Receive a weekly summary and discussion of the top papers of the week by leading researchers in the field.

In Scientific reports ; h5-index 158.0

For complex machine learning (ML) algorithms to gain widespread acceptance in decision making, we must be able to identify the features driving the predictions. Explainability models allow transparency of ML algorithms, however their reliability within high-dimensional data is unclear. To test the reliability of the explainability model SHapley Additive exPlanations (SHAP), we developed a convolutional neural network to predict tissue classification from Genotype-Tissue Expression (GTEx) RNA-seq data representing 16,651 samples from 47 tissues. Our classifier achieved an average F1 score of 96.1% on held-out GTEx samples. Using SHAP values, we identified the 2423 most discriminatory genes, of which 98.6% were also identified by differential expression analysis across all tissues. The SHAP genes reflected expected biological processes involved in tissue differentiation and function. Moreover, SHAP genes clustered tissue types with superior performance when compared to all genes, genes detected by differential expression analysis, or random genes. We demonstrate the utility and reliability of SHAP to explain a deep learning model and highlight the strengths of applying ML to transcriptome data.

Yap Melvyn, Johnston Rebecca L, Foley Helena, MacDonald Samual, Kondrashova Olga, Tran Khoa A, Nones Katia, Koufariotis Lambros T, Bean Cameron, Pearson John V, Trzaskowski Maciej, Waddell Nicola

2021-Jan-29