Mines ParisTech

About: Mines ParisTech is a education organization based out in Paris, France. It is known for research contribution in the topics: Finite element method & Microstructure. The organization has 6564 authors who have published 11676 publications receiving 359898 citations. The organization is also known as: École nationale supérieure des mines de Paris & École des mines de Paris.

Ergodicity and integral range

Abstract: SUMMARY When attempting to estimate stereological parameters starting from measurements in a limited domain, the question has to be addressed of how large the domain size must be in order to ensure representative measurements. To answer that question, the concept of ‘integral range’ is introduced. It allows a comparison between the scale of the phenomenon under study and the scale of observation. The integral range can be assessed using curves of dispersion variance, which can also yield empirical laws of change of scale.

Supervised reconstruction of biological networks with local models

Abstract: Motivation: Inference and reconstruction of biological networks from heterogeneous data is currently an active research subject with several important applications in systems biology. The problem has been attacked from many different points of view with varying degrees of success. In particular, predicting new edges with a reasonable false discovery rate is highly demanded for practical applications, but remains extremely challenging due to the sparsity of the networks of interest. Results: While most previous approaches based on the partial knowledge of the network to be inferred build global models to predict new edges over the network, we introduce here a novel method which predicts whether there is an edge from a newly added vertex to each of the vertices of a known network using local models. This involves learning individually a certain subnetwork associated with each vertex of the known network, then using the discovered classification rule associated with only that vertex to predict the edge to the new vertex. Excellent experimental results are shown in the case of metabolic and protein–protein interaction network reconstruction from a variety of genomic data. Availability: An implementation of the proposed algorithm is available upon request from the authors. Contact: Jean-Philippe.Vert@ensmp.fr

Self-consistent modeling of the flow behavior of wrought alpha/beta titanium alloys under isothermal and nonisothermal hot-working conditions

Abstract: A self-consistent model was applied to predict the plastic flow behavior during hot working of alpha/beta titanium alloys with wrought (equiaxed alpha) microstructures as a function of the flow behavior and volume fractions of the individual phases. For this purpose, constitutive relations that incorporated composition-dependent strength coefficients were determined for the alpha and beta phases. With these constitutive relations and measurements of the specific compositions and volume fractions of the two phases at hot-working temperatures, the flow stress dependence on temperature under nominally isothermal conditions and the (average) strain rates in the individual phases were predicted for Ti-6Al-4V. The effect of temperature transients during hot deformation on the flow stress under nonisothermal (conventional) forging conditions and under nominally isothermal, high strain-rate conditions was also established using the self-consistent modeling approach. In these instances, the effect of a rapid temperature drop or rise, respectively, on the retention of a metastable microstructure was quantified. The predicted flow behaviors showed good agreement with experimental measurements.

Drug side-effect prediction based on the integration of chemical and biological spaces.

Abstract: Drug side-effects, or adverse drug reactions, have become a major public health concern and remain one of the main causes of drug failure and of drug withdrawal once they have reached the market. Therefore, the identification of potential severe side-effects is a challenging issue. In this paper, we develop a new method to predict potential side-effect profiles of drug candidate molecules based on their chemical structures and target protein information on a large scale. We propose several extensions of kernel regression model for multiple responses to deal with heterogeneous data sources. The originality lies in the integration of the chemical space of drug chemical structures and the biological space of drug target proteins in a unified framework. As a result, we demonstrate the usefulness of the proposed method on the simultaneous prediction of 969 side-effects for approved drugs from their chemical substructure and target protein profiles and show that the prediction accuracy consistently improves owi...