A machine learning approach for predicting Antibody Properties

Allbwn ymchwil: Pennod mewn Llyfr/Adroddiad/Trafodion CynhadleddCyfraniad i gynhadleddadolygiad gan gymheiriaid

71 Wedi eu Llwytho i Lawr (Pure)

Crynodeb

This paper used an amino acid location-based sequence encoding as a feature extraction techniques to identify single chains antibody molecules that bind to B-lymphocyte stimulator (BLyS) antigen. The data were manually derived from the European patent (EP2275449B1) text. The dataset was cleaned and made suitable for the machine learning models. The accuracy, precision and recall achieved across individual descriptors (Membrane and Soluble) for Logistic regression, KNN, KSVM, and Random Forest Tree was above 80%. However, it was much lower for the Naïve Bayes except for the precision score. The promising accuracy value achieved from such a minimal dataset has significant implications for the drug discovery process – this includes considerable savings in time and resources.
Iaith wreiddiolSaesneg
TeitlProceedings of ICICM 2020 - 2020 10th International Conference on Information Communication and Management, Worshop
Is-deitlICKET 2020 - 2020 9th International Conference on Knowledge and Education Technology
Man cyhoeddiParis, France
CyhoeddwrAssociation for Computing Machinery
Tudalennau20-24
Nifer y tudalennau5
ISBN (Electronig)978-1-4503-8770-5
ISBN (Argraffiad)978-1-4503-8770-5
Dynodwyr Gwrthrych Digidol (DOIs)
StatwsCyhoeddwyd - 28 Meh 2020

Cyfres gyhoeddiadau

EnwACM International Conference Proceeding Series

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