Abstract
BackgroundThe use of predictive gene signatures to assist clinical decision is becoming more and more important. Deep learning has a huge potential in the prediction of phenotype from gene expression profiles. However, neural networks are viewed as black boxes, where accurate predictions are provided without any explanation. The requirements for these models to become interpretable are increasing, especially in the medical field.ResultsWe focus on explaining the predictions of a deep neural network model built from gene expression data. The most important neurons and genes influencing the predictions are identified and linked to biological knowledge. Our experiments on cancer prediction show that: (1) deep learning approach outperforms classical machine learning methods on large training sets; (2) our approach produces interpretations more coherent with biology than the state-of-the-art based approaches; (3) we can provide a comprehensive explanation of the predictions for biologists and physicians.ConclusionWe propose an original approach for biological interpretation of deep learning models for phenotype prediction from gene expression data. Since the model can find relationships between the phenotype and gene expression, we may assume that there is a link between the identified genes and the phenotype. The interpretation can, therefore, lead to new biological hypotheses to be investigated by biologists.
Highlights
The use of predictive gene signatures to assist clinical decision is becoming more and more important
In this paper, we propose an original approach for biological interpretation of deep learning models for phenotype prediction from gene expression data
These neurons are associated with a list of genes and the corresponding biological knowledge (GO, Kyoto Encyclopedia of Genes and Genomes (KEGG), and Disease ontology annotation lite (DOLite))
Summary
The use of predictive gene signatures to assist clinical decision is becoming more and more important. Deep learning has a huge potential in the prediction of phenotype from gene expression profiles. Neural networks are viewed as black boxes, where accurate predictions are provided without any explanation. The requirements for these models to become interpretable are increasing, especially in the medical field. The use of classifiers, constructed from gene expression profiles in clinical research to assist decision making, is Hanczar et al BMC Bioinformatics (2020) 21:501 becoming more and more important. Machine learning methods including support vector machine, random forest and boosting are among the main tools used in making biological discoveries from the huge amount of available gene expression data [1]
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