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Simon Jones

Researcher at University of Manchester

Publications -  1100
Citations -  45394

Simon Jones is an academic researcher from University of Manchester. The author has contributed to research in topics: Haskell & Functional programming. The author has an hindex of 92, co-authored 1012 publications receiving 39886 citations. Previous affiliations of Simon Jones include University of California, Berkeley & Microsoft.

Papers
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Network-based assessment of HDAC6 activity is highly predictive of pre-clinical and clinical responses to the HDAC6 inhibitor ricolinostat

TL;DR: The results of the first clinical trial evaluating the activity of ricolinostat plus nab-paclitaxel in metastatic BC patients showed that the two agents can be safely combined, that clinical activity is identified specifically in patients with HR+/HER2- disease, and that the HDAC6 score was predictive of response.
Patent

Systems and methods for selectively decoupling a parallel extended instruction pipeline

TL;DR: In this paper, a light weight mechanism in the form of a condition code as seen by the main processor is designed to allow intelligent decision maximizing overall performance to be made in run-time if further instructions should be issued to the parallel extended pipeline based on the queue status.
Journal ArticleDOI

Breast cancer metastasis burden in sentinel nodes analysed using one-step nucleic acid amplification predicts axillary nodal status.

TL;DR: Extent of sentinel lymph node positivity measured using OSNA predicts NSLN metastasis risk, aiding decisions surrounding axillary treatment.
Journal ArticleDOI

Incidence of household transmission of acute gastroenteritis (AGE) in a primary care sentinel network (1992-2017): cross-sectional and retrospective cohort study protocol.

TL;DR: This study used the Royal College of General Practitioners (RCGP) Research and Surveillance Centre (RSC) primary care sentinel network, comprising data from 1 750 167 registered patients, and conducted a novel analysis using a ’household key', to identify patients within the same household.