UCSF

The field of computing has made incredible strides in how we process and analyze information. Here I consider how to take these advances in theory and apply them in novel ways to different scopes of the medical pipeline: from the cellular level, to the clinic, and finally to the broadest level of healthcare and data management between different institutions. In particular, I sought to explore how the emergent technologies of deep learning and blockchain technologies can provide practical improvements to biomedical research, with a focus in mostly the neurodegenerative space. I investigated how to improve high-content high-throughput drug screens through deep learning—moving experimentation and analysis off the bench and into the servers as a first pass to generate hypotheses. There’s a plethora of information found in these screens. Through deep learning, such information can be refined to actionable insights, and complex algorithms can make sense of data that is uninterpretable to humans. So much time and money is lost in pharmaceutical endeavors due to following up on poor drug candidates. I hypothesized that if we had better predictions to begin with, then the search space for new drugs would be optimized. Hence, I developed an in-silico approach to phenotypic drug screens, by providing a deep learning method to glean related biological signals between markers and project them into images used for hypothesis generation. This resulted in better triaging efforts for finding active compounds in an Alzheimer’s-centered drug screen. More generally, I showed that this method can apply to another phenotypic screen in a completely different biological system. I showed that deep learning can not only assist at the bench, but also be useful in the clinical research space. Again focusing on neurodegenerative efforts, I developed deep learning methods to help neuropathologists identify amyloid beta pathologies. Following the idea that there is wisdom to be found in the crowd, I showed how consensus learning and ensembling over many experts can provide a robust framework for accurate detection despite being in a space where there may be expert disagreement. These efforts provided not only automation, but also potential for standardization in the field as I provided a method that is not only accurate but also completely consistent. I showed the method’s potential for practical adoption through a prospective validation study. Finally, at the broad level of data exchange between multiple medical institutions and data centers, I introduced a blockchain-based system into the clinical network, showing that blockchain technology has relevant applications outside of merely financial problems and questions. In particular, I made advances in the clinical trial process, and performed a proof-of-concept in which I simulated a real clinical trial on a more secure, traceable, and trustworthy data management system. We showed that such a system is immune to data tampering, and also provides a useful audit trail for regulators.