{'fraction_non_alphanumeric': 0.0199203187250996, 'fraction_numerical': 0.0, 'mean_word_length': 5.389830508474576, '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}}
A Neural Network Model for Maximizing Prediction Accuracy in Haplotype Tagging SNP Selection

Due to the tremendous number of single nucleotide polymorphisms (SNPs), there is a clear need to expedite genotyping by considering only a subset of all SNPs called haplotype tagging SNPs (htSNPs). Recently, the approach that selects htSNPs by maximizing their prediction accuracy has demonstrated very promising results. Here we propose a new prediction system for htSNP selection based on neural network models. We applied our system to three public data sets, and compared its prediction performance to that of two state-of-the-art prediction rules. The results demonstrate that our system consistently outperforms compared methods with robust performance.