Deep learning for single-molecule science

Nanotechnology

Volumn 28, Issue 42, 2017, Pages

  Albrecht, Tim ^a,c   Slabaugh, Gregory ^b   Alonso, Eduardo ^b   Al Arif, S M Masudur R ^b  

^a IMPERIAL COLLEGE LONDON   (United Kingdom)

^b CITY UNIVERSITY   (United Kingdom)

^c UNIVERSITY OF BIRMINGHAM   (United Kingdom)

Author keywords

data analysis; deep learning; machine learning; nanoscience; nanotechnology; single molecule

Indexed keywords

ARTIFICIAL INTELLIGENCE; DATA REDUCTION; DEEP LEARNING; DNA SEQUENCES; GENE ENCODING; IMAGE RECOGNITION; LEARNING SYSTEMS; MOLECULES; NANOSCIENCE; NANOTECHNOLOGY; NEURAL NETWORKS; SIGNAL TO NOISE RATIO; SPEECH RECOGNITION; TECHNOLOGY TRANSFER;

CONVOLUTIONAL NEURAL NETWORK; DATA EXPLORATION; HUMAN PERFORMANCE; INFORMATION CONTENTS; LATEST DEVELOPMENT; PRIORI KNOWLEDGE; SIGNAL CHARACTERISTIC; SINGLE MOLECULE;

DEEP NEURAL NETWORKS;

ARTICLE; CONVOLUTIONAL NEURAL NETWORK; DEEP LEARNING; DNA SEQUENCE; EXPECTATION; HUMAN; HUMAN EXPERIMENT; INTERPRETATION BIAS; PREDICTION; SIGNAL NOISE RATIO; SPEECH;

EID: 85030178612     PISSN: 09574484     EISSN: 13616528     Source Type: Journal    
DOI: 10.1088/1361-6528/aa8334     Document Type: Review

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