Team 3: developing deep learning models in virtual reality

Develop a VR environment for the ‘physical’ assembly of neural networks. Users of the tool will grab neural networks in the virtual and connect them to define the architecture, then run backprop and monitor training inside the VR.

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Team 4: simulating molecular dynamics of potential drug target regions

Simulate the binding of drugs to targets by identifying the shape and location of regions that may be stabilized through binding of targeted molecules, and correlate with structural changes in the protein active site or lack of drug binding stabilization.

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Team 7: Deep learning cell annotation framework for tumor classification

Here we propose to build an efficient annotation storage/visualization/editing framework that can be coupled with existing web-frameworks (e.g. openslide+openseadragon) to visualize tissue images.

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Team 8: Differentiation of metastatic potential in melanoma cells

We aim to develop an assay that can differentiate between melanoma cells with high and low metastatic properties. Distinguishing metastatic potential is the ultimate goal, yet a number of secondary quantitative analyses may also be of interest.

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Team 9: Machine learning for brain and cardiac MRI feature extraction

The project centers on the development and application of machine learning for the analysis of medical imaging data to automate the analysis and feature extraction, diagnosis or prognosis.

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Team 10: Visual dashboard and predictive analytics for pediatric endocrinology

To develop visual dashboards patient population data and predictive analytics for quick, efficient risk stratification of individual patients in a clinical setting.

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Team 11: Natural language processing tool for clinical trial inclusion criteria

The goal of this project is use natural language processing to extract exclusion and inclusion criteria from free form text fields to match patients with clinical trials.

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Team 12: ML algorithm for staging of liver disease

The focus of this project is to develop a machine learning algorithm using the plasma lipid data provided to provide staging for a patient’s liver disease diagnosis.

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