Jean-Daniel Zucker

FunNet: an integrative tool for exploring transcriptional interactions

UNLABELLEDnWe describe here an exploratory tool, called FunNet, which implements an original systems biology approach, aiming to improve the biological relevance of the modular interaction patterns identified in transcriptional co-expression networks. A suitable analytical model, involving two abstraction layers, has been devised to relate expression profiles to the knowledge on transcripts biological roles, extracted from genomic databases, into a comprehensive exploratory framework. This approach has been implemented into a user-friendly web tool to promote its open use by the community.nnnAVAILABILITYnhttp://www.funnet.info

A paradigm of diagnostic criteria for polyarteritis nodosa: Analysis of a series of 949 patients with vasculitides

OBJECTIVEnTo establish a set of clinical and paraclinical criteria potentially useful as a diagnostic screening tool for polyarteritis nodosa (PAN).nnnMETHODSnThe abilities of individual descriptive items to predict a diagnosis of PAN were evaluated by screening available data from 949 patients from the French Vasculitis Study Group database, including 262 with PAN and 687 with control vasculitides. Selected items were tested in a logistic regression model to establish a minimal set of nonredundant PAN-predictive criteria. The discriminative accuracy of these items and of the American College of Rheumatology (ACR) 1990 criteria were assessed by reapplying them to the initial patient sample and a subgroup restricted to PAN and microscopic polyangiitis (MPA) patients. A computer simulation procedure was conducted on artificially generated patient data to evaluate the usefulness of these criteria in predicting a diagnosis of PAN.nnnRESULTSnThe analysis resulted in the retention of 3 positive predictive parameters (hepatitis B virus antigen and/or DNA in serum, arteriographic anomalies, and mononeuropathy or polyneuropathy) and 5 negative predictive parameters (indirect immunofluorescence detection of antineutrophil cytoplasmic antibody; asthma; ear, nose, and throat signs; glomerulopathy; and cryoglobulinemia) for the criteria set. These criteria yielded 70.6% sensitivity for all control vasculitides and 89.7% for MPA controls, with 92.3% specificity for all controls and 83.1% for MPA controls. The discriminant abilities of this set of items outperformed the ACR 1990 criteria in all analytical situations, showing better robustness to variations in the prevalence of individual vasculitides.nnnCONCLUSIONnThe use of positive and negative discriminant criteria could constitute a sound basis for developing a diagnostic tool for PAN to be used by clinicians. Further prospective analyses and validations in different populations are needed to confirm these items as satisfactory diagnostic criteria.

Adipose Gene Expression Prior to Weight Loss Can Differentiate and Weakly Predict Dietary Responders

Background The ability to identify obese individuals who will successfully lose weight in response to dietary intervention will revolutionize disease management. Therefore, we asked whether it is possible to identify subjects who will lose weight during dietary intervention using only a single gene expression snapshot. Methodology/Principal Findings The present study involved 54 female subjects from the Nutrient-Gene Interactions in Human Obesity-Implications for Dietary Guidelines (NUGENOB) trial to determine whether subcutaneous adipose tissue gene expression could be used to predict weight loss prior to the 10-week consumption of a low-fat hypocaloric diet. Using several statistical tests revealed that the gene expression profiles of responders (8–12 kgs weight loss) could always be differentiated from non-responders (<4 kgs weight loss). We also assessed whether this differentiation was sufficient for prediction. Using a bottom-up (i.e. black-box) approach, standard class prediction algorithms were able to predict dietary responders with up to 61.1%±8.1% accuracy. Using a top-down approach (i.e. using differentially expressed genes to build a classifier) improved prediction accuracy to 80.9%±2.2%. Conclusion Adipose gene expression profiling prior to the consumption of a low-fat diet is able to differentiate responders from non-responders as well as serve as a weak predictor of subjects destined to lose weight. While the degree of prediction accuracy currently achieved with a gene expression snapshot is perhaps insufficient for clinical use, this work reveals that the comprehensive molecular signature of adipose tissue paves the way for the future of personalized nutrition.

A distinct adipose tissue gene expression response to caloric restriction predicts 6-mo weight maintenance in obese subjects

BACKGROUNDnWeight loss has been shown to reduce risk factors associated with cardiovascular disease and diabetes; however, successful maintenance of weight loss continues to pose a challenge.nnnOBJECTIVEnThe present study was designed to assess whether changes in subcutaneous adipose tissue (scAT) gene expression during a low-calorie diet (LCD) could be used to differentiate and predict subjects who experience successful short-term weight maintenance from subjects who experience weight regain.nnnDESIGNnForty white women followed a dietary protocol consisting of an 8-wk LCD phase followed by a 6-mo weight-maintenance phase. Participants were classified as weight maintainers (WMs; 0-10% weight regain) and weight regainers (WRs; 50-100% weight regain) by considering changes in body weight during the 2 phases. Anthropometric measurements, bioclinical variables, and scAT gene expression were studied in all individuals before and after the LCD. Energy intake was estimated by using 3-d dietary records.nnnRESULTSnNo differences in body weight and fasting insulin were observed between WMs and WRs at baseline or after the LCD period. The LCD resulted in significant decreases in body weight and in several plasma variables in both groups. WMs experienced a significant reduction in insulin secretion in response to an oral-glucose-tolerance test after the LCD; in contrast, no changes in insulin secretion were observed in WRs after the LCD. An ANOVA of scAT gene expression showed that genes regulating fatty acid metabolism, citric acid cycle, oxidative phosphorylation, and apoptosis were regulated differently by the LCD in WM and WR subjects.nnnCONCLUSIONnThis study suggests that LCD-induced changes in insulin secretion and scAT gene expression may have the potential to predict successful short-term weight maintenance. This trial was registered at clinicaltrials.gov as NCT00390637.

Interactional and functional centrality in transcriptional co-expression networks

MOTIVATIONnThe noisy nature of transcriptomic data hinders the biological relevance of conventional network centrality measures, often used to select gene candidates in co-expression networks. Therefore, new tools and methods are required to improve the prediction of mechanistically important transcriptional targets.nnnRESULTSnWe propose an original network centrality measure, called annotation transcriptional centrality (ATC) computed by integrating gene expression profiles from microarray experiments with biological knowledge extracted from public genomic databases. ATC computation algorithm delimits representative functional domains in the co-expression network and then relies on this information to find key nodes that modulate propagation of functional influences within the network. We demonstrate ATC ability to predict important genes in several experimental models and provide improved biological relevance over conventional topological network centrality measures.nnnAVAILABILITYnATC computational routine is implemented in a publicly available tool named FunNet (www.funnet.info).

Feature construction from synergic pairs to improve microarray-based classification

MOTIVATIONnMicroarray experiments that allow simultaneous expression profiling of thousands of genes in various conditions (tissues, cells or time) generate data whose analysis raises difficult problems. In particular, there is a vast disproportion between the number of attributes (tens of thousands) and the number of examples (several tens). Dimension reduction is therefore a key step before applying classification approaches. Many methods have been proposed to this purpose, but only a few of them considered a direct quantification of transcriptional interactions. We describe and experimentally validate a new dimension reduction and feature construction method, which assesses interactions between expression profiles to improve microarray-based classification accuracy.nnnRESULTSnOur approach relies on a mutual information measure that exposes some elementary constituents of the information contained in a pair of gene expression profiles. We show that their analysis implies a term that represents the information of the interaction between the two genes. The principle of our method, called FeatKNN, is to exploit the information provided by highly synergic gene pairs to improve classification accuracy. First, a heuristic search selects the most informative gene pairs. Then, for each selected pair, a new feature, representing the classification margin of a KNN classifier in the gene pairs space, is constructed. We show experimentally that the interactional information has a degree of significance comparable to that of the gene expression profiles considered separately. Our method has been tested with different classifiers and yielded significant improvements in accuracy on several public microarray databases. Moreover, a synthetic assessment of the biological significance of the concept of synergic gene pairs suggested its ability to uncover relevant mechanisms underlying interactions among various cellular processes.

Unsupervised multiple-instance learning for functional profiling of genomic data

Multiple-instance learning (MIL) is a popular concept among the AI community to support supervised learning applications in situations where only incomplete knowledge is available. We propose an original reformulation of the MIL concept for the unsupervised context (UMIL), which can serve as a broader framework for clustering data objects adequately described by the multiple-instance representation. Three algorithmic solutions are suggested by derivation from available conventional methods: agglomerative or partition clustering and MILs citation-kNN approach. Based on standard clustering quality measures, we evaluated these algorithms within a bioinformatic framework to perform a functional profiling of two genomic data sets, after relating expression data to biological annotations into an UMIL representation. Our analysis spotlighted meaningful interaction patterns relating biological processes and regulatory pathways into coherent functional modules, uncovering profound features of the biological model. These results indicate UMILs usefulness in exploring hidden behavioral patterns from complex data.

Online analysis and visualization of agent based models

Agent-based modeling is used to study many kind of complex systems in different fields such as biology, ecology, or sociology. Visualization of the execution of a such complex systems is crucial in the capacity to apprehend its dynamics. The ever increasing complexification of requirements asked by the modeller has highlighted the need for more powerful tools than the existing ones to represent, visualize and interact with a simulation and extract data online to discover imperceptible dynamics at different spatio-temporal scales. In this article we present our research in advanced visualization and online data analysis developed in GAMA an agent-based, spatially explicit, modeling and simulation platform.

From distributed robot perception to human topology : a learning model

This poster presents an approach to enable autonomous mobile robots to link perceived information from their environment to names of places (toponyms acquired through interaction with human beings).

A Wrapper-Based Approach to Robot Learning Concepts from Images

This work is about the building of a lexicon of shared symbols between a Pioneer2DX mobile robot and its human interlocutors. This lexicon contains words corresponding to objects seen in the environment. The difficulty relies in grounding these symbols with the actual data provided by the camera of the robot with respect to the learning scenario shown in figure