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Alexander Tong

Alumni

Publications

Topological Analysis of Single-Cell Hierarchy Reveals Inflammatory Glial Landscape of Macular Degeneration
Manik Kuchroo
Marcello DiStasio
Eric Song
Eda Calapkulu
Le Zhang
Maryam Ige
Amar H. Sheth
Madhvi Menon
Alexander Tong
Abhinav Godavarthi
Yu Xing
Scott Gigante
Holly Steach
Jessie Huang
Guillaume Huguet
Janhavi Narain
George Mourgkos
Rahul M. Dhodapkar
Matthew J. Hirn
Bastian Rieck … (see 3 more)
Guy Wolf
Smita Krishnaswamy
Brian P. Hafler
2020-12-31
SSRN Electronic Journal (unknown)
doi.org
Uncovering the Folding Landscape of RNA Secondary Structure Using Deep Graph Embeddings
Egbert Castro
Andrew Benz
Alexander Tong
Guy Wolf
Smita Krishnaswamy
Biomolecular graph analysis has recently gained much attention in the emerging field of geometric deep learning. Here we focus on organizing… (see more) biomolecular graphs in ways that expose meaningful relations and variations between them. We propose a geometric scattering autoencoder (GSAE) network for learning such graph embeddings. Our embedding network first extracts rich graph features using the recently proposed geometric scattering transform. Then, it leverages a semi-supervised variational autoencoder to extract a low-dimensional embedding that retains the information in these features that enable prediction of molecular properties as well as characterize graphs. We show that GSAE organizes RNA graphs both by structure and energy, accurately reflecting bistable RNA structures. Also, the model is generative and can sample new folding trajectories.
2020-12-31
IEEE International Conference on Big Data (unknown)
doi.org
arxiv.org
TrajectoryNet: A Dynamic Optimal Transport Network for Modeling Cellular Dynamics
Alexander Tong
Jessie Huang
Guy Wolf
David van Dijk
Smita Krishnaswamy
It is increasingly common to encounter data from dynamic processes captured by static cross-sectional measurements over time, particularly i… (see more)n biomedical settings. Recent attempts to model individual trajectories from this data use optimal transport to create pairwise matchings between time points. However, these methods cannot model continuous dynamics and non-linear paths that entities can take in these systems. To address this issue, we establish a link between continuous normalizing flows and dynamic optimal transport, that allows us to model the expected paths of points over time. Continuous normalizing flows are generally under constrained, as they are allowed to take an arbitrary path from the source to the target distribution. We present TrajectoryNet, which controls the continuous paths taken between distributions to produce dynamic optimal transport. We show how this is particularly applicable for studying cellular dynamics in data from single-cell RNA sequencing (scRNA-seq) technologies, and that TrajectoryNet improves upon recently proposed static optimal transport-based models that can be used for interpolating cellular distributions.
2020-07-13
International Conference on Machine Learning (Accept)
doi.org
proceedings.mlr.press