On March 11, 2021, we will organize the first MLFPM Winter Symposium. All talks will be streamed live on YouTube (link will follow).
|Time||Speaker||Title of the talk|
|08:30-09:30||Fabian Theis||Latent space learning in single cell genomics|
|09:45-10:45||Mathias Niepert||Neural-Relational Learning and some Biomedical Applications|
|16:00-17:00||David Sontag||Using machine learning to guide treatment suggestions|
All times are in CET.
More information will follow.
Fabian Theis (Institute of Computational Biology, Helmholtz Munich)
Thursday, March 11, 08:30-09:30
Modeling cellular state as well as dynamics e.g. during differentiation or in response to perturbations is a central goal of computational biology. Single-cell technologies now give us easy and large-scale access to state observations on the transcriptomic, epigenomic and more recently also spatial level. In particular, they allow resolving potential heterogeneities due to asynchronicity of differentiating or responding cells, and profiles across multiple conditions such as time points, space and replicates are being generated, with a series of implications across biology and medicine.
Most computational methods for single cell genomics are operating on an intermediate often nonlinear representation of the high-dimensional data such as a cell-cell knn graph or some more general latent space. Interpretation of these led already in early days towards models of cellular differentiation for example by pseudotemporal ordering or mapping time information. Hence latent space modeling and manifold learning have become a popular tool to learn overall variation in single cell gene expression, more recently also across data sets and modalities.
After a short review of these approaches, I will discuss how latent space learning can be achieved using variants of autoencoders, with applications from denoising, imputation to learning perturbations. I will then show how it can be used to integrate single cell RNA-seq data sets across multiple labs in a privacy-aware manner, and demonstrate mapping disease variation by querying COVID-19 patients ontop of a healthy immune reference atlas. I will present our recent resource Sfaira of data loaders and shared latent spaces across tissues, and finish with short outlook towards spatial modeling and interpretability of latent projections under perturbations.
Mathias Niepert (NEC Labs Europe)
Thursday, March 11, 09:45-10:45
The talk will provide an overview of graph-based machine learning research conducted at NEC Labs Europe. The biomedical applications are, among others, cancer vaccine development, variant calling, and drug side effect prediction.
Nataša Pržulj (Barcelona Supercomputing Center)
Thursday, March 11, 11:00-12:00
Michael Bronstein (Imperial College London & University of Lugano & Twitter)
Thursday, March 11, 13:30-14:30
Dana Pe’er (Memorial Sloan Kettering Cancer Center)
Thursday, March 11, 14:45-15:45
David Sontag (MIT)
Thursday, March 11, 14:45-15:45
The next decade will see a shift in focus of machine learning in healthcare from models for diagnosis and prognosis to models that directly guide treatment decisions. We show how to learn treatment policies from electronic medical records, doing a deep dive into our recent work on learning to recommend antibiotics for women with uncomplicated urinary tract infections (Kanjilal et al., Science Translational Medicine ’20). We then discuss bigger picture questions for the field, such as how to do rigorous retrospective evaluations, fairly comparing to existing clinical practice, and how to optimally design for clinician-AI interaction, including how to build trust and how to decide when to defer decisions to clinicians. We find that, relative to clinicians, our best models reduce inappropriate antibiotic prescriptions from 11.9% to 9.5% while at the same time using 50% fewer second-line antibiotics.