{'fraction_non_alphanumeric': 0.014848289218850872, 'fraction_numerical': 0.001936733376371853, 'mean_word_length': 5.595744680851064, '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}}
Database Mining in the Human Genome Initiative

The Human Genome Initiative is an international research program for the creation of detailed genetic and physical maps of the human genome. Genome research projects generate enormous quantities of data. Database mining is the process of finding and extracting useful information from raw datasets. Computational genomics has identified a classification of three successive levels for the management and analysis of genetic data in scientific databases: Genomics. 1. Gene expression. 2. Proteomics. 3. Genome database mining is the identification of the protein-encoding regions of a genome and the assignment of functions to these genes on the basis of sequence similarity homologies against other genes of known function. Gene expression database mining is the identification of intrinsic patterns and relationships in transcriptional expression data generated by large-scale gene expression experiments. Proteome database mining is the identification of intrinsic patterns and relationships in translational expression data generated by large-scale proteomics experiments. Improvements in genome, gene expression and proteome database mining algorithms will enable the prediction of protein function in the context of higher order processes such as the regulation of gene expression, metabolic pathways and signalling cascades. Thus, the final objective of such higher-level functional analysis will be the elucidation of high-resolution structural and functional maps of the human genome. Contents