Machine learning model finds genetic factors for heart disease broadinstitute NatureComms
, the resulting representations are useful for a variety of downstream tasks including phenotype prediction, modality translation, and genetic discovery. While we mainly apply our framework to integrate two modalities , we demonstrate that it can also be applied to three or more modalities in Supplementary Fig. We first demonstrate that supervised learning on cross-modal representations improves performance on phenotype prediction tasks.
Cross-modal embeddings allow for matching cardiac MRI and ECG test samples. We begin by verifying that our training methodology provides a latent space in which corresponding ECG and MRI pairs are nearby. Hence, even in the absence of one of the modalities, the cross-modal autoencoder provides a representation that is characteristic of all available modalities. In Fig.
Fig. 2: Improvement of phenotype prediction from cross-modal representations over unimodal representations or supervised learning from the original modalities.A t-SNE visualization of the cross-modal embeddings for the ECG and MRI samples demonstrates that the modality specifc embeddings are well-mixed, unlike the modality specific embeddings obtained from the unimodal autoencoders.
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