the ylaboratory likes to ask 'why' questions
Naturally, the ‘y’ laboratory likes to ask ‘why’ questions: Why are some cells more vulnerable to Alzheimer’s disease? Why are some cancers more resistant to treatment than others? More broadly speaking, we seek to develop methods to tackle any interesting disease-related questions, whenever computation and data might help accelerate biological discoveries and enable potential improvements in quality of life: What can model organisms tell us about human disease? How can we develop better visualizations to aid interpretation and improve the efficiency of not only other computational biologists, but also bench scientists and clinicians?
With a solid computational foundation, we apply machine learning and quantitative reasoning to the following biological problems:
unraveling the complexity of neurological diseases
The complexity of the brain is highly challenging to model and understand. As a result, there are few safe and effective diagnostics and therapeutics for neurodegenerative diseases and psychiatric disorders. This is in no small part due to the fact that we have yet to develop a sufficient understanding of the molecular mechanisms that underlie these disorders. By modeling neurons and other specific cell types in the brain we are trying to unravel the complex dysfunctions occurring in Alzheimer's, Parkinson's, and other psychiatric disorders that have significant impacts on quality of life.
cancer and chronic diseases
The ylab is also actively studying cancer initiation and progression. Underlying the inherent heterogeneity between individuals lurk some common factors that cannot be explained by genetics alone. We seek to develop models that can tease out this complexity by looking at the epigenome, the immune system, and microbial factors, giving a more complete picture of cancer development and new avenues for treatment. Furthermore, we leverage the synergies between these data to better understand autoimmune and other chronic diseases.
cross-organism translational research
Despite advances in technology, there are still a host of ethical and technical limitations to probing biological systems (e.g., experimentally assaying the human brain). To address this, a rich experimental toolset exists in model organisms, but transferring knowledge between model systems and humans is challenging. By developing novel computational methods that leverage the large collections of data in both human and model systems, we aim to reconcile the gap between model organism biology and human disease genetics.
interactive tools and data visualization
Methods and tools are best when they can be easily utilized by the greater community. We are strongly committed to developing tools and visualizations that help biologists and clinicians accelerate discovery. Dynamic, interactive systems combined with intuitive visualization can help unlock state of the art statistical and machine learning methods, enabling easy navigation of large, unwieldy, high-dimensional datasets, as well as models, even for those with no prior programming experience.