DeepPheno: Predicting single gene knockout phenotypes
Predicting the phenotypes resulting from molecular perturbations is one of the key challenges in genetics. Both forward and reverse genetic screen are employed to identify the molecular mechanisms underlying phenotypes and disease, and these resulted in a large number of genotype-phenotype association being available for humans and model organisms.
Predicting the phenotypes resulting from molecular perturbations is one of the key challenges in genetics. Both forward and reverse genetic screen are employed to identify the molecular mechanisms underlying phenotypes and disease, and these resulted in a large number of genotype-phenotype association being available for humans and model organisms.
Combined with recent advances in machine learning, it may now be possible to predict human phenotypes resulting from particular molecular aberrations.
We developed DeepPheno, a method for predicting
the phenotypes resulting from complete loss-of-function in single genes. DeepPheno uses the functional annotations with
gene products to predict the phenotypes resulting from a
loss-of-function; additionally, we employ a two-step procedure in which we predict these functions first and then predict
phenotypes.
This allows us to predict phenotypes associated with any known protein-coding gene. We evaluate our approach using evaluation metrics established by the CAFA challenge and compare with top performing CAFA 2 methods. Our method achieves an F_max of 0.46 which is a significant improvement over state-of-the-art F_max of 0.36.
Furthermore, we show that predictions generated by DeepPheno are applicable to predicting gene-disease associations based on comparing phenotypes, and 60% of predictions made by DeepPheno interact with a gene that is already associated with the predicted phenotype.
