{'fraction_non_alphanumeric': 0.017580144777662874, 'fraction_numerical': 0.0, 'mean_word_length': 5.865248226950355, '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}}
Constructing prototypes for classification using epigenetic and genetic analysis

Researchers seek to identify biological markers which accurately differentiate cancer subtypes and their severity from normal controls. One such biomarker, DNA methylation, has recently become more prevalent in genetic research studies in oncology. This project seeks to apply the innovative and adaptive machine learning methodology in case-based reasoning (CBR) to examine DNA methylation levels in breast cancer. Instead of relying on a generalized knowledge-base, CBR uses highly specific information extracted from similar cases which can also greatly expedite the process of finding a solution. Further, this can locate targeted biomarkers by reusing homogenous factors, or revising to locate novel biomarkers in highly heterogeneous samples. While locating these biomarkers, this project proposes to use CBR to classify samples, predict prognoses and determine survival factors.