PREDICTION DU REPLIEMENT PEPTIDIQUE GRACE AUX INVARIANTS STRUCTURAUX DE PROTEINES HOMOLOGUES

PARIS-BIUSJ-Physique recherche (751052113) / SudocCentre Technique Livre Ens. Sup. (774682301) / SudocSudocFranceF

Fast and Accurate Approaches for Large-Scale, Automated Mapping of Food Diaries on Food Composition Tables

Aim of Study: The use of weighed food diaries in nutritional studies provides a powerful method to quantify food and nutrient intakes. Yet, mapping these records onto food composition tables (FCTs) is a challenging, time-consuming and error-prone process. Experts make this effort manually and no automation has been previously proposed. Our study aimed to assess automated approaches to map food items onto FCTs.Methods: We used food diaries (~170,000 records pertaining to 4,200 unique food items) from the DiOGenes randomized clinical trial. We attempted to map these items onto six FCTs available from the EuroFIR resource. Two approaches were tested: the first was based solely on food name similarity (fuzzy matching). The second used a machine learning approach (C5.0 classifier) combining both fuzzy matching and food energy. We tested mapping food items using their original names and also an English-translation. Top matching pairs were reviewed manually to derive performance metrics: precision (the percentage of correctly mapped items) and recall (percentage of mapped items).Results: The simpler approach: fuzzy matching, provided very good performance. Under a relaxed threshold (score > 50%), this approach enabled to remap 99.49% of the items with a precision of 88.75%. With a slightly more stringent threshold (score > 63%), the precision could be significantly improved to 96.81% while keeping a recall rate > 95% (i.e., only 5% of the queried items would not be mapped). The machine learning approach did not lead to any improvements compared to the fuzzy matching. However, it could increase substantially the recall rate for food items without any clear equivalent in the FCTs (+7 and +20% when mapping items using their original or English-translated names). Our approaches have been implemented as R packages and are freely available from GitHub.Conclusion: This study is the first to provide automated approaches for large-scale food item mapping onto FCTs. We demonstrate that both high precision and recall can be achieved. Our solutions can be used with any FCT and do not require any programming background. These methodologies and findings are useful to any small or large nutritional study (observational as well as interventional)

Detecting Local High-Scoring Segments: a First-Stage Approach for Genome-Wide Association Studies

Genetic epidemiology aims at identifying biological mechanisms responsible for human diseases. Genome-wide association studies, made possible by recent improvements in genotyping technologies, are now promisingly investigated. In these studies, common first-stage strategies focus on marginal effects but lead to multiple-testing and are unable to capture the possibly complex interplay between genetic factors.We have adapted the use of the local score statistic, already successfully applied to analyse long molecular sequences. Via sum statistics, this method captures local and possible distant dependences between markers. Dedicated to genome-wide association studies, it is fast to compute, able to handle large datasets, circumvents the multiple-testing problem and outlines a set of genomic regions (segments) for further analyses. Applied to simulated and real data, our approach outperforms classical Bonferroni and FDR corrections for multiple-testing. It is implemented in a software termed LHiSA for Local High-scoring Segments for Association and available at: http://stat.genopole.cnrs.fr/software/lhisa.

Image_1_Fast and Accurate Approaches for Large-Scale, Automated Mapping of Food Diaries on Food Composition Tables.PDF

<p>Aim of Study: The use of weighed food diaries in nutritional studies provides a powerful method to quantify food and nutrient intakes. Yet, mapping these records onto food composition tables (FCTs) is a challenging, time-consuming and error-prone process. Experts make this effort manually and no automation has been previously proposed. Our study aimed to assess automated approaches to map food items onto FCTs.</p><p>Methods: We used food diaries (~170,000 records pertaining to 4,200 unique food items) from the DiOGenes randomized clinical trial. We attempted to map these items onto six FCTs available from the EuroFIR resource. Two approaches were tested: the first was based solely on food name similarity (fuzzy matching). The second used a machine learning approach (C5.0 classifier) combining both fuzzy matching and food energy. We tested mapping food items using their original names and also an English-translation. Top matching pairs were reviewed manually to derive performance metrics: precision (the percentage of correctly mapped items) and recall (percentage of mapped items).</p><p>Results: The simpler approach: fuzzy matching, provided very good performance. Under a relaxed threshold (score > 50%), this approach enabled to remap 99.49% of the items with a precision of 88.75%. With a slightly more stringent threshold (score > 63%), the precision could be significantly improved to 96.81% while keeping a recall rate > 95% (i.e., only 5% of the queried items would not be mapped). The machine learning approach did not lead to any improvements compared to the fuzzy matching. However, it could increase substantially the recall rate for food items without any clear equivalent in the FCTs (+7 and +20% when mapping items using their original or English-translated names). Our approaches have been implemented as R packages and are freely available from GitHub.</p><p>Conclusion: This study is the first to provide automated approaches for large-scale food item mapping onto FCTs. We demonstrate that both high precision and recall can be achieved. Our solutions can be used with any FCT and do not require any programming background. These methodologies and findings are useful to any small or large nutritional study (observational as well as interventional).</p

Convergent Functional Genomics of Oligodendrocyte Differentiation Identifies Multiple Autoinhibitory Signaling Circuits▿ †

Inadequate remyelination of brain white matter lesions has been associated with a failure of oligodendrocyte precursors to differentiate into mature, myelin-producing cells. In order to better understand which genes play a critical role in oligodendrocyte differentiation, we performed time-dependent, genome-wide gene expression studies of mouse Oli-neu cells as they differentiate into process-forming and myelin basic protein-producing cells, following treatment with three different agents. Our data indicate that different inducers activate distinct pathways that ultimately converge into the completely differentiated state, where regulated gene sets overlap maximally. In order to also gain insight into the functional role of genes that are regulated in this process, we silenced 88 of these genes using small interfering RNA and identified multiple repressors of spontaneous differentiation of Oli-neu, most of which were confirmed in rat primary oligodendrocyte precursors cells. Among these repressors were CNP, a well-known myelin constituent, and three phosphatases, each known to negatively control mitogen-activated protein kinase cascades. We show that a novel inhibitor for one of the identified genes, dual-specificity phosphatase DUSP10/MKP5, was also capable of inducing oligodendrocyte differentiation in primary oligodendrocyte precursors. Oligodendrocytic differentiation feedback loops may therefore yield pharmacological targets to treat disease related to dysfunctional myelin deposition