arXiv:2606. 07677v1 Announce Type: cross Abstract: Electronic health records (EHR) pose large-scale multi-disease modeling problems in which many outcomes are rare and strongly influenced by shared risk factors.
arXiv:2607. 16053v1 Announce Type: cross Abstract: Gene regulatory networks (GRNs) link transcription factor (TF) proteins to their target genes, yet reconstructing these networks from genome-wide data remains challenging under practical and methodological constraints.
arXiv:2608. 12640v1 Announce Type: cross Abstract: Causal discovery aims to uncover the underlying causal relationships given data generated from a system.
arXiv:2607. 22934v1 Announce Type: cross Abstract: Learning causal graphs from interventional data is a challenging problem with broad applications.
arXiv:2608.24328v1 Announce Type: new Abstract: In this work, we propose a structural variant of the Factorial Hidden Markov Model (FHMM) for the analysis of disease trajectories in patients with Typ...
arXiv:2606. 15458v1 Announce Type: cross Abstract: Variational inference (VI) is a core engine of modern AI, enabling scalable approximate Bayesian learning and uncertainty-aware training of large probabilistic and generative models.
arXiv:2606. 05994v1 Announce Type: new Abstract: Medical knowledge graphs (MKGs) infused with clinical knowledge have been increasingly used to model electronic health records (EHRs) to support interpretable predictions in healthcare domain.
arXiv:2606. 17491v1 Announce Type: cross Abstract: Binary data factorization is common, but real-valued methods ignore discreteness and yield hard-to-interpret factors.
arXiv:2509.13267v3 Announce Type: replace-cross Abstract: A discrete Bayesian network is a directed acyclic graph (DAG) consisting of categorical variables. Two popular approaches for DBN modeling in...
arXiv:2607. 04527v1 Announce Type: cross Abstract: Biological systems exhibit a hierarchical structure, characterised by directed flow from upstream regulators to downstream effects.
The paper introduces ePID, an embedding-based approach that scales partial information decomposition (PID) to large symptom networks by compressing non‑focal symptoms into a low‑cardinality discrete embedding. Using a supervised Agglomerative Conditional Information Bottleneck (ACIB) embedding, ePID accurately recovers source‑unique, remainder‑unique, redundant, and synergistic components for each ordered source‑target pair across 83 real‑world datasets, outperforming 12 other embeddings. Applied to PHQ‑9 and the Interpersonal Reactivity Index, ePID reveals distinct patterns of redundancy and synergy that align with each instrument’s construction, demonstrating its ability to separate overlapping from interaction‑dependent information in symptom networks.
arXiv:2509. 01916v2 Announce Type: replace Abstract: Causal disentanglement from soft interventions is identifiable under the assumptions of linear interventional faithfulness and availability of both observational and interventional data.
arXiv:2606. 11570v1 Announce Type: cross Abstract: We propose a spectral-based, unsupervised representation learning framework to derive low-dimensional embeddings for clinical concepts and patients in rare disease cohorts from electronic health records, where data are high-dimensional but sample sizes are limited.