Fieldays Exhibitor 2015

This magazine was assembled by Wintec Media Arts third year students in journalism, photography and graphic design under the supervision of the corresponding staff. The Fieldays Exhibitor magazine is a daily magazine which stretches over the four days of the Waikato Fieldays to document the goings on and excitement of the current affairs going on daily at Mystery Creek

Lunch and journalism: The Wintec Press Club

This book is a collection of profiles from the past 20 speakers at the Wintec Press club written by Wintec's journalism students and edited by Wintec's writer in residence, Steve Braunias

The young PI Buzz: Learning from the organizers of the junior principal investigator meeting at ISMB-ECCB 2013

Intelligent SystemsElectrical Engineering, Mathematics and Computer Scienc

A deep learning system accurately classifies primary and metastatic cancers using passenger mutation patterns

In cancer, the primary tumour's organ of origin and histopathology are the strongest determinants of its clinical behaviour, but in 3% of cases a patient presents with a metastatic tumour and no obvious primary. Here,as part of the ICGC/TCGA Pan-Cancer Analysis of Whole Genomes (PCAWG) Consortium, we train a deep learning classifier to predict cancer type based on patterns of somatic passenger mutations detected in whole genome sequencing (WGS) of 2606 tumours representing 24 common cancer types produced by the PCAWG Consortium. Our classifier achieves an accuracy of 91% on held-out tumor samples and 88% and 83% respectively on independent primary and metastatic samples, roughly double the accuracy of trained pathologists when presented with a metastatic tumour without knowledge of the primary. Surprisingly, adding information on driver mutations reduced accuracy. Our results have clinical applicability, underscore how patterns of somatic passenger mutations encode the state of the cell of origin, and can inform future strategies to detect the source of circulating tumour DNA