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Machine learning study for the prediction of transdermal peptide
Eunkyoung Jung
, Seung Hoon Choi
, Nam Kyung Lee
, Sang Kee Kang
, Yun Jaie Choi
, Jae Min Shin
,
Kihang Choi
, Dong Hyun Jung
Research output
:
Contribution to journal
›
Article
›
peer-review
8
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Citations (Scopus)
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Computer Science
Support Vector Machine
100%
Machine Learning
100%
Learning System
100%
Amino Acid Sequence
100%
Least Squares Method
50%
Kernel Function
50%
Radial Basis Function
50%
Characteristic Curve
50%
Neural Network Architecture
50%
Random Sequence
50%
Computational Method
50%
Artificial Neural Network
50%
Keyphrases
Machine Learning Experiments
100%
Transdermal Peptide
100%
ROC Score
33%
Support Vector Machine Model
16%
Positive Control
16%
Peptide Sequencing
16%
Kernel Function
16%
Artificial Neural Network
16%
Partial Least Squares Structural Equation Modeling (PLS-SEM)
16%
Radial Basis Function
16%
Area under the Receiver Operating Characteristic Curve
16%
Computational Methods
16%
Sensitivity Specificity
16%
Sequence Information
16%
Phage Display Technique
16%
Machine Learning Models
16%
Intact Skin
16%
Transdermal Drug Delivery
16%
Score-based
16%
Transdermal
16%
Neural Network Architecture
16%
Statistical Indicators
16%
Curve ROC
16%
Least Squares Support Vector Machine
16%
Peptide Database
16%
Latent Variables
16%
Least Squares Vector Machine
16%
Biochemistry, Genetics and Molecular Biology
Support Vector Machine
100%
Peptide Sequence
50%
Phage Display
50%
Artificial Neural Network
50%
Neuroscience
Support Vector Machine
100%
Neural Network
50%
Amino Acid Sequence
50%
Artificial Neural Network
50%
Psychology
Neural Network
100%
Learning Model
50%
Computational Method
50%