scispace - formally typeset
C

Chris Sander

Researcher at Harvard University

Publications -  730
Citations -  273726

Chris Sander is an academic researcher from Harvard University. The author has contributed to research in topics: Large Hadron Collider & Protein structure. The author has an hindex of 178, co-authored 713 publications receiving 233287 citations. Previous affiliations of Chris Sander include Purdue University & University of Leeds.

Papers
More filters
Journal ArticleDOI

Updated catalogue of homologues to human disease-related proteins in the yeast genome1

TL;DR: Four of them are particularly interesting, since the yeast sequence is the most phylogenetically distant member of the protein family, including proteins related to diseases such as phenylketonuria, lupus erythematosus, Norum and fish eye disease and Wiskott‐Aldrich syndrome.
Journal ArticleDOI

Search for Dark Matter Produced in Association with a Dark Higgs Boson Decaying into W^{±}W^{∓} or ZZ in Fully Hadronic Final States from sqrt[s]=13 TeV pp Collisions Recorded with the ATLAS Detector.

Georges Aad, +2938 more
TL;DR: An uncharted signature of dark matter particles produced in association with VV=W^{±}W^{∓} or ZZ pairs from a decay of a dark Higgs boson s is searched for using 139 fb^{-1} of pp collisions recorded by the ATLAS detector at a center-of-mass energy of 13 TeV.
Journal ArticleDOI

Erratum to: Search for diboson resonances in hadronic final states in 139 fb −1 of pp collisions at s = 13 TeV with the ATLAS detector (Journal of High Energy Physics, (2019), 2019, 9, (91), 10.1007/JHEP09(2019)091)

Georges Aad, +3001 more
TL;DR: A mistake was identified for the paper [1] in the treatment of the radion cross-sections, which resulted in multiple changes.
Proceedings ArticleDOI

Abstract 3302: The molecular landscape of oncogenic signaling pathways in The Cancer Genome Atlas

TL;DR: This work delineates the full landscape of oncogenic alterations in mitogenic signaling pathways across cancer, and the pathway templates as well as the richly annotated data set that it provides will constitute an invaluable public resource for future use by the cancer genomics and precision oncology communities.
Journal ArticleDOI

netboxr: Automated discovery of biological process modules by network analysis in R

TL;DR: The netboxr package is developed, which makes use of the NetBox algorithm to identify candidate cancer-related functional modules using a data-driven, network-based approach that combines prior knowledge with a network clustering algorithm, obviating the need for and the limitation of independently curated functionally labeled gene sets.