Foreseeing Survival Through ‘Fuzzy Intelligence’: A Cognitively-Inspired Incremental Learning Based de novo Model for Breast Cancer Prognosis by Multi-Omics Data Fusion

Abstract

High-precision breast cancer prognosis is crucial for early disease identification, avoiding hazardous side-effects of unnecessary therapies, and decreasing mortality rates through personalized and tailored treatment regimens. However, designing a prognosis model continues to be challenging, given the intricate relationship between distinct genetic attributes, varied clinical results of drug therapies, the noisy nature of gene expressions, and the high-class imbalance seen in multimodal cancer data. Furthermore, because labeled omics data collection is costly and requires highly-trained experts, the data available is very limited. This makes the design of the conventional machine and deep learning models incredibly challenging as they require large quantities of data for learning the underlying intricate patterns and would otherwise overfit, decreasing model precision. Moreover, all present models suffer from a ‘closed world assumption.’ These models, once trained, cannot be updated in real-time (when more omics data is available in the future) without a complete re-training. The present study is the first to introduce the ‘Fuzzy’ way towards Breast cancer prognosis, framing the task as an incremental learning problem. The proposed approach allows the model to continually update its learned feature space on a non-stationary multimodal data stream emulating the human brain’s remarkable quality to learn over time. We demonstrate the model’s ability to learn complex relationships between different multimodal attributes, training on severely imbalanced and limited data by mapping it to a high-dimensional ‘fused’ feature space. The proposed model surpasses state-of-the-art machine learning (ML) models significantly. These results suggest that prediction through ‘fuzzy intelligence’ is a promising approach towards breast cancer prognosis.