{'fraction_non_alphanumeric': 0.007700770077007701, 'fraction_numerical': 0.0, 'mean_word_length': 6.222222222222222, '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}}
Building phylogenetic trees from frequent subgraph mining techniques on reaction hypergraphs

Huge advances in experimental techniques have resulted in increasing amounts of biological network data being made available. Different kinds metabolic networks are just some of these. Analyzing the network topology of these metabolic networks across taxa can uncover important biological information that is independent of other currently available biological information. This study explores topological similarities between metabolic networks of different taxa to build phylogenetic trees. Similarities between graphs were determined using frequent subgraph mining techniques. Phylogenetic trees were then built based on the frequent subgraphs among reaction hypergraphs of different taxa. Experimental results show phylogenetic trees that bear a striking resemblance to those obtained from sequence comparisons.