Improved Prediction of Smoking Status via Isoform-Aware RNA-seq Deep Learning Models

Predictive models based on gene expression are already a part of medical decision making for selected situations such as early breast cancer treatment. Most of these models are based on measures that do not capture critical aspects of gene splicing, butwith RNA sequencing it is possible to capture some of these aspects of alternative splicing and use them to improve clinical predictions. Building on previous models to predict cigarette smoking status, we show that measures of alternative splicing significantly improve the accuracy of these predictive models. RNA sequencing data were generated from whole blood samples from 2,655 subjects enrolled in the COPDGene Study. Raw read data are submitted to dbGaP. **RAW DATA will be uploaded to dbGAP**