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Fig. 1 | BMC Bioinformatics

Fig. 1

From: An unsupervised deep learning framework for predicting human essential genes from population and functional genomic data

Fig. 1

Overview of the background information, motivation and methods behind DeepLOF. a Background: Introduction to loss of function (LOF) mutations, essential genes versus nonessential genes as well as LOF intolerance versus LOF tolerance. Motivation: Determining which genes are LOF intolerant can aid with discovery of human disease genes. b Motivation: The limitation of current population genomics-based methods for determining LOF intolerance is they are underpowered when predicting genes that are short in length. c Simple overview of the concept behind DeepLOF. Methods: Our method integrates a population genomics-based approach with a functional genomics approach, providing unparalleled ability to predict LOF intolerance, particularly in short genes. DeepLOF does not require human-labeled training data and thus, may not suffer from label leakage

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BMC Bioinformatics

ISSN: 1471-2105

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