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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.

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A multi-method approach for proteomic network inference in 11 human cancers

TL;DR: This study compares the performance of 13 established network inference methods in their capacity to retrieve literature-curated pathway interactions from RPPA data and reveals that signal transduction events involving receptor tyrosine kinases, the RAS/MAPK pathway, and the PI3K/AKT/mTOR pathway are the most significant PPIs shared across all tumor types.
ReportDOI

Perturbation biology models predict c-Myc as an effective co-target in RAF inhibitor resistant melanoma cells

TL;DR: Cell type-specific network models of signaling in RAF-inhibitor resistant melanoma cells are reported, showing that co-targeting c-Myc, using the BET bromodomain inhibitor JQ1, and the RAF/MEK pathway, using kinase inhibitors is both effective and synergistic in this context.
Journal ArticleDOI

Solutions to the computational protein folding problem

TL;DR: Collaborative efforts combining computational biology, structural biology and statistical physics expertise provide a solution to the computational protein folding problem.
Journal ArticleDOI

Significance of necrosis and gene expression profiling in high-grade myxoid/round cell liposarcoma (MRLS) of the extremity

TL;DR: The histologic grade of myxoid liposarcoma is determined by the extent of the round cell (RC) component, and for this study, MRLS was defined as having a round cell component.
Posted ContentDOI

Simultaneous enhancement of multiple functional properties using evolution-informed protein design

TL;DR: In this article , the authors apply evolutionary models of sequence co-variation to computationally design highly divergent variants of the model protein TEM-1 β-lactamase, and characterize these designs experimentally using multiple biochemical and biophysical assays.