FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W3210734461> ?p ?o ?g. }

• W3210734461 endingPage "1" @default.
• W3210734461 startingPage "1" @default.
• W3210734461 abstract "Essential proteins are considered the foundation of life as they are indispensable for the survival of living organisms. Computational methods for essential protein discovery provide a fast way to identify essential proteins. But most of them heavily rely on various biological information, especially protein-protein interaction networks, which limits their practical applications. With the rapid development of high-throughput sequencing technology, sequencing data has become the most accessible biological data. However, using only protein sequence information to predict essential proteins has limited accuracy. In this paper, we propose EP-EDL, an ensemble deep learning model using only protein sequence information to predict human essential proteins. EP-EDL integrates multiple classifiers to alleviate the class imbalance problem and to improve prediction accuracy and robustness. In each base classifier, we employ multi-scale text convolutional neural networks to extract useful features from protein sequence feature matrices with evolutionary information. Our computational results show that EP-EDL outperforms the state-of-the-art sequence-based methods. Furthermore, EP-EDL provides a more practical and flexible way for biologists to accurately predict essential proteins. The source code and datasets can be downloaded from https://github.com/CSUBioGroup/EP-EDL." @default.
• W3210734461 created "2021-11-08" @default.
• W3210734461 creator A5018365310 @default.
• W3210734461 creator A5020044791 @default.
• W3210734461 creator A5033885404 @default.
• W3210734461 creator A5061133691 @default.
• W3210734461 creator A5079316675 @default.
• W3210734461 date "2021-01-01" @default.
• W3210734461 modified "2023-10-16" @default.
• W3210734461 title "Accurate Prediction of Human Essential Proteins Using Ensemble Deep Learning" @default.
• W3210734461 cites W1501531009 @default.
• W3210734461 cites W1503938960 @default.
• W3210734461 cites W1709660540 @default.
• W3210734461 cites W1714524773 @default.
• W3210734461 cites W1934704664 @default.
• W3210734461 cites W1998152384 @default.
• W3210734461 cites W2005740247 @default.
• W3210734461 cites W2025971310 @default.
• W3210734461 cites W2035610697 @default.
• W3210734461 cites W2048386825 @default.
• W3210734461 cites W2053096433 @default.
• W3210734461 cites W2085808107 @default.
• W3210734461 cites W2097606916 @default.
• W3210734461 cites W2106900391 @default.
• W3210734461 cites W2113140044 @default.
• W3210734461 cites W2121507105 @default.
• W3210734461 cites W2130646768 @default.
• W3210734461 cites W2130790725 @default.
• W3210734461 cites W2142437744 @default.
• W3210734461 cites W2146447390 @default.
• W3210734461 cites W2147580225 @default.
• W3210734461 cites W2150744251 @default.
• W3210734461 cites W2158714788 @default.
• W3210734461 cites W2159595936 @default.
• W3210734461 cites W2169916860 @default.
• W3210734461 cites W2174686320 @default.
• W3210734461 cites W2180291937 @default.
• W3210734461 cites W2407174002 @default.
• W3210734461 cites W2512143001 @default.
• W3210734461 cites W2599873893 @default.
• W3210734461 cites W2766955181 @default.
• W3210734461 cites W2790765877 @default.
• W3210734461 cites W2896808365 @default.
• W3210734461 cites W2912802963 @default.
• W3210734461 cites W2930484514 @default.
• W3210734461 cites W2935275081 @default.
• W3210734461 cites W2935703330 @default.
• W3210734461 cites W2949280944 @default.
• W3210734461 cites W2991321419 @default.
• W3210734461 cites W3003053789 @default.
• W3210734461 cites W3003792773 @default.
• W3210734461 cites W3008847215 @default.
• W3210734461 cites W3049380554 @default.
• W3210734461 cites W3120403132 @default.
• W3210734461 cites W3177371828 @default.
• W3210734461 cites W3198604700 @default.
• W3210734461 doi "https://doi.org/10.1109/tcbb.2021.3122294" @default.
• W3210734461 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/34699365" @default.
• W3210734461 hasPublicationYear "2021" @default.
• W3210734461 type Work @default.
• W3210734461 sameAs 3210734461 @default.
• W3210734461 citedByCount "5" @default.
• W3210734461 countsByYear W32107344612022 @default.
• W3210734461 countsByYear W32107344612023 @default.
• W3210734461 crossrefType "journal-article" @default.
• W3210734461 hasAuthorship W3210734461A5018365310 @default.
• W3210734461 hasAuthorship W3210734461A5020044791 @default.
• W3210734461 hasAuthorship W3210734461A5033885404 @default.
• W3210734461 hasAuthorship W3210734461A5061133691 @default.
• W3210734461 hasAuthorship W3210734461A5079316675 @default.
• W3210734461 hasBestOaLocation W32107344611 @default.
• W3210734461 hasConcept C10010492 @default.
• W3210734461 hasConcept C104317684 @default.
• W3210734461 hasConcept C108583219 @default.
• W3210734461 hasConcept C111919701 @default.
• W3210734461 hasConcept C119857082 @default.
• W3210734461 hasConcept C154945302 @default.
• W3210734461 hasConcept C167625842 @default.
• W3210734461 hasConcept C41008148 @default.
• W3210734461 hasConcept C43126263 @default.
• W3210734461 hasConcept C55493867 @default.
• W3210734461 hasConcept C63479239 @default.
• W3210734461 hasConcept C70721500 @default.
• W3210734461 hasConcept C81363708 @default.
• W3210734461 hasConcept C86803240 @default.
• W3210734461 hasConcept C95623464 @default.
• W3210734461 hasConceptScore W3210734461C10010492 @default.
• W3210734461 hasConceptScore W3210734461C104317684 @default.
• W3210734461 hasConceptScore W3210734461C108583219 @default.
• W3210734461 hasConceptScore W3210734461C111919701 @default.
• W3210734461 hasConceptScore W3210734461C119857082 @default.
• W3210734461 hasConceptScore W3210734461C154945302 @default.
• W3210734461 hasConceptScore W3210734461C167625842 @default.
• W3210734461 hasConceptScore W3210734461C41008148 @default.
• W3210734461 hasConceptScore W3210734461C43126263 @default.
• W3210734461 hasConceptScore W3210734461C55493867 @default.
• W3210734461 hasConceptScore W3210734461C63479239 @default.
• W3210734461 hasConceptScore W3210734461C70721500 @default.

• next