{'fraction_non_alphanumeric': 0.024285714285714285, 'fraction_numerical': 0.0, 'mean_word_length': 5.397260273972603, '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}}
KATZMDA: Prediction of miRNA-Disease Associations Based on KATZ Model

MiRNAs are a kind of non-coding RNA molecules found in plants, animals, and various viruses. They have been proved to play an important role in multiple biological as well as physiological processes. Specifically, a growing number of studies have shown that miRNAs have close relationships with many diseases, and thus the exploration of the relationships between miRNAs and diseases is of great significance in disease research. Although traditional experimental methods can obtain the associations between miRNAs and diseases, the amount of data obtained is far from enough for us to fully understand the associations between them. Besides, traditional experiments are generally time-consuming and expensive. Therefore, it is necessary to propose efficient computational methods to predict miRNA-disease associations. In this paper, we develop a novel computational method based on KATZ model to predict MiRNA-Disease Associations (KATZMDA) by integrating multiple data sources. To evaluate the performance of KATZMDA, four classical methods are used to compare with our methods (WBSMDA, HGIMDA, RKNNMDA, MCMDA). The experimental results demonstrate that our method can be used as an effective tool to identify disease-related miRNAs. In addition, case studies of three common diseases further verify the utility of our method.