Multi-modal Genotype and Phenotype Mutual Learning to Enhance Single-Modal Input Based Longitudinal Outcome Prediction

In recent years, due to the advance of modern sensory devices, the collection of multiple biomedical data modalities such as imaging genetics has gotten feasible, and multimodal data analysis has attracted significant attention in bioinformatics. Although existing multimodal learning methods have shown superior ability in combining data from multiple sources, they are not directly applicable for many real-world biological and biomedical studies that suffer from missing data modalities due to the high expenses of collecting all modalities. Thus, in practice, usually, only a main modality containing a major ‘diagnostic signal’ is used for decision making as auxiliary modalities are not available. In addition, during the examination of a subject regarding a chronic disease (with longitudinal progression) in a visit, typically, two diagnosis-related questions are of main interest that are what their status currently is (diagnosis) and how it will change before their next visit (longitudinal outcome) if they maintain their disease trajectory and lifestyle. Accurate answers to these questions can distinguish vulnerable subjects and enable clinicians to start early treatments for them. In this paper, we propose a new adversarial mutual learning framework for longitudinal prediction of disease progression such that we properly leverage several modalities of data available in training set to develop a more accurate model using single-modal for prediction. Specifically, in our framework, a single-modal model (that utilizes the main modality) learns from a pretrained multimodal model (which takes both main and auxiliary modalities as input) in a mutual learning manner to 1) infer outcome-related representations of the auxiliary modalities based on its own representations for the main modality during adversarial training and 2) effectively combine them to predict the longitudinal outcome. We apply our new method to analyze the retinal imaging genetics for the early diagnosis of Age-related Macular Degeneration (AMD) disease in which we formulate prediction of longitudinal AMD progression outcome of subjects as a classification problem of simultaneously grading their current AMD severity as well as predicting their condition in their next visit with a preselected time duration between visits. Our experiments on the Age-Related Eye Disease Study (AREDS) dataset demonstrate the superiority of our model compared to baselines for simultaneously grading and predicting future AMD severity of subjects.