arXiv subjects

Yong See Foo

Publications and source records attributed to Yong See Foo.

4 recordsLinked to original sources

arXiv2026

Reliable model selection in the presence of parameter non-identifiability

Mathematical models are invaluable for understanding and predicting how biological systems behave, although their construction requires specifying mechanisms and relationships that are often not perfectly known. In the presence of multiple competing models, model uncertainty should be accounted for when performing inference based on available data. Bayesian model selection is a framework for testing mechanistic hypotheses and generating predictions under model uncertainty, which generally requires computation of the model evidence. In this work, we investigate the reliability of evidence computation methods when parameter non-identifiability -- the inability to distinguish between parameter values given available data -- is present, and find that deterministic evidence approximations can produce misleading model selection results because their underlying assumptions are violated. We propose a novel implementation of adaptive multiple importance sampling for evidence estimation, and demonstrate its robustness against non-identifiability. We use ecological case studies to demonstrate how simple model selection methods fail to produce accurate results, whereas our method yields model selection results that are comparable to those obtained by Markov chain Monte Carlo methods at substantially lower computational cost. Given the pervasiveness of parameter non-identifiability in mathematical biology, this work provides a practical approach to reliable model selection in the presence of poorly identified parameters.

stat.ME↗

arXiv2026

Quantifying structural uncertainty in chemical reaction network inference

Dynamical systems in biology are complex, and one often does not have comprehensive knowledge about the interactions involved. Chemical reaction network (CRN) inference aims to identify, from observing species concentrations over time, the unknown reactions between the species. Existing approaches such as sparse regularisation largely focus on identifying a single, most likely CRN, without addressing uncertainty about the network structure. However, it is important to quantify structural uncertainty to have confidence in our inference and predictions. In this work, we explore how effective sparse regularisation methods are for quantifying structural uncertainty. Locally optimal solutions to sparse regularisation are mapped to CRN structures; however, it is unclear whether this approach encompasses all plausible CRNs. We find that inducing sparsity with nonconvex penalty functions results in better coverage of the plausible CRNs compared to the popular lasso regularisation. To validate our approach, we apply our methods to real-world data examples, and are able to simultaneously recover reactions proposed across multiple literature sources for a reaction system. Our emphasis on network-level probabilities enables a novel, hierarchical representation of structural ambiguities in the space of CRNs. This representation translates into alternative reaction pathways suggested by the available data, thus guiding the efforts of future experimental design.

stat.ME↗

arXiv2023

Haplotype frequency inference from pooled genetic data with a latent multinomial model

In genetic studies, haplotype data provide more refined information than data about separate genetic markers. However, large-scale studies that genotype hundreds to thousands of individuals may only provide results of pooled data, where only the total allele counts of each marker in each pool are reported. Methods for inferring haplotype frequencies from pooled genetic data that scale well with pool size rely on a normal approximation, which we observe to produce unreliable inference when applied to real data. We illustrate cases where the approximation breaks down, due to the normal covariance matrix being near-singular. As an alternative to approximate methods, in this paper we propose exact methods to infer haplotype frequencies from pooled genetic data based on a latent multinomial model, where the observed allele counts are considered integer combinations of latent, unobserved haplotype counts. One of our methods, latent count sampling via Markov bases, achieves approximately linear runtime with respect to pool size. Our exact methods produce more accurate inference over existing approximate methods for synthetic data and for data based on haplotype information from the 1000 Genomes Project. We also demonstrate how our methods can be applied to time-series of pooled genetic data, as a proof of concept of how our methods are relevant to more complex hierarchical settings, such as spatiotemporal models.

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arXiv2021

A Comparison of Bayesian Inference Techniques for Sparse Factor Analysis

Dimension reduction algorithms aim to discover latent variables which describe underlying structures in high-dimensional data. Methods such as factor analysis and principal component analysis have the downside of not offering much interpretability of its inferred latent variables. Sparse factor analysis addresses this issue by imposing sparsity on its factor loadings, allowing each latent variable to be related to only a subset of features, thus increasing interpretability. Sparse factor analysis has been used in a wide range of areas including genomics, signal processing, and economics. We compare two Bayesian inference techniques for sparse factor analysis, namely Markov chain Monte Carlo (MCMC), and variational inference (VI). VI is computationally faster than MCMC, at the cost of a loss in accuracy. We derive MCMC and VI algorithms and perform a comparison using both simulated and biological data, demonstrating that the higher computational efficiency of VI is desirable over the small gain in accuracy when using MCMC. Our implementation of MCMC and VI algorithms for sparse factor analysis is available at https://github.com/ysfoo/sparsefactor.

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