{'fraction_non_alphanumeric': 0.014925373134328358, 'fraction_numerical': 0.0, 'mean_word_length': 5.390476190476191, '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}}
A Bayesian approach for protein classification

In this work, we propose a new approach for protein classification based on Bayesian classifiers. Our goal is to predict the functional family of novel protein sequences based on their motif composition. For this purpose, datasets extracted from Prosite, a curated protein family database, are used as training datasets. In the conducted experiments, the performance of our classifier is compared to other known data mining approaches. The computational results have shown that the proposed method outperforms the other ones and looks very promising for problems with characteristics similar to the problem addressed here.