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Hybrid Network Model for “Deep Learning” of Chemical Data: Application to Antimicrobial Peptides
Author(s) -
Schneider Petra,
Müller Alex T.,
Gabernet Gisela,
Button Alexander L.,
Posselt Gernot,
Wessler Silja,
Hiss Jan A.,
Schneider Gisbert
Publication year - 2017
Publication title -
molecular informatics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.481
H-Index - 68
eISSN - 1868-1751
pISSN - 1868-1743
DOI - 10.1002/minf.201600011
Subject(s) - artificial intelligence , computer science , principal component analysis , feed forward , artificial neural network , pattern recognition (psychology) , dimensionality reduction , robustness (evolution) , deep learning , machine learning , curse of dimensionality , feedforward neural network , data mining , chemistry , engineering , biochemistry , control engineering , gene
We present a “deep” network architecture for chemical data analysis and classification together with a prospective proof‐of‐concept application. The model features a self‐organizing map (SOM) as the input layer of a feedforward neural network. The SOM converts molecular descriptors to a two‐dimensional image for further processing. We implemented lateral neuron inhibition for contrast enhancement. The model achieved improved classification accuracy and predictive robustness compared to feedforward network classifiers lacking the SOM layer. By nonlinear dimensionality reduction the networks extracted meaningful chemical features from the data and outperformed linear principal component analysis (PCA). The learning machine was trained on the sequence‐length independent recognition of antibacterial peptides and correctly predicted the killing activity of a synthetic test peptide against Staphylococcus aureus in an in vitro experiment.