Supplementary File 4 from Bayesian Machine Learning Enables Identification of Transcriptional Network Disruptions Associated with Drug-Resistant Prostate Cancer

Supplementary File 4 from Bayesian Machine Learning Enables Identification of Transcriptional Network Disruptions Associated with Drug-Resistant Prostate Cancer

<p>Excel file with regulon edges and ChIP-seq enrichment scores for TraRe, GRNboost2 and ARACNE-AP</p>

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Détails bibliographiques
Auteur principal:	Charles Blatti (15292859) (author)
Autres auteurs:	Jesús de la Fuente (15292862) (author), Huanyao Gao (14962434) (author), Irene Marín-Goñi (15292865) (author), Zikun Chen (15292868) (author), Sihai D. Zhao (15292871) (author), Winston Tan (15035292) (author), Richard Weinshilboum (15292874) (author), Krishna R. Kalari (15099627) (author), Liewei Wang (14962464) (author), Mikel Hernaez (15292877) (author)
Publié:	2025
Sujets:	Cancer Molecular and Cellular Biology Therapeutic Research and Development Methods and Technology Computational Methods Artificial intelligence & machine learning Drug Resistance Novel mechanisms Regulation of gene expression in drug resistance Gene Regulation Genitourinary Cancers Prostate cancer Systems Biology
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