Machine Learning and Data Science Approaches in Mathematical Biology: Recent Advances and Emerging Topics

Tuesday, June 15 at 11:30am (PDT)
Tuesday, June 15 at 07:30pm (BST)
Wednesday, June 16 03:30am (KST)

SMB2021 SMB2021 Follow Tuesday (Wednesday) during the "MS08" time block.
Note: this minisymposia has multiple sessions. The second session is MS09-DDMB (click here).

Share this


Paul Atzberger (University of California Santa Barbara, USA), Smita Krishnaswamy (Yale University, USA), Kevin Lin (University of Arizona, USA)


Biological investigations have resulted historically in the development of many new methods for data analysis. This session aims to discuss recent advances both concerning new biological application areas and algorithms drawing on increasingly large datasets and availability of computational resources. Topic areas include but are not limited to, data-driven modeling, applications of deep learning to problems in biology (sequence analysis, protein folding, experimental design, control), physics-informed machine learning, kernel methods for biological systems, linear and non-linear system identification, hybrid data-driven simulation methods, and other areas. The session also aims to facilitate discussions on emerging methods and areas for the biological sciences where data analysis is playing increasingly central roles.

Smita Krishnaswamy

(Yale University, USA)
"Geometric and Topological Approaches to Representation Learning in Biomedical Data"
High-throughput, high-dimensional data has become ubiquitous in the biomedical, health and social sciences as a result of breakthroughs in measurement technologies and data collection. While these large datasets containing millions of observations of cells, peoples, or brain voxels hold great potential for understanding generative state space of the data, as well as drivers of differentiation, disease and progression, they also pose new challenges in terms of noise, missing data, measurement artifacts, and the so-called “curse of dimensionality.” In this talk, I will cover data geometric and topological approaches to understanding the shape and structure of the data. First, we show how diffusion geometry and deep learning can be used to obtain useful representations of the data that enable denoising (MAGIC), dimensionality reduction (PHATE), and factor analysis (Archetypal Analysis Network) of the data. Next we will show how to learn dynamics from static snapshot data by using a manifold-regularized neural ODE-based optimal transport (TrajectoryNet). Finally, we cover a novel approach to combine diffusion geometry with topology to extract multi-granular features from the data (Diffusion Condensation and Multiscale PHATE) to assist in differential and predictive analysis. On the flip side, we also create a manifold geometry from topological descriptors, and show its applications to neuroscience. Together, we will show a complete framework for exploratory and unsupervised analysis of big biomedical data.

Sui Tang

(UCSB, United States)
"Data-driven discovery of interacting particle system using Gaussian processes"
Interacting particle or agent systems are widely used to model complicated collective motions of animal groups in biological science, such as flocking of birds, milling of fish, and swarming of prey. A fundamental goal is to understand the link between individual interaction rules and collective behaviors. We consider second-order interacting agent systems and study an inverse problem: given observed data, can we discover the interaction rule? For the interactions that only depends on pairwise distance, we propose a learning approach based on Gaussian processes that can simultaneously infer the interaction kernel without assuming a parametric form and learn other unknown parameters in the governing equations. The numerical results on prototype systems, including Cuker-Smale dynamics and fish milling dynamics, show that our approach produced faithful estimators from scarce and noisy trajectory data and made accurate predictions of collective behaviors. This talk is based on the joint work with Jinchao Feng.

Rose Yu

(University of California San Diego, USA)
"Physics-Guided Deep Learning for Forecasting COVID-19"

Alan Aspuru-Guzik

(University of Toronto, USA)
"Artificial Intelligence and Self-Driving Laboratories for Molecular Discovery"

Hosted by SMB2021 Follow
Virtual conference of the Society for Mathematical Biology, 2021.