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A deep learning framework for predicting human essential genes from population and functional genomic data
Being able to predict essential genes intolerant to loss-of-function (LOF) mutations can dramatically improve our ability to identify genes associated with genetic disorders. Numerous computational methods have recently been developed to predict human essential genes from population genomic data; however, the existing methods have limited power in pinpointing short essential genes due to the sparsity of polymorphisms in the human genome. Here we present an evolution-based deep learning model,doi:10.1101/2021.12.21.473690 fatcat:iwtsy7zpfzcytmyyjyrzm2a3vq