MOLI: multi-omics late integration with deep neural networks for drug response prediction
- 1. School of Computing Science, Simon Fraser University, Burnaby, BC, Canada Vancouver Prostate Centre, Vancouver, BC, Canada
- 2. International Research Training Group Computational Methods for the Analysis of the Diversity and Dynamics of Genomes and Genome Informatics, Faculty of Technology and Center for Biotechnology, Bielefeld University, Germany
- 3. Vancouver Prostate Centre, Vancouver, BC, Canada Department of Urologic Sciences, University of British Columbia, Vancouver, BC, Canada
Description
Harmonized data used in "MOLI: multi-omics late integration with deep neural networks for drug response prediction", 2019, Bioinformatics https://academic.oup.com/bioinformatics/article/35/14/i501/5529255.
CNA.tar.gz contains CNA profiles with non-integer estimates of copy number, e.g. log-ratios. Please use binarized CNA profiles (CNA_binary.tar.gz) to replicate the results described in the paper.
All raw data were obtained from open sources:
- https://www.cancerrxgene.org/
- ArrayExpress https://www.ebi.ac.uk/arrayexpress/
- Firehose Broad GDAC http://gdac.broadinstitute.org/runs/stddata__2016_01_28/data/
- Supplementary of Gao et al., 2015 https://www.nature.com/articles/nm.3954
Gene symbols were mapped to Entrez Gene IDs. Data preprocessing is described in detail in supplementary materials. The code is available at https://github.com/hosseinshn/MOLI/tree/master/preprocessing_scr.
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Additional details
Related works
- Is supplement to
- Journal article: 10.1093/bioinformatics/btz318 (DOI)
- Preprint: 10.1101/531327 (DOI)