Noise and nonlinearities in high-throughput data

High-throughput data analyses are becoming common in biology, communications, economics and sociology. The vast amounts of data are usually represented in the form of matrices and can be considered as knowledge networks. Spectra-based approaches have proved useful in extracting hidden information within such networks and for estimating missing data, but these methods are based essentially on linear assumptions. The physical models of matching, when applicable, often suggest non-linear mechanisms, that may sometimes be identified as noise. The use of non-linear models in data analysis, however, may require the introduction of many parameters, which lowers the statistical weight of the model. According to the quality of data, a simpler linear analysis may be more convenient than more complex approaches. In this paper, we show how a simple non-parametric Bayesian model may be used to explore the role of non-linearities and noise in synthetic and experimental data sets.Comment: 12 pages, 3 figure

Involvement of circular intermediates in the transfer of T-DNA from Agrobacterium tumefaciens to plant cells

Co-cultivation of Agrobacterium tumefaciens with plant cells leads to the induction of circular copies of the T-DNA segment of the large tumour-inducing (Ti) plasmid in the bacterial cells. These circular molecules are presumably intermediates in DNA transfer from the A. tumefaciens genome to the plant cells. In support of this suggestion, the junction of the T-DNA circles occurs precisely in the 25-base pair terminal sequence involved in T-DNA transfer