Avoiding pitfalls in L1-regularised inference of gene networks.

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LASSIM-A network inference toolbox for genome-wide mechanistic modeling.GeneSPIDER - gene regulatory network inference benchmarking with controlled network and data properties.

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Avoiding pitfalls in L1-regularised inference of gene networks.

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2014 nî lūn-bûn

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Avoiding pitfalls in L1-regularised inference of gene networks.

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Avoiding pitfalls in L1-regularised inference of gene networks.

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Avoiding pitfalls in L1-regularised inference of gene networks.

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Avoiding pitfalls in L1-regularised inference of gene networks.

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Avoiding pitfalls in L1-regularised inference of gene networks.

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Avoiding pitfalls in L1-regularised inference of gene networks.

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P1476

Avoiding pitfalls in L1-regularised inference of gene networks

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P2093

Matthew Studham

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P2860

Large-scale mapping and validation of Escherichia coli transcriptional regulation from a compendium of expression profiles

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Inferring genetic networks and identifying compound mode of action via expression profiling

Assessing the accuracy of prediction algorithms for classification: an overview

Singular value decomposition for genome-wide expression data processing and modeling

A comprehensive assessment of methods for de-novo reverse-engineering of genome-scale regulatory networks.

Robust data-driven incorporation of prior knowledge into the inference of dynamic regulatory networks.

Advances to Bayesian network inference for generating causal networks from observational biological data.

Exploration, visualization, and preprocessing of high-dimensional data.

Reverse engineering gene networks using singular value decomposition and robust regression.

P304

287-296

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scholarly article

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10.1039/C4MB00419A

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2014-11-07T00:00:00Z

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25377664

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Avoiding pitfalls in L1-regularised inference of gene networks.

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P2860

P2860

Avoiding pitfalls in L1-regularised inference of gene networks.

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P1476

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