{'fraction_non_alphanumeric': 0.019890260631001373, 'fraction_numerical': 0.0, 'mean_word_length': 5.343478260869565, '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}}
Literature mining associations of diseases using gene ontology

Upon the coming of post-genomics era, the research on gene function becomes especially important. Updating of the curated databases is lack of timely. There is a lot of knowledge hidden in biomedicine literatures. With the ever increasing amount of biomedicine literatures, mining the relations automatically is very urgent. Modern research has found most diseases are genetic. The relations between diseases and gene functions are waiting to be mining. This paper proposes a system to mine relations between diseases with common gene functions in the literature with normalized Metathesaurus. First, a two-phase Conditional Random Fields (CRFs) is used to recognize the disease mentions and gene function mentions, including the location and identification. Then, the paper adapts the Disease Ontology (DO) and the Gene Ontology (GO) to annotate the diseases and gene functions recognized for normalization by computing the similarity between mentions and concepts. According to the similarities, the mentions are denoted as concepts and instances distinctively. Thirdly, the paper builds a network and measure relations between diseases by computing similarities between common sub-graphs. The experiments carried out on a corpus randomly selected by GoPubMed with disease and the three domains in GO. The performance shows a lot of hidden relations between diseases and gives an explanation.