Molecular Tumor Boards: What they are; What they do; What they need
- Jeff Shrager | Stanford
For some time my colleagues and I have been studying cancer tumor boards, now rapidly becoming molecularized by the molbio/omics/big-data revolutions. A Molecular Tumor Board (MTB) is essentially an engineering team charged with giving real-time guidance to a human being facing a life-threatening disease. However, unlike “real” engineering teams, who have numerous tools and strong theories to guide them in their designs, MTBs are pretty much stuck with search engines, which are rather weak reasoning aides. What tools would an MTB need? Honestly, I have no idea. What I can tell you, though, is what they do, and from that we may be able to co-construct, along with them, what they might need.
Speaker Details
Jeff Shrager is a CommerceNet Senior Fellow, consulting CTO of Cancer Commons, and consulting Professor in the Symbolic Systems Program at Stanford. He is best described as a “psychologist of science”. He holds degrees in computer science and cognitive and developmental psychology from Penn and CMU, and has conducted research in molecular and computational biology, cognitive and developmental neuroscience, and in machine learning, NLP, and all that jazz. His BioBike (nee BioLingua) was the world’s first cloud-based biocomputing platform, and remains the only biocomputing platform with symbolic reasoning capabilities. Fun facts: Jeff wrote the version of Eliza that ran on the first personal computers, created the first “Wizard” program (essentially Clippy for Vax VMS), and discovered how the brain does deep learning.
-
-
Jeff Running
-
Series: Microsoft Research Talks
-
Decoding the Human Brain – A Neurosurgeon’s Experience
- Dr. Pascal O. Zinn
-
-
-
-
-
-
Challenges in Evolving a Successful Database Product (SQL Server) to a Cloud Service (SQL Azure)
- Hanuma Kodavalla,
- Phil Bernstein
-
Improving text prediction accuracy using neurophysiology
- Sophia Mehdizadeh
-
Tongue-Gesture Recognition in Head-Mounted Displays
- Tan Gemicioglu
-
DIABLo: a Deep Individual-Agnostic Binaural Localizer
- Shoken Kaneko
-
-
-
-
Audio-based Toxic Language Detection
- Midia Yousefi
-
-
From SqueezeNet to SqueezeBERT: Developing Efficient Deep Neural Networks
- Forrest Iandola,
- Sujeeth Bharadwaj
-
Hope Speech and Help Speech: Surfacing Positivity Amidst Hate
- Ashique Khudabukhsh
-
-
-
Towards Mainstream Brain-Computer Interfaces (BCIs)
- Brendan Allison
-
-
-
-
Learning Structured Models for Safe Robot Control
- Subramanian Ramamoorthy
-