRetracted "false" @default.
- W4309083002 workType "article" @default.