Random forests for genomic data analysis.

TL;DR: Major tumour regressions are reported in a subset of patients with metastatic melanoma treated with an anti-CTLA4 antibody and radiation and reproduced this effect in mouse models, showing that PD-L1 on melanoma cells allows tumours to escape anti- NCTLA4-based therapy, and the combination of radiation, anti- CTLA4 and anti-PD-L 1 promotes response and immunity through distinct mechanisms.

TL;DR: It is demonstrated that prolonged interferon signaling orchestrates PDL1-dependent and PDL 1-independent resistance to immune checkpoint blockade (ICB) and to combinations such as radiation plus anti-CTLA4, and biomarkers for interferons-driven resistance associate with clinical progression after anti-PD1 therapy.

TL;DR: The whole blood tuberculosis risk signature prospectively identified people at risk of developing active tuberculosis, opening the possibility for targeted intervention to prevent the disease.

TL;DR: Primary human and/or mouse BrCa analysis support the role of antiviral/NOTCH3 pathways in NOTCH signaling and stroma-mediated resistance, which is abrogated by combination therapy with gamma secretase inhibitors.

TL;DR: The methodology used to construct tree structured rules is the focus of a monograph as mentioned in this paper, covering the use of trees as a data analysis method, and in a more mathematical framework, proving some of their fundamental properties.

TL;DR: random forests are proposed, which add an additional layer of randomness to bagging and are robust against overfitting, and the randomForest package provides an R interface to the Fortran programs by Breiman and Cutler.

TL;DR: This article gives an introduction to the subject of classification and regression trees by reviewing some widely available algorithms and comparing their capabilities, strengths, and weakness in two examples.