What is the Machine Learning?

Applications of machine learning tools to problems of physical interest are often criticized for producing sensitivity at the expense of transparency. To address this concern, we explore a data planing procedure for identifying combinations of variables -- aided by physical intuition -- that can discriminate signal from background. Weights are introduced to smooth away the features in a given variable(s). New networks are then trained on this modified data. Observed decreases in sensitivity diagnose the variable's discriminating power. Planing also allows the investigation of the linear versus non-linear nature of the boundaries between signal and background. We demonstrate the efficacy of this approach using a toy example, followed by an application to an idealized heavy resonance scenario at the Large Hadron Collider. By unpacking the information being utilized by these algorithms, this method puts in context what it means for a machine to learn.Comment: 6 pages, 3 figures. Version published in PRD, discussion adde

Valorisation de coproduits de soja : propriétés fonctionnelles et liens entre rhéologie interfaciale et volumique

National audienc

A multi-scale approach of the foaming and emulsifying properties of Soybean co-products : comparisons and synergy with protein solutions

International audienc

Emulsions solely stabilized by soybean fibers : phase diagram and rheological studies

International audienc

Glyceollins trigger anti-proliferative effects through estradiol-dependent and independent pathways in breast cancer cells

International audienceBACKGROUND: Estrogen receptors (ER) α and β are found in both women and men in many tissues, where they have different functions, including having roles in cell proliferation and differentiation of the reproductive tract. In addition to estradiol (E2), a natural hormone, numerous compounds are able to bind ERs and modulate their activities. Among these compounds, phytoestrogens such as isoflavones, which are found in plants, are promising therapeutics for several pathologies. Glyceollins are second metabolites of isoflavones that are mainly produced in soybean in response to an elicitor. They have potentially therapeutic actions in breast cancer by reducing the proliferation of cancer cells. However, the molecular mechanisms driving these effects remain elusive.METHODS: First, to determine the proliferative or anti-proliferative effects of glyceollins, in vivo and in vitro approaches were used. The length of epithelial duct in mammary gland as well as uterotrophy after treatment by E2 and glyceollins and their effect on proliferation of different breast cell line were assessed. Secondly, the ability of glyceollin to activate ER was assessed by luciferase assay. Finally, to unravel molecular mechanisms involved by glyceollins, transcriptomic analysis was performed on MCF-7 breast cancer cells.RESULTS: In this study, we show that synthetic versions of glyceollin I and II exert anti-proliferative effects in vivo in mouse mammary glands and in vitro in different ER-positive and ER-negative breast cell lines. Using transcriptomic analysis, we produce for the first time an integrated view of gene regulation in response to glyceollins and reveal that these phytochemicals act through at least two major pathways. One pathway involving FOXM1 and ERα is directly linked to proliferation. The other involves the HIF family and reveals that stress is a potential factor in the anti-proliferative effects of glyceollins due to its role in increasing the expression of REDD1, an mTORC1 inhibitor.CONCLUSION: Overall, our study clearly shows that glyceollins exert anti-proliferative effects by reducing the expression of genes encoding cell cycle and mitosis-associated factors and biomarkers overexpressed in cancers and by increasing the expression of growth arrest-related genes. These results reinforce the therapeutic potential of glyceollins for breast cancer

Additional file 5: Figure S3. of Glyceollins trigger anti-proliferative effects through estradiol-dependent and independent pathways in breast cancer cells

GO enrichment analysis of different treatment-related expression patterns. Eight expression patterns are matched with a selection of GO terms from the ontology “phenotypes,” “biological process,” “cellular component” and “pathways.” The numbers of genes associated with each GO term are indicated in the first column. Enrichment is indicated by bolded rectangles, where the first number indicates the number of genes found in our analysis and the second the number expected with a random list of genes. Overrepresented genes in a specific GO term are shown in red, and underrepresented genes are shown in blue. (TIFF 2724 kb