Exploring combinations of dimensionality reduction, transfer learning, and regularization methods for predicting binary phenotypes with transcriptomic data
Authors/Creators
- 1. Department of Medical Oncology, University Medical Center Groningen, University of Groningen, Groningen, the Netherlands
Description
This repository contains supplementary materials for the research paper "Exploring combinations of dimensionality reduction, transfer learning, and regularization methods for predicting binary phenotypes with transcriptomic data." The materials are organized into two distinct folders:
- supplementary_code: source code to reproduce the analysis
AE network training
AVAE network training
phenotype data collection
predictive modeling pipeline
- supplementary_data: networks and datasets used in this study
01: trained AE network
02: trained AVAE network
03: trained c-ICA network
04: 30 transcriptomic datasets with all representations and phenotypes
05: dataset predictive performances and significance
06: robustness analysis
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