{'fraction_non_alphanumeric': 0.015358361774744027, 'fraction_numerical': 0.0, 'mean_word_length': 5.450549450549451, '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}}
Hybrid models of uncertainty in protein topology prediction

Predicting protein structure is an important problem in molecular biology, and one that has attracted much attention. It is also a difficult problem, since the available data are incomplete and pervaded with uncertainty. This paper describes models for the prediction of an intermediate level of protein structure known as the topology of the protein. The models handle uncertainty explicitly, making use of probability, possibility, and evidence theories singly and in combination to handle different aspects of the problem.