Identification of intelligence-related proteins through a robust two-layer predictor.
Intell_pred
learning
protein features
support vector machine
Journal
Communicative & integrative biology
ISSN: 1942-0889
Titre abrégé: Commun Integr Biol
Pays: United States
ID NLM: 101478473
Informations de publication
Date de publication:
2022
2022
Historique:
entrez:
21
11
2022
pubmed:
22
11
2022
medline:
22
11
2022
Statut:
epublish
Résumé
In this study, we advance a robust methodology for identifying specific intelligence-related proteins across phyla. Our approach exploits a support vector machine-based classifier capable of predicting intelligence-related proteins based on a pool of meaningful protein features. For the sake of illustration of our proposed general method, we develop a novel computational two-layer predictor, Intell_Pred, to predict query sequences (proteins or transcripts) as intelligence-related or non-intelligence-related proteins or transcripts, subsequently classifying the former sequences into learning and memory-related classes. Based on a five-fold cross-validation and independent blind test, Intell_Pred obtained an average accuracy of 87.48 and 88.89, respectively. Our findings revealed that a score >0.75 (during prediction by Intell_Pred) is a well-grounded choice for predicting intelligence-related candidate proteins in most organisms across biological kingdoms. In particular, we assessed seismonastic movements and associate learning in plants and evaluated the proteins involved using Intell_Pred. Proteins related to seismonastic movement and associate learning showed high percentages of similarities with intelligence-related proteins. Our findings lead us to believe that Intell_Pred can help identify the intelligence-related proteins and their classes using a given protein/transcript sequence.
Identifiants
pubmed: 36406257
doi: 10.1080/19420889.2022.2143101
pii: 2143101
pmc: PMC9673931
doi:
Types de publication
Journal Article
Langues
eng
Pagination
253-264Informations de copyright
© 2022 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group.
Déclaration de conflit d'intérêts
No potential conflict of interest was reported by the author(s).
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