Probabilistic models for identifying regulation networks
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Summary
This talk will argue that one way of addressing the causal structure of the interactions between genes is a Bayesian framework, where the authors treat the measured expression level of each gene as a random variable and each regulatory interaction as a probabilistic dependency between such variables.
- Type
- article
- Published
- 2003-09-27
- Cited by
- 17
- References
- 0
- Access
- Open access
- OpenAlex
- https://openalex.org/W2057053591
- Semantic Scholar
- https://api.semanticscholar.org/CorpusID:19782453
Keywords
Computer science, Probabilistic logic, Expression (computer science), Bayesian network, Bayesian probability
References
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Cited by
- Identification of Relevant Protein-Gene Associations by Integrating Gene Expression Data and Transcriptional Regulatory Networks.
- Machine learning in low-level microarray analysis
- Partially observed bipartite network analysis to identify predictive connections in transcriptional regulatory networks
- MotifCut: regulatory motifs finding with maximum density subgraphs
- Inferring gene transcriptional modulatory relations: a genetical genomics approach.
- The Inferelator: an algorithm for learning parsimonious regulatory networks from systems-biology data sets de novo
- Bayesian analysis of signaling networks governing embryonic stem cell fate decisions
- Pathway analysis of Microarray data
- RegNetB: Predicting Relevant Regulator-Gene Relationships in Localized Prostate Tumor Samples
- An efficient method for fuzzy identification of regulatory events in gene expression time series data
- Statistical methods for identifying differentially expressed gene combinations.
- A Bayesian Noisy Logic Model for Inference of Transcription Factor Activity from Single Cell and Bulk Transcriptomic Data
- A Bayesian noisy logic model for inference of transcription factor activity from single cell and bulk transcriptomic data
- Integrating Multi-Omics with environmental data for precision health: A novel analytic framework and case study on prenatal mercury induced childhood fatty liver disease
- Finding Regulatory Motifs with Maximum Density Subgraphs
- Uncorrected proofs — not for distribution 3 Pathway Analysis of Microarray Data
- Signal Transduction Networks During Stress Responses in Arabidopsis: High-Throughput Analysis and Modelling
- Non-linear dimensionality reduction analysis of the apoptosis signalling network
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