activity
20242026
collaborators

6 papers

cs.LG2026

Conditionally Site-Independent Neural Evolution of Antibody Sequences

Stephen Zhewen Lu, Aakarsh Vermani, Kohei Sanno +4

Common deep learning approaches for antibody engineering focus on modeling the marginal distribution of sequences. By treating sequences as independent samples, however, these meth…

stat.ME2026

Bayesian inference of antibody evolutionary dynamics using multitype branching processes

Athanasios G. Bakis, Ashni A. Vora, Tatsuya Araki +8

When our immune system encounters foreign antigens (i.e., from pathogens), the B cells that produce our antibodies undergo a cyclic process of proliferation, mutation, and selectio…

q-bio.PE2025

Vector encoding of phylogenetic trees by ordered leaf attachment

Harry Richman, Cheng Zhang, Frederick A. Matsen

As part of work to connect phylogenetics with machine learning, there has been considerable recent interest in vector encodings of phylogenetic trees. We present a simple new "orde…

stat.ML2025

PhyloVAE: Unsupervised Learning of Phylogenetic Trees via Variational Autoencoders

Tianyu Xie, Harry Richman, Jiansi Gao +2

Learning informative representations of phylogenetic tree structures is essential for analyzing evolutionary relationships. Classical distance-based methods have been widely used t…

q-bio.PE2024

Finding high posterior density phylogenies by systematically extending a directed acyclic graph

Chris Jennings-Shaffer, David H Rich, Matthew Macaulay +7

Bayesian phylogenetics typically estimates a posterior distribution, or aspects thereof, using Markov chain Monte Carlo methods. These methods integrate over tree space by applying…

stat.ML2024

Variational Bayesian Phylogenetic Inference with Semi-implicit Branch Length Distributions

Tianyu Xie, Frederick A. Matsen, Marc A. Suchard +1

Reconstructing the evolutionary history relating a collection of molecular sequences is the main subject of modern Bayesian phylogenetic inference. However, the commonly used Marko…