We are building the next generation of data science methods for curing disease.
We are specifically focused on the following work:
- Drug screening for pediatric diseases. We perform microscopy-based, in vitro drug screens to identify promising drug candidates. Our goal is to identify new therapeutic options for children with diseases like Neurofibromatosis Type 1 (NF1), neuroblastoma, and pediatric high grade glioma.
- Reproducible software for processing high-dimensional microscopy readouts. We are building open source software to support reproducible image-based profiling. We develop pycytominer, CytoTable, and CytoSnake to process large-scale microscopy images. Our aim is to improve data processing pipelines, reproducibility, data provenance, and dataset interoperability.
- Microscopy representations of cell state. We analyze high-content information from microscopy images, extracting biologically-meaningful and reproducible representations which contain systems biology information. We train artificial intelligence and machine learning (AI/ML) algorithms to predict cell phenotypes from these representations. These phenotypes include various cell health states and cell death mechanisms. Our aim is to use these representations to annotate drug screening data with phenotype and mechanism.
- Innovative method development for drug screening and translational research. We develop new assays and computational methods to improve human health. This includes modeling NF1 and other pediatric diseases using patient-derived organoids, developing gene-network-based targets that take advantage of polypharmacology, developing CRISPRi approaches to simulate specific high-dimensional phenotypes, modeling cell resistance to cancer therapies, predicting heart failure subtypes, and more.
How we do science
- Open science and software. We perform all of our work in the open and release all of our software as open source. We aim to maximize the impact and reproducibility of our research by making everything we do immediately available for others to build upon. We host all of our code, data, and analysis at https://github.com/WayScience.
- Scientific publishing. We submit preprints of our work and subsequently publish in peer-reviewed journals to disseminate knowledge more formally. We use pre-print review services (like Review Commons) whenever possible to improve the peer-review process. For each project, we also share project-specific github repositories (representing a lab notebook) to facilitate computational reproducibility.
We strive for creativity, integrity, inclusivity, and rigor in everything that we do.
See here for a full list of work.
See below for a selection of our recent papers.