David L. Lahr

TL;DR: The expanded CMap is reported, made possible by a new, low-cost, high-throughput reduced representation expression profiling method that is shown to be highly reproducible, comparable to RNA sequencing, and suitable for computational inference of the expression levels of 81% of non-measured transcripts.

TL;DR: A new, low-cost, high throughput reduced representation expression profiling method, L1000, is shown to be highly reproducible, comparable to RNA sequencing, and suitable for computational inference of the expression levels of 81% of non-measured transcripts.

TL;DR: Among these impactful variants are rare somatic, clinically actionable variants including EGFR S645C, ARAF S214C and S214F, ERBB2 S418T, and multiple BRAF variants, demonstrating that rare mutations can be functionally important in cancer.

TL;DR: This work shows that mitigating off-target effects is feasible in these datasets via computational methodologies to produce a consensus gene signature (CGS) and compared RNAi technology to clustered regularly interspaced short palindromic repeat (CRISPR)-based knockout by analysis of 373 single guide RNAs (sgRNAs) to show that the on-target efficacies are comparable, but CRISPR technology is far less susceptible to systematic off- target effects.

TL;DR: In this article, a conductometric chemical sensor architecture based upon an assembly of parallel metal oxide nanotubes is described. But the authors do not consider the use of metal contacts for resistance measurements at the ends of the tubes.