Steven M. Hill
Steven M. Hill
CRUK Cancer Biomarker Centre, University of Manchester
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Lineage-specific genome architecture links enhancers and non-coding disease variants to target gene promoters
BM Javierre, OS Burren, SP Wilder, R Kreuzhuber, SM Hill, S Sewitz, ...
Cell 167 (5), 1369-1384. e19, 2016
A pan-cancer proteomic perspective on The Cancer Genome Atlas
R Akbani, PKS Ng, HMJ Werner, M Shahmoradgoli, F Zhang, Z Ju, W Liu, ...
Nature communications 5 (1), 3887, 2014
Community assessment to advance computational prediction of cancer drug combinations in a pharmacogenomic screen
MP Menden, D Wang, MJ Mason, B Szalai, KC Bulusu, Y Guan, T Yu, ...
Nature communications 10 (1), 2674, 2019
Inferring causal molecular networks: empirical assessment through a community-based effort
SM Hill, LM Heiser, T Cokelaer, M Unger, NK Nesser, DE Carlin, Y Zhang, ...
Nature methods 13 (4), 310-318, 2016
Bayesian Inference of Signaling Network Topology in a Cancer Cell Line
SM Hill, Y Lu, J Molina, LM Heiser, PT Spellman, TP Speed, JW Gray, ...
Bioinformatics 28, 2804–2810, 2012
Functional proteomics identifies miRNAs to target a p27/Myc/phospho-Rb signature in breast and ovarian cancer
EG Seviour, V Sehgal, Y Lu, Z Luo, T Moss, F Zhang, SM Hill, W Liu, ...
Oncogene 35 (6), 691-701, 2016
cfDNA methylome profiling for detection and subtyping of small cell lung cancers
F Chemi, SP Pearce, A Clipson, SM Hill, AM Conway, SA Richardson, ...
Nature Cancer 3 (10), 1260-1270, 2022
Do small worlds synchronize fastest?
C Grabow, SM Hill, S Grosskinsky, M Timme
EPL (Europhysics Letters) 90, 48002, 2010
The alzheimer's disease prediction of longitudinal evolution (TADPOLE) challenge: Results after 1 year follow-up
RV Marinescu, NP Oxtoby, AL Young, EE Bron, AW Toga, MW Weiner, ...
Machine Learning for Biomedical Imaging 1, 1-60, 2021
Context-specificity in causal signaling networks revealed by phosphoprotein profiling
SM Hill, NK Nesser, K Johnson-Camacho, M Jeffress, A Johnson, ...
Cell Systems 4 (1), 73-83.e10, 2017
High-dimensional regression in practice: an empirical study of finite-sample prediction, variable selection and ranking
F Wang, S Mukherjee, S Richardson, SM Hill
Statistics and Computing 30, 697-719, 2020
Integrating biological knowledge into variable selection: an empirical Bayes approach with an application in cancer biology
SM Hill, RM Neve, N Bayani, WL Kuo, S Ziyad, PT Spellman, JW Gray, ...
BMC Bioinformatics 13 (1), 94, 2012
DREAMTools: a Python package for scoring collaborative challenges [version 1; referees: 3 approved with reservations]
T Cokelaer, M Bansal, C Bare, E Bilal, BM Bot, EC Neto, F Eduati, ...
F1000Research 4, 1030, 2015
Molecular heterogeneity at the network level: high-dimensional testing, clustering and a TCGA case study
N Städler, F Dondelinger, SM Hill, R Akbani, Y Lu, GB Mills, S Mukherjee
Bioinformatics 33 (18), 2890-2896, 2017
Network clustering: probing biological heterogeneity by sparse graphical models
S Mukherjee, SM Hill
Bioinformatics 27 (7), 994-1000, 2011
Network-based clustering with mixtures of L1-penalized Gaussian graphical models: an empirical investigation
SM Hill, S Mukherjee
arXiv preprint arXiv:1301.2194, 2013
Inferring network structure from interventional time-course experiments
SEF Spencer, SM Hill, S Mukherjee
The Annals of Applied Statistics 9 (1), 507-524, 2015
Corrigendum: A pan-cancer proteomic perspective on The Cancer Genome Atlas
R Akbani, PKS Ng, HMJ Werner, M Shahmoradgoli, F Zhang, Z Ju, W Liu, ...
Nature communications 6, 2015
Causal Learning via Manifold Regularization
SM Hill, C Oates, DA Blythe, S Mukherjee
Journal of Machine Learning Research 20, 1-32, 2019
Genome-wide perturbations by miRNAs map onto functional cellular pathways, identifying regulators of chromatin modifiers
TJ Moss, Z Luo, EG Seviour, V Sehgal, Y Lu, SM Hill, R Rupaimoole, ...
npj Systems Biology and Applications 1, 15001, 2015
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Artículos 1–20