Email Address: email@example.com
Other Research Theme Collaborations: SOMX
Application of statistical and computational methods in molecular biology. Analysis of noisy, high-dimensional data with a specific focus on network inference, variable selection and clustering. Applications include inference of causal protein signalling networks, drug response prediction and disease subtype discovery.
Selected PapersStädler N*, Dondelinger F*, Hill SM, Akbani R, Lu Y, Mills GB and Mukherjee S (2017)Molecular heterogeneity at the network level: high-dimensional testing, clustering and a TCGA case study
Bioinformatics 33: 2890–2896 (*Joint first authors)
Hill SM*, Nesser NK*, . . . , Mukherjee S^ and Spellman PT^ (2017)Context-specificity in causal signaling networks revealed by phosphoprotein profiling
Cell Systems 4: 73-83.e10 (*Joint first authors, ^Corresponding authors)
Javierre BM*, Burren OS*, Wilder SP*, Kreuzhuber R*, Hill SM*, . . . , Frontini M^, Wallace C^, Spivakov M^ and Fraser P^ (2016)Lineage-specific genome architecture links enhancers and non-coding disease variants to target gene promoters
Cell 167: 1369-1384.e19 (*Joint first authors, ^Corresponding authors)
Hill SM*, Heiser LM*, . . . , Stolovitzky G^, Saez-Rodriguez J^ and Mukherjee S^ (2016)Inferring causal molecular networks: empirical assessment through a community-based effort
Nature Methods 13: 310-318 (*Joint first authors, ^Corresponding authors)
Spencer SEF, Hill SM and Mukherjee S (2015)Inferring network structure from interventional time-course experiments
The Annals of Applied Statistics 9: 507-524
Akbani R, Ng PKS, Werner HMJ, . . . , Hill SM, . . . , Mukherjee S, Lu Y and Mills GB (2014)A pan-cancer proteomic perspective on the Cancer Genome Atlas.
Nature Communications 5: 3887
Hill SM, Lu Y, Molina J, Heiser LM, Spellman PT, Speed TP, Gray JW, Mills GB and Mukherjee S (2012)Bayesian inference of signaling network topology in a cancer cell line.
Bioinformatics 28: 2804-2810
Hill SM, Neve RM, Bayani N, Kuo W-L, Ziyad S, Spellman PT, Gray JW and Mukherjee S. (2012)Integrating biological knowledge into variable selection: An empirical Bayes approach with an application in cancer biology.
BMC Bioinformatics 13: 94