841164
doi
10.5281/zenodo.841164
oai:zenodo.org:841164
user-precise
user-eu
Manica, Matteo
IBM Research Zurich / ETH Zurich
Rodriguez Martinez, Maria
IBM Research Zurich
DeepGRN: Deciphering gene deregulation in cancer development using deep learning
Mathis, Roland
IBM Research Zurich
info:eu-repo/semantics/openAccess
Creative Commons Attribution Non Commercial No Derivatives 4.0 International
https://creativecommons.org/licenses/by-nc-nd/4.0/legalcode
<p>Understanding gene regulatory networks (GRNs) is key towards deciphering gene deregulation in cancer development. We are building on efforts to find tissue-specific and disease-specific gene regulatory networks. While large efforts have been devoted to create context specific GRNs for a range of tissues as well as diseases, most currently available cancer GRNs are inferred from unmatched datasets for which only the diseased tissue is available. Our goal is to find disease-specific changes of gene regulation using matched normal and tumor patient data in a cohort-specific fashion.</p>
Zenodo
2017-07-22
info:eu-repo/semantics/conferencePoster
841163
user-precise
user-eu
award_title=PERSONALIZED ENGINE FOR CANCER INTEGRATIVE STUDY AND EVALUATION; award_number=668858; award_identifiers_scheme=url; award_identifiers_identifier=https://cordis.europa.eu/projects/668858; funder_id=00k4n6c32; funder_name=European Commission;
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md5:f39e025f9d237803d9355a5d45fefba9
https://zenodo.org/records/841164/files/ISMB-2017-DeepGRN.pdf
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10.5281/zenodo.841163
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