About this Biochemistry, Genetics and Molecular Biology article
Deep GONet: self-explainable deep neural network based on Gene Ontology for phenotype prediction from gene expression data by Victoria Bourgeais; Farida Zehraoui; Mohamed Ben Hamdoune; Blaise Hanczar is a Biochemistry, Genetics and Molecular Biology article available to read on EtoBox.
## Abstract ## Background With the rapid advancement of genomic sequencing techniques, massive production of gene expression data is becoming possible, which prompts the development of precision medicine. Deep learning is a promising approach for phenotype prediction (clinical diagnosis, prognosis, and drug response) based on gene expression profile. Existing deep learning models are usually considered as black-boxes that provide accurate predictions but are not interpretable. However, accuracy and interpretation are both essential for precision medicine. In addition, most models do not integrate the knowledge of the domain. Hence, making deep learning models interpretable for medical applications using prior biological knowledge is the main focus of this paper. ## Results In this paper, we propose a new self-explainable deep learning model, called Deep GONet, integrating the Gene Ontology into the hierarchical architecture of the neural network. This model is based on a fully-connected architecture constrained by the Gene Ontology annotations, such that each neuron represents a biological function. The experiments on cancer diagnosis datasets demonstrate that Deep GONet is both ea
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- Author
- Victoria Bourgeais; Farida Zehraoui; Mohamed Ben Hamdoune; Blaise Hanczar
- Publisher
- Springer Science and Business Media LLC
- Published
- 2021
- Language
- EN
- Field
- Biochemistry, Genetics and Molecular Biology (Life Sciences)