Igor Jurisica, Tier I Canada Research Chair in Integrative Cancer Informatics, Senior Scientist at Princess Margaret Cancer Centre, Prof at U Toronto and Visiting Scientist at IBM CAS.
I. Jurisica's research focuses on integrative computational biology and the representation, analysis and visualization of high-dimensional data to identify prognostic and predictive signatures, drug mechanism_of_action
and in-silico repurposing of drugs. Interests include prediction and analysis of protein interactions networks, modeling signaling cascades and high-throughput protein crystallography.
He has published extensively on data mining, visualization and cancer informatics, including multiple papers in Science, Nature, Nature Medicine, Nature Methods, J Clinical Oncology, and has over 8,787 citations since 2010. He has been included in Thomson Reuters 2015 & 2014 list of Highly Cited Researchers, and The World's Most Influential Scientific Minds: 2014 Report.
The D-A-C website converted the local time of this talk (12:30pm) to GMT when sending out the automatic notice. I'm hoping it will stop doing this now, but just in case, please check the time event on the D-A-C website, which I will try to make sure is correct.
Sorry for the confusion!
Joint Research Seminar in Biomedical and Molecular Science and School of Computing.
Modern science and clinical care makes significant advances towards fathoming complex diseases by adopting high-throughput molecular and imaging technologies for describing disease characteristics and progression. However, there remains a mixture of the newest "-omic" and imaging technologies with extremely old approaches characterizing patients' health. Wearable technology started to revolutionize data-driven science and medicine by providing continuous data streams on multiple measured features related to our activities, lifestyle and overall health. While we characterize diseases with the latest molecular technologies, e.g., next generation sequencing, proteomic and
metabolomic platforms, we continue to collect other patient data unreliably and sporadically, much of it using questionnaires or snapshots of sampled measures. We know that weight correlates with risk of many diseases,including heart condition, diabetes and cancer; one example of many; we know that BMI is an imprecise and limited estimate of overall fat percentage and fitness, especially when estimated from the weight and height, yet it is used often in clinical studies, and linked to disease risk. We need more precise measures of fitness and body mass - we need to move from "evidence-based medicine" to data-driven medicine. Knowing the heart rate, real time ECG, breathing rate and volume, sleeping pattern and quality, and overall activity may also provide valuable insights into our health, but we need continuous data, not just a few snapshots. This in turn will change how we consider evidence, recommendations, and belief in guidelines. Diverse wearable devices provide variable precision in measuring these parameters, especially across specific physical activities. Innovative approaches are needed to ensure long-term adherence, as
benefits linked to behavior change reduce quickly when key behaviors are discontinued. Privacy and confidentiality is paramount, yet, "the future is connected", and through the collective data handling and analysis, we will manage even the most complex diseases, eventually.