{'fraction_non_alphanumeric': 0.01383399209486166, 'fraction_numerical': 0.003952569169960474, 'mean_word_length': 5.371069182389937, '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}}
Systematic assessment of high-throughput experimental data for reliable protein interactions using network topology

Current protein interaction detection via high-throughput experimental methods such as yeast-two-hybrid has been reported to be highly erroneous. This work introduces a novel measure called IRAP for assessing the reliability of protein interaction based on the underlying topology of the protein interaction network. A candidate protein interaction is considered to be reliable if it is involved in a closed loop in which the alternative path of interactions between the two interacting proteins is strong. We design an algorithm to compute the IRAP value for each interaction in a protein interaction network. Validation of IRAP as a measure for assessing the reliability of protein-protein interactions from conventional high-throughput experiments is performed. We devise a heuristic algorithm to compute IRAP that is able to achieve a 40% speedup in runtime while maintaining a 95% accuracy.