Abstract:Qualitative models provide crucial instruments for modelling complex biological systems. While advances in automated reasoning and symbolic encodings have enabled rigorous inference of these models from data, the process remains highly fragile. First, biological measurement errors inevitably propagate into formal model specifications. Second, when a specification becomes unsatisfiable, distinguishing between fundamental design flaws and minor technical errors is notoriously difficult. This uncertainty often leads to under-specification, as it is unclear which observations are still ``safe'' to incorporate. To overcome these challenges, we introduce a robust inference method based on weighted MaxSMT. By encoding uncertain biological observations as weighted soft constraints, our approach enables the solver to identify a model best reflecting the observations, even with some conflicting constraints. Our method allows for Boolean and multi-valued variable domains, alongside observations derived from discretisation (level constraints) and differential expression (ordering constraints). We show our approach can be used to successfully infer neural cell differentiation models from prior-knowledge networks with 200--1300 genes using ordering constraints on all included genes.
| Subjects: | Molecular Networks (q-bio.MN); Machine Learning (cs.LG); Logic in Computer Science (cs.LO) |
| Cite as: | arXiv:2605.07433 [q-bio.MN] |
| (or arXiv:2605.07433v1 [q-bio.MN] for this version) | |
| https://doi.org/10.48550/arXiv.2605.07433 arXiv-issued DOI via DataCite (pending registration) |
From: Samuel Pastva [view email]
[v1]
Fri, 8 May 2026 08:34:50 UTC (484 KB)
此内容由惯性聚合(RSS阅读器)自动聚合整理，仅供阅读参考。 原文来自 — 版权归原作者所有。