9 research outputs found
Knowledge-based gene expression classification via matrix factorization
Motivation: Modern machine learning methods based on matrix decomposition techniques, like independent component analysis (ICA) or non-negative matrix factorization (NMF), provide new and efficient analysis tools which are currently explored to analyze gene expression profiles. These exploratory feature extraction techniques yield expression modes (ICA) or metagenes (NMF). These extracted features are considered indicative of underlying regulatory processes. They can as well be applied to the classification of gene expression datasets by grouping samples into different categories for diagnostic purposes or group genes into functional categories for further investigation of related metabolic pathways and regulatory networks.
Results: In this study we focus on unsupervised matrix factorization techniques and apply ICA and sparse NMF to microarray datasets. The latter monitor the gene expression levels of human peripheral blood cells during differentiation from monocytes to macrophages. We show that these tools are able to identify relevant signatures in the deduced component matrices and extract informative sets of marker genes from these gene expression profiles. The methods rely on the joint discriminative power of a set of marker genes rather than on single marker genes. With these sets of marker genes, corroborated by leave-one-out or random forest cross-validation, the datasets could easily be classified into related diagnostic categories. The latter correspond to either monocytes versus macrophages or healthy vs Niemann Pick C disease patients.Siemens AG, MunichDFG (Graduate College 638)DAAD (PPP Luso - Alem˜a and PPP Hispano - Alemanas
Radio imaging with information field theory
Data from radio interferometers provide a substantial challenge for
statisticians. It is incomplete, noise-dominated and originates from a
non-trivial measurement process. The signal is not only corrupted by imperfect
measurement devices but also from effects like fluctuations in the ionosphere
that act as a distortion screen. In this paper we focus on the imaging part of
data reduction in radio astronomy and present RESOLVE, a Bayesian imaging
algorithm for radio interferometry in its new incarnation. It is formulated in
the language of information field theory. Solely by algorithmic advances the
inference could be sped up significantly and behaves noticeably more stable
now. This is one more step towards a fully user-friendly version of RESOLVE
which can be applied routinely by astronomers.Comment: 5 pages, 3 figure
Dynamics of the Lithium Amide/Alkyllithium Interactions: Mixed Dimers and Beyond
International audienc