
Deep Learning to Accelerate Drug Development: An NYC Symposium
Tuesday, November 13, 2018, 8:30 AM - 6:15 PM EST
The New York Academy of Sciences, 7 World Trade Center, 250 Greenwich St Fl 40, New York
Technological advances in artificial intelligence, especially in the field of deep learning, hold the potential to transform modern drug discovery and development. In recent years, the accumulation of multi-faceted big data in medicine offers exciting new opportunities to apply state-of-the-art deep learning advances to pressing global health challenges. However, significant ground-work in applying this research is still critically needed before real breakthroughs can happen.
In this symposium, we will bring together the best minds from the biopharmaceutical industry and academic deep learning communities to create focused dialogues and research collaborations in New York City. The meeting will feature exciting new opportunities on topics including mobile health and sensors, medical and pathology imaging, genomics, computer vision and robotics for lab automation, and electronic health records (EHRs), and beyond.
Registration
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Tuesday
November 13, 2018
Continental Breakfast and Registration
Introduction and Welcome Remarks
Speakers
Keynote Lecture: Artificial Intelligence in Cancer Genomics and Therapy
Speaker
Networking Coffee Break
Session 1: Applying Deep Learning to Develop Novel Health Sensors
Counting Reps: Quantitating Rehabilitation Dose After Stroke
Speaker
Deep Learning-Enabled Remote Gait and Mobility Monitoring Using Mobile Phones Sensor Data
Speaker
Speech Processing In The Human Brain Meets Deep Learning
Speaker
Networking Lunch
Session 2: Deep Learning Approaches for Genomics
Deciphering the Genome: From Deep Learning to Deep Knowledge
Speaker
Opportunities and Obstacles for Deep Learning in Drug Development
Speaker
Session 3: Advanced Image Analysis Using Deep Learning
To Be Confirmed
Speaker
Image-based Convolutional Neural Networks for Decision Support in Monoclonal Cell Line Development
Speaker
Networking Coffee Break
Session 4: Deciphering Real World Data with Deep Learning Methods
Deep Learning on Medical Records for Disease Prediction and Biomarker Discovery
Speaker
Phenotyping Endometriosis through Modeling of Self-Tracking Data
Speaker
Estimating Disease Heritability Using 14 Million Patients' Records
Speaker
Neworking Reception and Poster Viewing
Closing Remarks and F1000 Poster Prize Presentation
Speaker