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An unsolved fundamental problem in biology and ecology is to predictobservable traits (phenotypes) from a new genetic constitution (genotype) of anorganism under environmental perturbations (e.g., drug treatment). Theemergence of multiple omics data provides new opportunities but imposes greatchallenges in the predictive modeling of genotype-phenotype associations.Firstly, the high-dimensionality of genomics data and the lack of labeled dataoften make the existing supervised learning techniques less successful.Secondly, it is a challenging task to integrate heterogeneous omics data fromdifferent resources. Finally, the information transmission from DNA tophenotype involves multiple intermediate levels of RNA, protein, metabolite,etc. The higher-level features (e.g., gene expression) usually have strongerdiscriminative power than the lower level features (e.g., somatic mutation). Toaddress above issues, we proposed a novel Cross-LEvel Information Transmissionnetwork (CLEIT) framework. CLEIT aims to explicitly model the asymmetricalmulti-level organization of the biological system. Inspired by domainadaptation, CLEIT first learns the latent representation of high-level domainthen uses it as ground-truth embedding to improve the representation learningof the low-level domain in the form of contrastive loss. In addition, we adopta pre-training-fine-tuning approach to leveraging the unlabeled heterogeneousomics data to improve the generalizability of CLEIT. We demonstrate theeffectiveness and performance boost of CLEIT in predicting anti-cancer drugsensitivity from somatic mutations via the assistance of gene expressions whencompared with state-of-the-art methods.

A Cross-Level Information Transmission Network for Predicting Phenotype from New Genotype: Application to Cancer Precision Medicine