Structure Learning in Nested Effects Models

DOI10.2202/1544-6115.1332zbMath1276.92075arXiv0710.4481OpenAlexW3104952140WikidataQ51891892 ScholiaQ51891892MaRDI QIDQ2863959

Achim Tresch, Florian Markowetz

Publication date: 5 December 2013

Published in: Statistical Applications in Genetics and Molecular Biology (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/0710.4481

zbMATH Keywords

feature selectionmodel identifiabilitygene perturbationpathway reconstruction

Mathematics Subject Classification ID

Applications of statistics to biology and medical sciences; meta analysis (62P10) Genetics and epigenetics (92D10) Graphical methods in statistics (62A09)

Related Items (5)

Context-Specific Nested Effects Models ⋮ Refining cellular pathway models using an ensemble of heterogeneous data sources ⋮ Autoregressive models for gene regulatory network inference: sparsity, stability and causality issues ⋮ Reconstructing evolving signalling networks by hidden Markov nested effects models ⋮ Review on statistical methods for gene network reconstruction using expression data

Uses Software

• limma

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