Machine learning study for the prediction of transdermal peptide

Journal of Computer-Aided Molecular Design

Volumn 25, Issue 4, 2011, Pages 339-347

  Jung, Eunkyoung ^a   Choi, Seung Hoon ^a   Lee, Nam Kyung ^b   Kang, Sang Kee ^b   Choi, Yun Jaie ^b   Shin, Jae Min ^c   Choi, Kihang ^d   Jung, Dong Hyun ^a  

^a Insilicotech Co Ltd   (South Korea)

^b Seoul National University   (South Korea)

^c SBScience Co Ltd   (South Korea)

^d Korea University   (South Korea)

Author keywords

Artificial neural network; Machine learning; Partial least squares; ROC score; Support vector machine; Transdermal peptide; VHSE descriptor

Indexed keywords

CONTROLLED DRUG DELIVERY; FORECASTING; LEAST SQUARES APPROXIMATIONS; NETWORK ARCHITECTURE; RADIAL BASIS FUNCTION NETWORKS; SUPPORT VECTOR MACHINES;

DESCRIPTORS; LEARNING STUDIES; MACHINE-LEARNING; PARTIAL LEAST-SQUARES; PEPTIDE SEQUENCES; ROC SCORE; SUPPORT VECTORS MACHINE; TRANSDERMAL; TRANSDERMAL PEPTIDE; VHSE DESCRIPTOR;

PEPTIDES;

HEPTAPEPTIDE;

ACCURACY; AMINO ACID SEQUENCE; ANIMAL TISSUE; ARTICLE; ARTIFICIAL NEURAL NETWORK; CONTROLLED STUDY; DRUG DELIVERY SYSTEM; DRUG PENETRATION; INTERMETHOD COMPARISON; KERNEL METHOD; MACHINE LEARNING; MALE; NONHUMAN; PARTIAL LEAST SQUARES REGRESSION; PEPTIDE ANALYSIS; PHAGE DISPLAY; PREDICTION; PRIORITY JOURNAL; RAT; RECEIVER OPERATING CHARACTERISTIC; SCORING SYSTEM; SENSITIVITY AND SPECIFICITY; SKIN PERMEABILITY; SUPPORT VECTOR MACHINE; VALIDATION PROCESS;

EID: 79956220950     PISSN: 0920654X     EISSN: 15734951     Source Type: Journal    
DOI: 10.1007/s10822-011-9424-2     Document Type: Article

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