GTEx Consortium; Laboratory, Data Analysis & Coordinating Center (LDACC)—Analysis Working Group; Statistical Methods groups—Analysis Working Group; Enhancing GTEx (eGTEx) groups; NIH Common Fund; NIH/NCI; et al. Genetic effects on gene expression across human tissues. Nature. 2017;550(7675):204-213.
Aminian M, Couvin D, Shabbeer A, Hadley K,et al. Predicting Mycobacterium tuberculosis complex clades using knowledge-based bayesian networks. BioMed Res Int. 2014; 2014: 398484.
Aminian M, Shabbeer A, Hadley K, et al. Knowledge-based Bayesian network for the classification of Mycobacterium tuberculosis complex sublineages. Proceedings of the 2nd ACM Conference on Bioinformatics, Computational Biology and Biomedicine (BCB '11). ACM, New York, NY, USA. 2011; Pages 201-208.
Kane Hadley, B.Sc.
Kane Hadley is a software engineer in the Cancer Program of the Broad Institute of MIT and Harvard, where he works under the direction of Michael Noble in the Gaddy Getz lab as part of the GTEx Portal team.
As a member of this team, Hadley helps develop the technology enabling the portal, such as the database, the app providing access to the data, and the visualizations and experience on the website for users. Ultimately, the goal is to help researchers interested in incorporating the data collected by the GTEx project into their own research by providing tools to make the data accessible to visualization and interaction, based on the premise that the more we can increase research speed and lower the barrier to making use of the data, the greater the rate of research success.
Prior to joining the Broad Institute in 2014, Hadley worked at Lincoln Laboratory in its cybersecurity group. He holds a B.Sc. in computer science with a focus on computer and systems engineering from Rensselaer Polytechnic Institute.
Contact Kane Hadley via email at firstname.lastname@example.org.