Tero Aittokallio
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Crowdsourcing a Better Prostate Cancer Prediction Tool
Knowing the likely course of cancer can influence treatment decisions. Now a new prediction model published today in Lancet Oncology offers a more accurate prognosis for a patient's metastatic castration-resistant prostate cancer. The approach was as novel as the result - while researchers commonly work in small groups, intentionally isolating their data, the current study embraces the call in Joe Biden's "Cancer Moonshot" to open their question and their data, collecting previously published clinical trial data and calling for worldwide collaboration to evaluate its predictive power...
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First-Ever Crowdsourced Prostate Cancer Data-Mining Competition Discovery Impacts and Predicts Patient Survival
Today, a breakthrough report in the international journal Lancet Oncology demonstrates how a collaborative effort to analyze broadly accessible clinical data led to novel insights and improvements in cancer treatment and management. Participants in the Prostate Cancer DREAM (Dialogue for Reverse Engineering Assessments and Methods) Challenge – an effort initiated by Project Data Sphere, LLC (PDS) in collaboration with Sage Bionetworks using proven DREAM methodology – developed new risk factor models for metastatic castration-resistant prostate cancer (mCRPC)...
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Winners Announced for Crowdsourcing and Data Sharing Competition to Drive Innovation in Prostate Cancer Research
Industry leaders in biomedical research, oncology data sharing and computational science announced the winners of an innovative research challenge for prostate cancer using previously unavailable clinical data. The Prostate Cancer DREAM Challenge is the first research challenge in prostate cancer to marry crowdsourcing with data sharing, paving a new way to tackle key research questions about metastatic castration-resistant prostate cancer (mCRPC), an advanced form of the disease with poor outcomes. The Challenge called upon the cancer research and computational biology community to find solutions to key open clinical research questions about mCRPC and explore innovative research and modeling approaches. The three specific questions posed were to:
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