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Tom Michoel

Showing results (1-10 of 73) with videos related to

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Royal Society Open Science|August 17, 2019
Accurate wisdom of the crowd from unsupervised dimension reductionLingfei Wang, Tom Michoel
Methods in Molecular Biology (Clifton, N.J.)|December 15, 2018
Whole-Transcriptome Causal Network Inference with Genomic and Transcriptomic DataLingfei Wang, Tom Michoel
Physical Review. E, Statistical, Nonlinear, and Soft Matter Physics|December 11, 2012
Alignment and integration of complex networks by hypergraph-based spectral clusteringTom Michoel, Bruno Nachtergaele
Bioinformatics (Oxford, England)|September 11, 2012
Context-specific transcriptional regulatory network inference from global gene expression maps using double two-way t-testsJianlong Qi, Tom Michoel
Frontiers in Endocrinology|September 5, 2022
eQTLs as causal instruments for the reconstruction of hormone linked gene networksSean Bankier, Tom Michoel
BMC Bioinformatics|October 28, 2021
A Graph Feature Auto-Encoder for the prediction of unobserved node features on biological networksRamin Hasibi, Tom Michoel
Plos Computational Biology|August 19, 2017
Efficient and accurate causal inference with hidden confounders from genome-transcriptome variation dataLingfei Wang, Tom Michoel
Bioinformatics Advances|December 24, 2024
Predicting the genetic component of gene expression using gene regulatory networksGutama Ibrahim Mohammad, Tom Michoel
G3 (Bethesda, Md.)|December 5, 2021
Restricted maximum-likelihood method for learning latent variance components in gene expression data with known and unknown confoundersMuhammad Ammar Malik, Tom Michoel
Molecular Omics|January 13, 2021
Comparison between instrumental variable and mediation-based methods for reconstructing causal gene networks in yeastAdriaan-Alexander Ludl, Tom Michoel
Pageof 8

Showing results (1-10 of 73) with videos related to

Sort By:
Pageof 8
Royal Society Open Science|August 17, 2019
Accurate wisdom of the crowd from unsupervised dimension reductionLingfei Wang, Tom Michoel
Methods in Molecular Biology (Clifton, N.J.)|December 15, 2018
Whole-Transcriptome Causal Network Inference with Genomic and Transcriptomic DataLingfei Wang, Tom Michoel
Physical Review. E, Statistical, Nonlinear, and Soft Matter Physics|December 11, 2012
Alignment and integration of complex networks by hypergraph-based spectral clusteringTom Michoel, Bruno Nachtergaele
Bioinformatics (Oxford, England)|September 11, 2012
Context-specific transcriptional regulatory network inference from global gene expression maps using double two-way t-testsJianlong Qi, Tom Michoel
Frontiers in Endocrinology|September 5, 2022
eQTLs as causal instruments for the reconstruction of hormone linked gene networksSean Bankier, Tom Michoel
BMC Bioinformatics|October 28, 2021
A Graph Feature Auto-Encoder for the prediction of unobserved node features on biological networksRamin Hasibi, Tom Michoel
Plos Computational Biology|August 19, 2017
Efficient and accurate causal inference with hidden confounders from genome-transcriptome variation dataLingfei Wang, Tom Michoel
Bioinformatics Advances|December 24, 2024
Predicting the genetic component of gene expression using gene regulatory networksGutama Ibrahim Mohammad, Tom Michoel
G3 (Bethesda, Md.)|December 5, 2021
Restricted maximum-likelihood method for learning latent variance components in gene expression data with known and unknown confoundersMuhammad Ammar Malik, Tom Michoel
Molecular Omics|January 13, 2021
Comparison between instrumental variable and mediation-based methods for reconstructing causal gene networks in yeastAdriaan-Alexander Ludl, Tom Michoel
Pageof 8