Hasil untuk "Analysis"

S. Eberhart

Bastian Goldlücke

Andreas Krämer, Jeff Green, Jack Pollard et al.

Motivation: Prior biological knowledge greatly facilitates the meaningful interpretation of gene-expression data. Causal networks constructed from individual relationships curated from the literature are particularly suited for this task, since they create mechanistic hypotheses that explain the expression changes observed in datasets. Results: We present and discuss a suite of algorithms and tools for inferring and scoring regulator networks upstream of gene-expression data based on a large-scale causal network derived from the Ingenuity Knowledge Base. We extend the method to predict downstream effects on biological functions and diseases and demonstrate the validity of our approach by applying it to example datasets. Availability: The causal analytics tools ‘Upstream Regulator Analysis', ‘Mechanistic Networks', ‘Causal Network Analysis' and ‘Downstream Effects Analysis' are implemented and available within Ingenuity Pathway Analysis (IPA, http://www.ingenuity.com). Supplementary information: Supplementary material is available at Bioinformatics online.

Jian Yang, S. H. Lee, M. Goddard et al.

G. Fernald, E. Capriotti, Roxana Daneshjou et al.

E. Candès, Xiaodong Li, Yi Ma et al.

This article is about a curious phenomenon. Suppose we have a data matrix, which is the superposition of a low-rank component and a sparse component. Can we recover each component individually? We prove that under some suitable assumptions, it is possible to recover both the low-rank and the sparse components exactly by solving a very convenient convex program called Principal Component Pursuit; among all feasible decompositions, simply minimize a weighted combination of the nuclear norm and of the ℓ1 norm. This suggests the possibility of a principled approach to robust principal component analysis since our methodology and results assert that one can recover the principal components of a data matrix even though a positive fraction of its entries are arbitrarily corrupted. This extends to the situation where a fraction of the entries are missing as well. We discuss an algorithm for solving this optimization problem, and present applications in the area of video surveillance, where our methodology allows for the detection of objects in a cluttered background, and in the area of face recognition, where it offers a principled way of removing shadows and specularities in images of faces.

M. Miles, A. Huberman, J. Saldaña

D. Comaniciu, P. Meer

I. Jolliffe

R. Boyatzis

J. Devereux, P. Haeberli, O. Smithies

G. Box, D. Cox

N. Mantel, W. Haenszel

Mahesh S. Patel

M. Wilkins, E. Gasteiger, A. Bairoch et al.

J. Harborne

L. Green, D. Wagner, J. Glogowski et al.

R. Bansal

Ross Ihaka