Christoph Hafemeister

TL;DR: This study establishes benchmarks for gene essentiality prediction, presents a community resource for future comparison with this benchmark, and provides insights into factors influencing the ability to predict gene essentiality from functional genetic screens.

TL;DR: It is demonstrated that appropriate reduction of model complexity can result in substantial improvements both in classification performance and running time and a Bayesian approach to parameter estimation and inference helps to cope with the short, but highly multivariate time-courses.

TL;DR: The DREAM Single-Cell Transcriptomics challenge focused on the spatial reconstruction of cells from the Drosophila embryo from scRNAseq data, leveraging as silver standard, genes with in situ hybridization data from the Berkeley Droseophila Transcription Network Project reference atlas.

TL;DR: This work develops and demonstrates a web-based framework for coordinating visualization and exploration of expression data, network models and gene-binding data (ChIP-seq), and designs an efficient querying model to support interactive analysis of the data.

TL;DR: A network inference approach designed to identify sRNA-mediated regulation of transcript levels using existing transcriptional data sets and prior knowledge to infer sRNA regulons using the authors' network inference tool, the Inferelator, which produces genome-wide gene regulatory networks that include contributions by both transcription factors and sRNAs.