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A Machine Learning Approach for Predicting Antibody Properties
Oche A. Egaji
, Seamus Ballard-Smith
,
Ikram Asghar
, Mark Griffiths
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Chapter in Book/Report/Conference proceeding
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Conference contribution
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Keyphrases
Machine Learning Models
100%
Machine Learning Approach
100%
Location-based
100%
Feature Extraction Methods
100%
Amino Acids
100%
Logistic Regression
100%
Sequence Encoding
100%
Single-chain Antibody
100%
Accuracy Value
100%
Nave Bayes
100%
Random Tree
100%
Precision Score
100%
Limited Datasets
100%
Drug pipeline
100%
European Patent
100%
B-lymphocyte Stimulator (BLyS)
100%
Computer Science
Random Decision Forest
100%
Logistic Regression
100%
Nave Bayes
100%
k-Nearest Neighbors Algorithm
100%
Machine Learning Approach
100%
Discovery Process
100%
Machine Learning
100%
Learning System
100%
Feature Extraction
100%
Engineering
Learning Approach
100%
Learning System
100%
Feature Extraction
50%
Random Forest
50%
B Cell
50%
Stimulator
50%
Antibody Molecule
50%
Material Science
Lymphocyte
100%