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Prediction of response to anti-cancer drugs becomes robust via network integration of molecular data
Marcela Franco, Ashwini Jeggari, Sylvain Peuget, Franziska Böttger, Galina Selivanova, View ORCID ProfileAndrey Alexeyenko
doi: https://doi.org/10.1101/301838
Marcela Franco
1Department of Microbiology, Tumor and Cell Biology (MTC), Karolinska Institutet, Stockholm, Sweden.
Ashwini Jeggari
2Department of Cell and Molecular Biology, Karolinska Institutet, 171 77 Stockholm, Sweden.
Sylvain Peuget
1Department of Microbiology, Tumor and Cell Biology (MTC), Karolinska Institutet, Stockholm, Sweden.
Franziska Böttger
1Department of Microbiology, Tumor and Cell Biology (MTC), Karolinska Institutet, Stockholm, Sweden.
3OncoProteomics Laboratory, Department of Medical Oncology, VU University Medical Center, 1081HV Amsterdam, The Netherlands
Galina Selivanova
1Department of Microbiology, Tumor and Cell Biology (MTC), Karolinska Institutet, Stockholm, Sweden.
Andrey Alexeyenko
1Department of Microbiology, Tumor and Cell Biology (MTC), Karolinska Institutet, Stockholm, Sweden.
4National Bioinformatics Infrastructure Sweden, Science for Life Laboratory, Box 1031, 17121, Solna, Sweden
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Posted April 16, 2018.
Prediction of response to anti-cancer drugs becomes robust via network integration of molecular data
Marcela Franco, Ashwini Jeggari, Sylvain Peuget, Franziska Böttger, Galina Selivanova, Andrey Alexeyenko
bioRxiv 301838; doi: https://doi.org/10.1101/301838
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