Explore how big data and machine learning are revolutionizing drug development risk assessment in this 36-minute lecture from Yale University. Delve into the challenges of identifying new drug targets and developing safe, effective medications, while examining the complexities of characterizing drug development risk. Learn how the combination of machine learning and data availability can provide more accurate and unbiased estimates of success probabilities in pharmaceutical research. Discover the approach developed and commercialized by Intelligencia Inc., currently utilized by top biopharmaceutical companies. Gain insights from speaker Vangelis Vergetis, PhD, co-founder and CEO of Epikast, as he shares his extensive experience in healthcare, technology, and data science, offering valuable perspectives on improving R&D productivity and resource allocation in the pharmaceutical industry.
Assessing Drug Development Risk Using Big Data and Machine Learning
Overview
Syllabus
Assessing Drug Development Risk Using Big Data and Machine Learning
Taught by
Yale Radiology and Biomedical Imaging
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Reviews
5.0 rating, based on 1 Class Central review
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The "Assessing Drug Development Risk Using Big Data and Machine Learning" course offered by Yale University was a comprehensive and informative learning experience. It covered relevant topics and utilized real-world examples to illustrate concepts. I feel well-equipped with the knowledge and skills to assess drug development risk using big data and machine learning. I highly recommend this course to anyone interested in the pharmaceutical industry. Thank you, Yale University, for offering such a valuable course.
