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Application of a Pruning Algorithm To Optimize Artificial Neural Networks for Pharmaceutical Fingerprinting
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What this work says
Outgoing claims: the papers this work cites, builds on, or argues with.
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SuperSAB: Fast adaptive back propagation with good scaling properties
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Basic liquid chromatography
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Efficient Partition of Learning Data Sets for Neural Network Training
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Pharmaceutical Fingerprinting: Evaluation of Neural Networks and Chemometric Techniques for Distinguishing among Same-Product Manufacturers
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Preprocessing of HPLC Trace Impurity Patterns by Wavelet Packets for Pharmaceutical Fingerprinting Using Artificial Neural Networks
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High-Low Chromatography: Estimating Impurities in HPLC Using a Pair of Sample Injections
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An FDA Laboratory Approach to Uncovering Potential Fraud in the Generic Drug Industry
What points at this work
Incoming claims: where this work appears in other papers’ reference lists and arguments.
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Artificial Neural Networks and Their use in Chemistry
inferred← citeshttps://doi.org/10.1002/chin.199845275openalex-snapshot
ChemInform Abstract: Application of a Pruning Algorithm to Optimize Artificial Neural Networks for Pharmaceutical Fingerprinting.
inferred← citeshttps://doi.org/10.1002/prot.20477openalex-snapshot
Rapid assessment of contact‐dependent secondary structure propensity: Relevance to amyloidogenic sequences
inferred← citeshttps://doi.org/10.1007/978-1-60327-101-1_10openalex-snapshot
Associative Neural Network
inferred← citeshttps://doi.org/10.1007/s00216-011-5155-4openalex-snapshot
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inferred← citeshttps://doi.org/10.1007/s10822-005-9013-3openalex-snapshot
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inferred← citeshttps://doi.org/10.1007/s11030-021-10237-zopenalex-snapshot
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inferred← citeshttps://doi.org/10.1016/j.bmc.2007.12.055openalex-snapshot
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Class Modeling Analysis of Heparin 1H NMR Spectral Data Using the Soft Independent Modeling of Class Analogy and Unequal Class Modeling Techniques
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Pharmaceutical Fingerprinting in Phase Space. 1. Construction of Phase Fingerprints
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Self-Configuring Radial Basis Function Neural Networks for Chemical Pattern Recognition
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Oscillatory Models for Biological Signal Processing and Pattern Recognition
inferred← citeshttps://doi.org/10.1109/wcica.2000.863357openalex-snapshot
Structure optimization strategy of neural networks - research on the pruning algorithm
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Big Data and Artificial Intelligence Modeling for Drug Discovery
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Artificial Neural Networks in Forensic Medicine (review)
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