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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Bibliographische Detailangaben
1. Verfasser: Charles Blatti (15292859) (author)
Weitere Verfasser: 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)
Veröffentlicht: 2025
Schlagworte:
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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