ArticleMathematical biosciences and engineering : MBE2021
Synthetic data generation with probabilistic Bayesian Networks.
Article in Mathematical biosciences and engineering : MBE, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
12 citing papers in PubMed.
- Conserved residues in the Gα interface show subtype specificity in Gβγ coupling.The Journal of biological chemistry · 2026Article
- An Evaluation of Pretrained Generative Models for Augmenting Small Health Data: Comparative Modeling Study.Journal of medical Internet research · 2026Article
- Conserved Residues in the Gα interface show subtype specificity in Gβγ coupling.bioRxiv : the preprint server for biology · 2026Article
- AI-based methods for the assessment of DNA damage and repair mechanisms.Frontiers in systems biology · 2026Review
- Augmenting small tabular health data for training prognostic ensemble machine learning models using generative models.BMC medical informatics and decision making · 2025Article
- Reliable Bayesian Network Structure Learning in Biomedical Applications: Model Uncertainty Criterion and Its Operating Characteristics.bioRxiv : the preprint server for biology · 2025Article
- Changes in expression of VGF, SPECC1L, HLA-DRA and RANBP3L act with APOE E4 to alter risk for late onset Alzheimer's disease.Scientific reports · 2024Article
- Bayesian network models identify cooperative GPCR:G protein interactions that contribute to G protein coupling.The Journal of biological chemistry · 2024Article
- The Human Brainome: changes in expression of VGF, SPECC1L, HLA-DRA and RANBP3L act with APOE E4 to alter risk for late onset Alzheimer's disease.Research square · 2023Article
- Bayesian network models identify co-operative GPCR:G protein interactions that contribute to G protein coupling.bioRxiv : the preprint server for biology · 2023Article
- Article
- Causal Datasheet for Datasets: An Evaluation Guide for Real-World Data Analysis and Data Collection Design Using Bayesian Networks.Frontiers in artificial intelligence · 2021Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
Abstract
Bayesian Network (BN) modeling is a prominent and increasingly popular computational systems biology method. It aims to construct network graphs from the large heterogeneous biological datasets that reflect the underlying biological relationships. Currently, a variety of strategies exist for evaluating BN methodology performance, ranging from utilizing artificial benchmark datasets and models, to specialized biological benchmark datasets, to simulation studies that generate synthetic data from predefined network models. The last is arguably the most comprehensive approach; however, existing implementations often rely on explicit and implicit assumptions that may be unrealistic in a typical biological data analysis scenario, or are poorly equipped for automated arbitrary model generation. In this study, we develop a purely probabilistic simulation framework that addresses the demands of statistically sound simulations studies in an unbiased fashion. Additionally, we expand on our current understanding of the theoretical notions of causality and dependence / conditional independence in BNs and the Markov Blankets within.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.