Cooperative learning for multiview analysis

TL;DR

This paper introduces cooperative learning, a flexible supervised learning method for multiview data that combines predictions with an agreement penalty, improving predictive accuracy especially in multiomics applications.

Contribution

It presents a novel cooperative learning framework that adaptively fuses multiple data views using various fitting mechanisms, including regularized linear regression.

Findings

Achieves higher predictive accuracy on simulated data.

Improves labor onset prediction in multiomics data.

Flexible framework adaptable to different data modalities.

Abstract

We propose a new method for supervised learning with multiple sets of features ("views"). The multiview problem is especially important in biology and medicine, where "-omics" data such as genomics, proteomics and radiomics are measured on a common set of samples. Cooperative learning combines the usual squared error loss of predictions with an "agreement" penalty to encourage the predictions from different data views to agree. By varying the weight of the agreement penalty, we get a continuum of solutions that include the well-known early and late fusion approaches. Cooperative learning chooses the degree of agreement (or fusion) in an adaptive manner, using a validation set or cross-validation to estimate test set prediction error. One version of our fitting procedure is modular, where one can choose different fitting mechanisms (e.g. lasso, random forests, boosting, neural networks) appropriate for different data views. In the setting of cooperative regularized linear regression, the method combines the lasso penalty with the agreement penalty, yielding feature sparsity. The method can be especially powerful when the different data views share some underlying relationship in their signals that can be exploited to boost the signals. We show that cooperative learning achieves higher predictive accuracy on simulated data and a real multiomics example of labor onset prediction. Leveraging aligned signals and allowing flexible fitting mechanisms for different modalities, cooperative learning offers a powerful approach to multiomics data fusion.