Posted 16th March 2020 by Liv Sewell
Professor Anant Madabhushi is a world-recognised, award-winning leader in computerized imaging research and translational applications, with over 160 peer-reviewed journal publications and close to 100 patents issued or pending. He is a keynote speaker at the 6th Digital Pathology & AI Congress: USA. He explains here why having patents is not enough…
Posted 7th February 2020 by Liv Sewell
Ahead of The 3rd Global NASH Congress, Dr Bevin Gangadharan explains how he helped to discover novel NAFLD biomarkers and their role in the development of a point-of-care test for NAFLD.
Deep Learning based detection of tumor tissue compartments improves prognostic immunoprofiling in muscle-invasive bladder cancer
Posted 11th November 2019 by Liv Sewell
Worldwide, bladder cancer (BC) is the 11th most commonly diagnosed cancer. In men, BC is the 7thmost commonly diagnosed cancer worldwide.Although men are more likely to develop BC than women, women present with more advanced disease and have worse survival rates.
Muscle-invasive bladder cancers (MIBC) are cancers that have grown into or through the muscle layers of the bladder wall.
Dr. Katharina Nekolla and Ansh Kapil and their team applied Deep Learning enabled pathology to better understand prognostic factors in MIBC. Here we review the significance of the research and share their original poster.
Posted 12th June 2019 by Joshua Sewell
When conducting an experiment to identify biomarkers, it is crucial to design the experiment properly. 80-90% of all biomarker populations for the last 20 years have not and cannot be reproduced, and the main reason that biomarkers fail is that these experiments are not designed properly. In this post, I will outline two ways in which experiments are poorly designed, and then outline the technological and methodological solution in a later blog.
Big Data Analytics and Artificial Intelligence: 7 Design Principles to Empower Life Science Researchers
Posted 5th January 2018 by Jane Williams
Life sciences research is increasingly relying on big data analytics to drive scientific discovery. One of the contributing factors to this trend is the fact that researchers are confronted with a rapidly growing body of scientific literature and databases required to interpret experimental outcomes.