ArticleBioinformatics (Oxford, England)2013
BNFinder2: Faster Bayesian network learning and Bayesian classification.
Article in Bioinformatics (Oxford, England), 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed.
- Opportunities and challenges of single-cell and spatially resolved genomics methods for neuroscience discovery.Nature neuroscience · 2024Review
- TissueNexus: a database of human tissue functional gene networks built with a large compendium of curated RNA-seq data.Nucleic acids research · 2022Article
- Probabilistic Graphical Models Applied to Biological Networks.Advances in experimental medicine and biology · 2021Article
- Reverse engineering directed gene regulatory networks from transcriptomics and proteomics data of biomining bacterial communities with approximate Bayesian computation and steady-state signalling simulations.BMC bioinformatics · 2020Article
- Article
- Computational dynamic approaches for temporal omics data with applications to systems medicine.BioData mining · 2017Review
- Inferring Broad Regulatory Biology from Time Course Data: Have We Reached an Upper Bound under Constraints Typical of In Vivo Studies?PloS one · 2015Article
- CausalTrail: Testing hypothesis using causal Bayesian networks.F1000Research · 2015Article
- Dynamic Bayesian Network Modeling of the Interplay between EGFR and Hedgehog Signaling.PloS one · 2015Article
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Authors and funding
4 authors.
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Abstract
summaryBayesian Networks (BNs) are versatile probabilistic models applicable to many different biological phenomena. In biological applications the structure of the network is usually unknown and needs to be inferred from experimental data. BNFinder is a fast software implementation of an exact algorithm for finding the optimal structure of the network given a number of experimental observations. Its second version, presented in this article, represents a major improvement over the previous version. The improvements include (i) a parallelized learning algorithm leading to an order of magnitude speed-ups in BN structure learning time; (ii) inclusion of an additional scoring function based on mutual information criteria; (iii) possibility of choosing the resulting network specificity based on statistical criteria and (iv) a new module for classification by BNs, including cross-validation scheme and classifier quality measurements with receiver operator characteristic scores. AVAILABILITY AND IMPLEMENTATION: BNFinder2 is implemented in python and freely available under the GNU general public license at the project Web site https://launchpad.net/bnfinder, together with a user's manual, introductory tutorial and supplementary methods.
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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.