{'fraction_non_alphanumeric': 0.013, 'fraction_numerical': 0.0, 'mean_word_length': 5.718120805369128, '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}}
Kernel Learning Framework for Cancer Subtype Analysis with Multi-omics Data Integration

Recent advances in Multiple Kernel Learning (MKL) and unsupervised clustering methods have each enabled large-scale analysis of integrated multiomics data for cancer subtyping. However, efforts to combine the advantages of a kernel approach with the flexibility of unsupervised methods have not progressed due to the underconstrained nature of the problem. In this paper, we present a novel approach to solving this problem based on methods for Unsupervised Multiple Kernel Learning (UMKL). We present the possibility of constraining the problem with available data on clinically determined subtypes for cancer patients. We successfully demonstrate that modifying the constraints of the UMKL problem to include conditions for sparse clinical labelling data is tractable and thus presents a robust alternative for cancer subtype analysis by reducing it to an efficient alternating quadratic optimization problem.