{'fraction_non_alphanumeric': 0.013837638376383764, 'fraction_numerical': 0.0, 'mean_word_length': 6.045454545454546, '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}}
Predicing Yeast Synthetic Lethal Genetic Interactions Using Short Polypeptide Clusters

Synthetic lethal genetic interactions (SLGI) among proteins have been widely used to define functional relationships between proteins and pathways. However, the molecular mechanism of synthetic lethal genetic interactions is still unclear. In this study we used the clusters of short polypeptide sequences, which are typically shorter than the classically defined protein domains, to characterize the functionalities of proteins. We developed a framework to identify significant short polypeptide clusters from yeast protein sequences. We then used these short polypeptide clusters as features to predict SLGIs. Both cross-validation and evaluation on experimental data sets showed that the short polypeptide clusters based approach is superior to the previous protein domain based approach. The short polypeptide clusters based approach provides significantly higher coverage for predicting SLGIs. Moreover, the short polypeptide clusters based approach produced less false positive predictions.