{'fraction_non_alphanumeric': 0.01899441340782123, 'fraction_numerical': 0.0, 'mean_word_length': 5.8396946564885495, '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}}
Qualitative Modeling Framework for Identification of Genetic Regulatory Networks

Many complex processes such as adaptation, development, differentiation, cell cycle etc. in biological systems involve multiple genes functioning in a hierarchical and highly interconnected structure. The advent of gene expression microarray technology in the recent years has resulted in high throughput data sets, the analysis of which holds the promise of identification of the nonlinear dynamic function of the biochemical regulatory networks. However, the precision and reliability of these quantitative data sets varies widely and most often permit only a coarsegrained analysis (up regulation, down regulation, no effect etc.). The conventional modeling approaches are not well suited for such coarsely quantized data sets and are likely to result in inconsistent, if not incorrect, network identification.