PSB Workshops

Cancer results from an evolutionary process that yields a heterogeneous tumor with distinct subpopulations and varying sets of somatic mutations. Viewing cancer through the lens of evolution is critical to improve our understanding of tumorigenesis and ultimately treatment of cancer. The workshop will focus on algorithms and models of evolutionary processes in cancer. Specifically, we aim to bring together the algorithmically-focused side of the cancer evolution community with the biologically-focused side.

Contact: Michelle Holko
Email: michelleholko at google dot com

The Clinical Genome Resource is a community of scientists, doctors, genetic counselors and software engineers who are creating a rich platform for curating and exchanging data and sharing it with national resources to help people across the world. This workshop will describe the various tools and technologies used to create, harbor, and share that data. In our workshop we will also introduce methods for accessing this “gold standard” data for use in bioinformatics pipelines and other downstream applications.

Contact: Karen Dalton
Email: kdalton at stanford dot edu

Artificial intelligence (AI) is making a big impact on patient experiences, clinician workflows, researchers, and the pharmaceutical industry work in the healthcare sector. In recent decades, technological advancements across scientific and medical disciplines have led to a torrent of diverse, large-scale biomedical datasets such as health, imaging data, clinical notes, lab test results, and other ‘omics data. The dropping costs of genomic sequencing coupled with advances in computing allow unprecedented opportunities to understand the effects of genetics on human disease etiologies and has resulted in the creation of population-level biobanks. As a consequence, the demand for novel computational methods, computational infrastructure, and algorithm improvements to efficiently process and derive insights from these datasets, particularly where it applies to clinical translational research, has dramatically increased. In addition to handling the sheer size and quantity of biomedical data, newly developed methods must also adapt and employ state-of-the-art AI algorithms that account for the unique complexities of biomedical datasets, such as sparseness, incompleteness, and noisiness of data, data multidimensionality such as clinical measurements from electronic health records, prescription drug data, environmental exposures. Additionally, these methods have to leverage the advances in high-performance computing like GPUs, faster inter-connects, and fast-access memory to help generate the needed insights at a faster rate.

Contact: Anurag Verma
Email: anurag.verma at pennmedicine dot upenn dot edu

The primary efforts of disease and epidemiological research can be divided into two areas: identifying the causal mechanisms and utilizing important variables for risk prediction. The latter is generally perceived as a more obtainable goal due to the vast number of readily available tools and the faster pace of obtaining results. However, the lower barrier of entry in risk prediction means that it is easy to make predictions, yet it is incredibility more difficult to make sound predictions. As an ever-growing amount of data is being generated, developing risk prediction models and turning them into clinically actionable findings is crucial as the next step. However, there are still sizable gaps before risk prediction models can be implemented clinically. While clinicians are eager to embrace new ways to improve patients’ care, they are overwhelmed by a plethora of prediction methods. Thus, the next generation of prediction models will need to shift from making simple predictions towards interpretable, equitable, explainable and ultimately, casual predictions. The purpose of this workshop is to introduce and discuss the current and future of risk prediction in the context of disease and epidemiological research. We will discuss the pressing topics ranging from data sources to model implementation.

Contact: Ruowang Li
Email: ruowang.li at cshs dot org

In this workshop, we will explore and highlight recent advances in computational biology at the nexus of single-cell spatial biology and precision cancer medicine. Topics will include multiplexed imaging, spatial transcriptomics, and platform integration (e.g., alignment of single-cell and spatial transcriptomics), with an emphasis on basic and translational cancer research. Our goal is to stimulate new ideas, foster critical debate, and form new collaborations in this exciting and emerging research area.

Contact: Aaron Newman
Email: amnewman at stanford dot edu