FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W3185189396> ?p ?o ?g. }

• W3185189396 endingPage "269" @default.
• W3185189396 startingPage "245" @default.
• W3185189396 abstract "Today’s world is seriously affected and medical infrastructure is challenged due to the novel COVID-19 epidemics. On 15th May 2020, Naturemedicine published a detailed survey report which concluded that drugs like aminoquinolines, chloroquine, hydroxychloroquine targeted the virus replication cycle by keeping the virus out of host cells and helping people to overcome the disease. But, the exponential incidence rate cannot be stopped and some limitations also detected to treat the patients using these drugs. In these circumstances, suitable medicines are immediately needed that can also help in vaccine production. In this chapter, a novel AI based architecture has been proposed to obtain influencial drugs for the COVID-19 treatment. Here, The COVID-19 (MT050943 and MT012098) data are taken from the NCBI database, submitted by the Government of India to build tests and find drugs. A detailed analysis of these sequences has been performed to find the noncoding percentage, gene density, and abundances in functional gene categories using Virus Amplicon Sequencing Assembly Pipeline (V-ASAP). This analysis finds the unique regions in the virus genome as a target region. This knowledge is feed into the AI model. Firstly, the informative genes are selected from the target region. Then the Advanced Matched Molecular Pair (AMMP) analysis is performed to study the possible local region of a drug and its impact. At last, a Generative Adversarial Convolution Neural Network (GACNN) is trained with the information gain and performs a gradient-based optimization to predict the drug to target for the validate genome. The result of the proposed methodology obtains higher accuracy than the other existing methods and identified effective drugs (like, Dexamethasone, Remdesivir etc.) for the study of the COVID-19 vaccine." @default.
• W3185189396 created "2021-08-02" @default.
• W3185189396 creator A5044446632 @default.
• W3185189396 creator A5068040643 @default.
• W3185189396 date "2021-07-28" @default.
• W3185189396 modified "2023-09-27" @default.
• W3185189396 title "Predicting Antiviral Drugs for COVID-19 Treatment Using Artificial Intelligence Based Approach" @default.
• W3185189396 cites W1993285168 @default.
• W3185189396 cites W2015142981 @default.
• W3185189396 cites W2145496621 @default.
• W3185189396 cites W2296686360 @default.
• W3185189396 cites W2615174046 @default.
• W3185189396 cites W2621473099 @default.
• W3185189396 cites W2734677449 @default.
• W3185189396 cites W2910793948 @default.
• W3185189396 cites W2944959599 @default.
• W3185189396 cites W3007010524 @default.
• W3185189396 cites W3012353902 @default.
• W3185189396 cites W3012994592 @default.
• W3185189396 cites W3013166012 @default.
• W3185189396 cites W3013758358 @default.
• W3185189396 cites W3019802146 @default.
• W3185189396 cites W3020514163 @default.
• W3185189396 cites W3025899282 @default.
• W3185189396 cites W3027747386 @default.
• W3185189396 cites W3038796705 @default.
• W3185189396 cites W3041467169 @default.
• W3185189396 cites W3109495579 @default.
• W3185189396 cites W3167906655 @default.
• W3185189396 cites W4230556746 @default.
• W3185189396 cites W4248236843 @default.
• W3185189396 doi "https://doi.org/10.1007/978-3-030-74761-9_11" @default.
• W3185189396 hasPublicationYear "2021" @default.
• W3185189396 type Work @default.
• W3185189396 sameAs 3185189396 @default.
• W3185189396 citedByCount "3" @default.
• W3185189396 countsByYear W31851893962021 @default.
• W3185189396 countsByYear W31851893962022 @default.
• W3185189396 countsByYear W31851893962023 @default.
• W3185189396 crossrefType "book-chapter" @default.
• W3185189396 hasAuthorship W3185189396A5044446632 @default.
• W3185189396 hasAuthorship W3185189396A5068040643 @default.
• W3185189396 hasConcept C103637391 @default.
• W3185189396 hasConcept C104317684 @default.
• W3185189396 hasConcept C119857082 @default.
• W3185189396 hasConcept C141231307 @default.
• W3185189396 hasConcept C154945302 @default.
• W3185189396 hasConcept C2780035454 @default.
• W3185189396 hasConcept C41008148 @default.
• W3185189396 hasConcept C54355233 @default.
• W3185189396 hasConcept C70721500 @default.
• W3185189396 hasConcept C86803240 @default.
• W3185189396 hasConcept C98274493 @default.
• W3185189396 hasConceptScore W3185189396C103637391 @default.
• W3185189396 hasConceptScore W3185189396C104317684 @default.
• W3185189396 hasConceptScore W3185189396C119857082 @default.
• W3185189396 hasConceptScore W3185189396C141231307 @default.
• W3185189396 hasConceptScore W3185189396C154945302 @default.
• W3185189396 hasConceptScore W3185189396C2780035454 @default.
• W3185189396 hasConceptScore W3185189396C41008148 @default.
• W3185189396 hasConceptScore W3185189396C54355233 @default.
• W3185189396 hasConceptScore W3185189396C70721500 @default.
• W3185189396 hasConceptScore W3185189396C86803240 @default.
• W3185189396 hasConceptScore W3185189396C98274493 @default.
• W3185189396 hasLocation W31851893961 @default.
• W3185189396 hasOpenAccess W3185189396 @default.
• W3185189396 hasPrimaryLocation W31851893961 @default.
• W3185189396 hasRelatedWork W1603543036 @default.
• W3185189396 hasRelatedWork W2961085424 @default.
• W3185189396 hasRelatedWork W2987420353 @default.
• W3185189396 hasRelatedWork W3046775127 @default.
• W3185189396 hasRelatedWork W3170094116 @default.
• W3185189396 hasRelatedWork W4285260836 @default.
• W3185189396 hasRelatedWork W4286629047 @default.
• W3185189396 hasRelatedWork W4306321456 @default.
• W3185189396 hasRelatedWork W4306674287 @default.
• W3185189396 hasRelatedWork W4224009465 @default.
• W3185189396 isParatext "false" @default.
• W3185189396 isRetracted "false" @default.
• W3185189396 magId "3185189396" @default.
• W3185189396 workType "book-chapter" @default.