{'fraction_non_alphanumeric': 0.016194331983805668, 'fraction_numerical': 0.0, 'mean_word_length': 6.272058823529412, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
Neural-network design applied to protein-secondary-structure predictions.

The success of neural networks is often limited by a sparse database of training examples, deficient neural-network architectures, and nonglobal optimization of the network variables. The convolution of these three problems has curtailed the application of network models to protein-structure predictions, where homology modeling or information theory approaches are considered better controlled alternatives. This paper introduces our broad objective of disentangling the three degrading features of neural networks cited above, beginning with improved designs of network architectures used in the prediction of protein secondary structure. This work demonstrates that network architecture design considerations greatly improve generalization and more efficiently extract complex sequence-structure relationships from the existing database, as compared to arbitrary architectures with the same size input window.