References of "Journee, Michel"
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See detailLow-rank optimization on the cone of positive semidefinite matrices
Journee, Michel ULg; Bach, F.; Absil, P.-A. et al

in SIAM Journal on Optimization (2010), 20(5)

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See detailGeneralized power method for sparse principal component analysis
Nesterov, Yurii; Richtarik, Peter; Journee, Michel ULg et al

in Journal of Machine Learning Research (2010), 11

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See detailElucidating the altered transcriptional programs in breast cancer using independent component analysis
Teschendorff, A. E.; Journee, Michel ULg; Absil, P.-A. et al

in PLoS Computational Biology (2007), 3(8), 1539-1554

The quantity of mRNA transcripts in a cell is determined by a complex interplay of cooperative and counteracting biological processes. Independent Component Analysis ( ICA) is one of a few number of ... [more ▼]

The quantity of mRNA transcripts in a cell is determined by a complex interplay of cooperative and counteracting biological processes. Independent Component Analysis ( ICA) is one of a few number of unsupervised algorithms that have been applied to microarray gene expression data in an attempt to understand phenotype differences in terms of changes in the activation/ inhibition patterns of biological pathways. While the ICA model has been shown to outperform other linear representations of the data such as Principal Components Analysis ( PCA), a validation using explicit pathway and regulatory element information has not yet been performed. We apply a range of popular ICA algorithms to six of the largest microarray cancer datasets and use pathway- knowledge and regulatory- element databases for validation. We show that ICA outperforms PCA and clustering- based methods in that ICA components map closer to known cancer- related pathways, regulatory modules, and cancer phenotypes. Furthermore, we identify cancer signalling and oncogenic pathways and regulatory modules that play a prominent role in breast cancer and relate the differential activation patterns of these to breast cancer phenotypes. Importantly, we find novel associations linking immune response and epithelial - mesenchymal transition pathways with estrogen receptor status and histological grade, respectively. In addition, we find associations linking the activity levels of biological pathways and transcription factors ( NF1 and NFAT) with clinical outcome in breast cancer. ICA provides a framework for a more biologically relevant interpretation of genomewide transcriptomic data. Adopting ICA as the analysis tool of choice will help understand the phenotype - pathway relationship and thus help elucidate the molecular taxonomy of heterogeneous cancers and of other complex genetic diseases. [less ▲]

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See detailComparative assessment of old and new suboptimal control schemes on three example processes
Journee, Michel ULg; Schweickhardt, Tobias; Allgöwer, Frank

in International Journal of Tomography & Statistics (2007), 6(S07), 45--50

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See detailGradient-optimization on the orthogonal group for Independent Component Analysis
Journee, Michel ULg; Absil, P.-A.; Sepulchre, Rodolphe ULg

in 7th International Conference on Independent Component Analysis and Blind Signal Separation (ICA 2007) (2007)

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See detailGeometric optimization methods for independent component analysis applied on gene expression data
Journee, Michel ULg; Teschendorff, A. E.; Absil, P.-A. et al

in Proceedings of the IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP 2007) (2007)

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See detailGeometric optimization methods for the analysis of gene expression data
Journee, Michel ULg; Teschendorff, A. E.; Absil, P.-A. et al

Part of book (2007)

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See detailComparative assessment of old and new suboptimal control schemes on three example processes
Journee, Michel ULg; Schweickhardt, T.; Allgöwer, Frank

in Proceedings of the 13th IFAC Workshop on Control Applications of Optimisation, Paris (2006)

Detailed reference viewed: 4 (2 ULg)