CONTRASTIVE MULTI-OMICS ASSOCIATION LEARNING FOR COMPLEX DISEASES

原文（中文）

A plurality of data pairs are created by matching an element from a first modality with an element from a second modality. Each element from the first modality and each element from the second modality are tokenized to obtain first modality tokens and second modality tokens. A model is trained based on the plurality of data pairs, the training comprising learning a first embedding from the first modality tokens via a first attention-based encoder for the first modality and a second embedding from the second modality tokens via a second attention-based encoder for the second modality, calculating a cosine similarity between the first embedding and the second embedding for each data pair and computing a loss between predicted items and ground truth based on the cosine similarity. The predicted items with a minimal loss are validated to obtain at least one candidate therapeutic.