A Multiattribute Gaussian Graphical Model for Inferring Multiscale Regulatory Networks: An Application in Breast Cancer

2018 ◽  
pp. 143-160 ◽  
Author(s):  
Julien Chiquet ◽  
Guillem Rigaill ◽  
Martina Sundqvist
Keyword(s):  
Breast Cancer ◽  
Regulatory Networks ◽  
Graphical Model ◽  
Gaussian Graphical Model

scholarly journals KBoost: a new method to infer gene regulatory networks from gene expression data

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Luis F. Iglesias-Martinez ◽  
Barbara De Kegel ◽  
Walter Kolch
Keyword(s):  
Breast Cancer ◽  
Gene Regulatory Networks ◽  
Regulatory Networks ◽  
Bayesian Model Averaging ◽  
Model Averaging ◽  
R Package ◽  
Breast Cancer Patients ◽  
Sources Of Information ◽  
Cancer Subtypes ◽  
Gene Regulatory

AbstractReconstructing gene regulatory networks is crucial to understand biological processes and holds potential for developing personalized treatment. Yet, it is still an open problem as state-of-the-art algorithms are often not able to process large amounts of data within reasonable time. Furthermore, many of the existing methods predict numerous false positives and have limited capabilities to integrate other sources of information, such as previously known interactions. Here we introduce KBoost, an algorithm that uses kernel PCA regression, boosting and Bayesian model averaging for fast and accurate reconstruction of gene regulatory networks. We have benchmarked KBoost against other high performing algorithms using three different datasets. The results show that our method compares favorably to other methods across datasets. We have also applied KBoost to a large cohort of close to 2000 breast cancer patients and 24,000 genes in less than 2 h on standard hardware. Our results show that molecularly defined breast cancer subtypes also feature differences in their GRNs. An implementation of KBoost in the form of an R package is available at: https://github.com/Luisiglm/KBoost and as a Bioconductor software package.

scholarly journals Systematic dissection of transcriptional regulatory networks by genome-scale and single-cell CRISPR screens

2021 ◽  
Vol 7 (27) ◽  
pp. eabf5733
Author(s):  
Rui Lopes ◽  
Kathleen Sprouffske ◽  
Caibin Sheng ◽  
Esther C. H. Uijttewaal ◽  
Adriana Emma Wesdorp ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Regulatory Networks ◽  
Essential Role ◽  
Regulatory Elements ◽  
Transcriptional Regulatory Networks ◽  
Transcriptional Regulatory Elements ◽  
Transcriptional Regulatory ◽  
Functional Relevance ◽  
Genome Scale ◽  
Transcription Program

Millions of putative transcriptional regulatory elements (TREs) have been cataloged in the human genome, yet their functional relevance in specific pathophysiological settings remains to be determined. This is critical to understand how oncogenic transcription factors (TFs) engage specific TREs to impose transcriptional programs underlying malignant phenotypes. Here, we combine cutting edge CRISPR screens and epigenomic profiling to functionally survey ≈15,000 TREs engaged by estrogen receptor (ER). We show that ER exerts its oncogenic role in breast cancer by engaging TREs enriched in GATA3, TFAP2C, and H3K27Ac signal. These TREs control critical downstream TFs, among which TFAP2C plays an essential role in ER-driven cell proliferation. Together, our work reveals novel insights into a critical oncogenic transcription program and provides a framework to map regulatory networks, enabling to dissect the function of the noncoding genome of cancer cells.

scholarly journals The Evolving Role of Ferroptosis in Breast Cancer: Translational Implications Present and Future

Cancers ◽  
2021 ◽  
Vol 13 (18) ◽  
pp. 4576
Author(s):  
Hung-Yu Lin ◽  
Hui-Wen Ho ◽  
Yen-Hsiang Chang ◽  
Chun-Jui Wei ◽  
Pei-Yi Chu
Keyword(s):  
Breast Cancer ◽  
Cell Death ◽  
Regulatory Networks ◽  
Molecular Mechanisms ◽  
Drug Resistant ◽  
Therapeutic Targeting ◽  
The Past ◽  
Regulated Cell Death ◽  
Common Malignancy

Breast cancer (BC) is the most common malignancy among women worldwide. The discovery of regulated cell death processes has enabled advances in the treatment of BC. In the past decade, ferroptosis, a new form of iron-dependent regulated cell death caused by excessive lipid peroxidation has been implicated in the development and therapeutic responses of BC. Intriguingly, the induction of ferroptosis acts to suppress conventional therapy-resistant cells, and to potentiate the effects of immunotherapy. As such, pharmacological or genetic modulation targeting ferroptosis holds great potential for the treatment of drug-resistant cancers. In this review, we present a critical analysis of the current understanding of the molecular mechanisms and regulatory networks involved in ferroptosis, the potential physiological functions of ferroptosis in tumor suppression, its potential in therapeutic targeting, and explore recent advances in the development of therapeutic strategies for BC.

Sparse Gaussian graphical model estimation via alternating minimization

Biometrika ◽  
2017 ◽  
Vol 104 (2) ◽  
pp. 379-395 ◽  
Author(s):  
Onkar Dalal ◽  
Bala Rajaratnam
Keyword(s):  
Graphical Model ◽  
Model Estimation ◽  
Alternating Minimization ◽  
Gaussian Graphical Model

Tensor-Based Gaussian Graphical Model

2021 ◽  
pp. 285-298
Author(s):  
Yipeng Liu ◽  
Jiani Liu ◽  
Zhen Long ◽  
Ce Zhu
Keyword(s):  
Graphical Model ◽  
Gaussian Graphical Model
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