Author

Michael Howell

Primary ORCID profile (0000-0003-0912-0079 · 4 papers)Unclaimed ORCID profile · follow to get notified of new preprints

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Michael HowelliD4 papers · The Francis Crick Institute

Biology Disciplines

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Cancer Biology· 1

Signature Assays, Organisms & Methods

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Organism:human· 1 Approach:machine learning (classical)· 1 Research Focus:variant calling· 1 Assay:whole-exome sequencing· 1

Frequent Co-Authors

Christopher YauiD· 1 shared Damjan TemelkovskiiD· 1 shared Elizabeth FoxalliD· 1 shared Francesca D. CiccarelliiD· 1 shared Jesper LagergreniD· 1 shared Joel NulseniD· 1 shared Juliane PerneriD· 1 shared Lorena BenedettiiD· 1 shared

Activity by Year

2018-2018 · peak 1/yr

Co-Author Collaboration Network

10 top collaborators · 45 mutual co-author links

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1 shared paperCancer Biology

Christopher YauORCID

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1 paper

Recent·Most cited

Patient-specific detection of cancer genes reveals recurrently perturbed processes in esophageal adenocarcinoma

Thanos P. Mourikis, Lorena Benedetti, Elizabeth Foxall, Damjan Temelkovski, Joel Nulsen, Juliane Perner, Matteo Cereda, Jesper Lagergren, Michael Howell, Christopher Yau, Rebecca C. Fitzgerald, Paola Scaffidi, Francesca D. Ciccarelli, on behalf of the Oesophageal Cancer Clinical and Molecular Stratification (OCCAMS) Consortium

This study identifies patient-specific cancer genes in esophageal adenocarcinoma using machine learning, revealing recurrent process perturbations that suggest therapeutic targets.

bioRxiv · Oct 17, 2018 · PreprintCancer Biology