Efficient Inference for Sparse Latent Variable Models of Transcriptional Regulation.

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Efficient Inference for Sparse Latent Variable Models of Transcriptional Regulation.

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2017 nî lūn-bûn

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2017年の論文

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2017年論文

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2017年論文

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2017年論文

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2017年論文

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2017年論文

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2017年论文

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Efficient Inference for Sparse Latent Variable Models of Transcriptional Regulation.

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Efficient Inference for Sparse Latent Variable Models of Transcriptional Regulation.

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Efficient Inference for Sparse Latent Variable Models of Transcriptional Regulation.

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Efficient Inference for Sparse Latent Variable Models of Transcriptional Regulation.

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Efficient Inference for Sparse Latent Variable Models of Transcriptional Regulation.

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Efficient Inference for Sparse Latent Variable Models of Transcriptional Regulation.

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P1476

Efficient Inference for Sparse Latent Variable Models of Transcriptional Regulation.

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P2093

Neil D Lawrence

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Zhenwen Dai

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P2860

Wisdom of crowds for robust gene network inference

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Rv3133c/dosR is a transcription factor that mediates the hypoxic response of Mycobacterium tuberculosis

The Mycobacterium tuberculosis regulatory network and hypoxia

Factor analysis for gene regulatory networks and transcription factor activity profiles.

Computational analysis of cell-to-cell heterogeneity in single-cell RNA-sequencing data reveals hidden subpopulations of cells.

The dynamic architecture of the metabolic switch in Streptomyces coelicolor.

Revealing strengths and weaknesses of methods for gene network inference.

Identification of the binding sites of regulatory proteins in bacterial genomes

Extracting regulator activity profiles by integration of de novo motifs and expression data: characterizing key regulators of nutrient depletion responses in Streptomyces coelicolor.

P304

3776-3783

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P31

scholarly article

P356

10.1093/BIOINFORMATICS/BTX508

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P407

English

P433

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P478

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P50

Magnus Rattray

Mudassar Iqbal

P577

2017-12-01T00:00:00Z

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P698

28961802

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P932

5860323

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