Pistoia Alliance April Virtual Conference: Accelerating and Improving Flow Cytometry Data Analysis for Clinical Trials
Accelerating and Improving Flow Cytometry Data Analysis for Clinical Trials, Patient Diagnosis, and Biomarker Discovery via Machine Learning Derived From Crowdsourced Analysis of COVID-19 Data Using Eve Online
This on-demand recording is part of the Pistoia Alliance Conference: Collaborative R&D in Action, April 20-23, 2021. For more information about related events, please visit our online calendar.
Manual analysis of flow cytometry is currently not only highly subjective and time consuming, but the complexity of datasets has made it impossible to access the full amount of information embedded within them.
To address these challenges and aid scientists using flow cytometry technology to study COVID-19, in June 2020 we launched a crowdsourced science effort called Project Discovery in collaboration with CCP, developers of Eve Online (a space-based, persistent world massive multiplayer online role-playing game). Citizen scientists have already generated an unprecedented amount of training data that will benefit the entire community interested in developing ML algorithms for FCM.
Over 263K participants have generated over 88M bivariate plots. 46M of these plots come from data associated with peer-reviewed publications focused on COVID-19.
At the conclusion of this session, participants should be able to: