Matches in SemOpenAlex for { <https://semopenalex.org/work/W3021484021> ?p ?o ?g. }
Showing items 1 to 73 of
73
with 100 items per page.
- W3021484021 abstract "Biological databases consist of variety of information such as the interactions between proteins, biological functions of proteins and the structure of the proteins. Most of the biological functions of the human body are coordinated by the interactions of proteins with other proteins. Proteins don’t act alone. It interacts with other proteins in a biological network to perform numerous functions such as signaling, gene expression. Most of the biological functions are performed with the help of protein interaction network. There is a great need to understand the protein interaction network that helps the researcher to identify proteins associated for various diseases. Many intelligent based computational based approaches have been analyzed by the researchers to improve the prediction exactness of the vital proteins for diseases. However, it is challenging to predict the vital proteins associated with diseases. In this research, we outline the traditional data mining and machine learning methods available to predict the proteins associated with the disease, projecting the strengths, weaknesses and challenges of each model. Furthermore, a new research direction to mine the most vital proteins is proposed and results are remarkable than traditional methods" @default.
- W3021484021 created "2020-05-13" @default.
- W3021484021 creator A5000838883 @default.
- W3021484021 creator A5036437458 @default.
- W3021484021 date "2019-12-01" @default.
- W3021484021 modified "2023-10-16" @default.
- W3021484021 title "An Intelligent Computational Model To Predict Target Genes For Infectious Disease" @default.
- W3021484021 cites W1714524773 @default.
- W3021484021 cites W1970173121 @default.
- W3021484021 cites W1975191997 @default.
- W3021484021 cites W1976978745 @default.
- W3021484021 cites W2081837827 @default.
- W3021484021 cites W2095344264 @default.
- W3021484021 cites W2107290125 @default.
- W3021484021 cites W2145427519 @default.
- W3021484021 cites W2146447390 @default.
- W3021484021 cites W2150744251 @default.
- W3021484021 cites W2153280946 @default.
- W3021484021 cites W2326727950 @default.
- W3021484021 cites W2517915074 @default.
- W3021484021 cites W2789538492 @default.
- W3021484021 cites W828529927 @default.
- W3021484021 doi "https://doi.org/10.1109/icoac48765.2019.246865" @default.
- W3021484021 hasPublicationYear "2019" @default.
- W3021484021 type Work @default.
- W3021484021 sameAs 3021484021 @default.
- W3021484021 citedByCount "0" @default.
- W3021484021 crossrefType "proceedings-article" @default.
- W3021484021 hasAuthorship W3021484021A5000838883 @default.
- W3021484021 hasAuthorship W3021484021A5036437458 @default.
- W3021484021 hasConcept C104317684 @default.
- W3021484021 hasConcept C11804247 @default.
- W3021484021 hasConcept C119857082 @default.
- W3021484021 hasConcept C136197465 @default.
- W3021484021 hasConcept C154945302 @default.
- W3021484021 hasConcept C201797286 @default.
- W3021484021 hasConcept C28225019 @default.
- W3021484021 hasConcept C2911029443 @default.
- W3021484021 hasConcept C41008148 @default.
- W3021484021 hasConcept C54355233 @default.
- W3021484021 hasConcept C60644358 @default.
- W3021484021 hasConcept C70721500 @default.
- W3021484021 hasConcept C86803240 @default.
- W3021484021 hasConceptScore W3021484021C104317684 @default.
- W3021484021 hasConceptScore W3021484021C11804247 @default.
- W3021484021 hasConceptScore W3021484021C119857082 @default.
- W3021484021 hasConceptScore W3021484021C136197465 @default.
- W3021484021 hasConceptScore W3021484021C154945302 @default.
- W3021484021 hasConceptScore W3021484021C201797286 @default.
- W3021484021 hasConceptScore W3021484021C28225019 @default.
- W3021484021 hasConceptScore W3021484021C2911029443 @default.
- W3021484021 hasConceptScore W3021484021C41008148 @default.
- W3021484021 hasConceptScore W3021484021C54355233 @default.
- W3021484021 hasConceptScore W3021484021C60644358 @default.
- W3021484021 hasConceptScore W3021484021C70721500 @default.
- W3021484021 hasConceptScore W3021484021C86803240 @default.
- W3021484021 hasLocation W30214840211 @default.
- W3021484021 hasOpenAccess W3021484021 @default.
- W3021484021 hasPrimaryLocation W30214840211 @default.
- W3021484021 hasRelatedWork W10987132 @default.
- W3021484021 hasRelatedWork W10991292 @default.
- W3021484021 hasRelatedWork W12194291 @default.
- W3021484021 hasRelatedWork W13703775 @default.
- W3021484021 hasRelatedWork W3858741 @default.
- W3021484021 hasRelatedWork W6933897 @default.
- W3021484021 hasRelatedWork W8010423 @default.
- W3021484021 hasRelatedWork W8249623 @default.
- W3021484021 hasRelatedWork W9566262 @default.
- W3021484021 hasRelatedWork W7333638 @default.
- W3021484021 isParatext "false" @default.
- W3021484021 isRetracted "false" @default.
- W3021484021 magId "3021484021" @default.
- W3021484021 workType "article" @default.