Stability of feature selection algorithms: a study on high-dimensional spaces

With the proliferation of extremely high-dimensional data, feature selection algorithms have become indispensable components of the learning process. Strangely, despite extensive work on the stability of learning algorithms, the stability of feature selection algorithms has been relatively neglected. This study is an attempt to fill that gap by quantifying the sensitivity of feature selection algorithms to variations in the training set. We assess the stability of feature selection algorithms based on the stability of the feature preferences that they express in the form of weights-scores, ranks, or a selected feature subset. We examine a number of measures to quantify the stability of feature preferences and propose an empirical way to estimate them. We perform a series of experiments with several feature selection algorithms on a set of proteomics datasets. The experiments allow us to explore the merits of each stability measure and create stability profiles of the feature selection algorithms. Finally, we show how stability profiles can support the choice of a feature selection algorith

Study of cereals flows at local scales: Examples in the Rhône-Alpes région, the Isère département and the SCOT de Grenoble

International audienceThe purpose of this article is to put forward the role applied mathematics and computer science could play in the field of ecological accounting and particularly in that of material flow analysis. It is done based on a detailed study on modeling cereal flows at sub-national scales

Bimodal modulation of L1 interneuron activity in anterior cingulate cortex during fear conditioning

The anterior cingulate cortex (ACC) plays a crucial role in encoding, consolidating and retrieving memories related to emotionally salient experiences, such as aversive and rewarding events. Various studies have highlighted its importance for fear memory processing, but its circuit mechanisms are still poorly understood. Cortical layer 1 (L1) of the ACC might be a particularly important site of signal integration, since it is a major entry point for long-range inputs, which is tightly controlled by local inhibition. Many L1 interneurons express the ionotropic serotonin receptor 3a (5HT3aR), which has been implicated in post-traumatic stress disorder and in models of anxiety. Hence, unraveling the response dynamics of L1 interneurons and subtypes thereof during fear memory processing may provide important insights into the microcircuit organization regulating this process. Here, using 2-photon laser scanning microscopy of genetically encoded calcium indicators through microprisms in awake mice, we longitudinally monitored over days the activity of L1 interneurons in the ACC in a tone-cued fear conditioning paradigm. We observed that tones elicited responses in a substantial fraction of the imaged neurons, which were significantly modulated in a bidirectional manner after the tone was associated to an aversive stimulus. A subpopulation of these neurons, the neurogliaform cells (NGCs), displayed a net increase in tone-evoked responses following fear conditioning. Together, these results suggest that different subpopulations of L1 interneurons may exert distinct functions in the ACC circuitry regulating fear learning and memory

Implication of sperm RNAs in transgenerational inheritance of the effects of early trauma in mice.

Small non-coding RNAs (sncRNAs) are potential vectors at the interface between genes and environment. We found that traumatic stress in early life altered mouse microRNA (miRNA) expression, and behavioral and metabolic responses in the progeny. Injection of sperm RNAs from traumatized males into fertilized wild-type oocytes reproduced the behavioral and metabolic alterations in the resulting offspring.We thank M. Rassoulzadegan and V. Grandjean for help with the sperm purification, F. Manuella and H. Hörster for assistance with the MSUS paradigm, H. Welzl for help with behavior, G. Vernaz for help with western blotting, R. Tweedie-Cullen and P. Nanni for help with mass spectrometry, A. Patrignani for advice on DNA and RNA quality assessment, and A. Chen and A. Brunner for constructive discussions. This work was supported by the Austrian Academy of Sciences, the University of Zürich, the Swiss Federal Institute of Technology, Roche, the Swiss National Science Foundation, and The National Center of Competence in Research “Neural Plasticity and Repair”. P.S. was supported by a Gonville and Caius College fellowship.This is the accepted manuscript. The final version is available in Nature Neuroscience 17, 667–669 (2014), doi:10.1038/nn.369

Méthode pour le pré-traitement et l'extraction des biomarqueurs en spéctrométrie de masse

La spectrométrie de masse offre des outils pour analyser la composition moléculaire d'un échantillons. En protéomique clinique, les technologies SELDI-TOF et MALDI-TOF ont récemment reçus une attention particulière pour leur facultés à analyser des échantillons biologiques de patients (serum sanguin, urine, tissus,...). Malgré leur résolution faible, ces technologies offrent en effet la possibilité de dresser rapidement le profil d'expression de centaines de protéines pour des centaines d'échantillons, ce qui permet d'envisager leur utilisation pour le diagnostique de patients et la recherche de biomarqueurs de certaines maladies. Ces applications soulèvent néanmoins des interrogations quant à la fiabilité des résultats que l'on peut tirer de ces technologies. Cette thèse aborde différents aspects pour améliorer la fiabilité des résultats. Nous étudions principalement le problème de l'analyse bio-informatique des données mais aussi l'amélioration des protocoles de préparation des échantillons biologiques. Concernant l'analyse bio-informatique, les difficultés majeures résident dans la structuration des données, et dans leur forte dimension qui perturbent de nombreux algorithmes d'extraction de connaissances

bmrm user manual

Package bmrm implements the ”Bundle Methods for Regularized Risk Minimization” proposed by Teo et al. (2010). This framework efficiently solves a minimization problem encountred in many recent machine learning algorithm where the goal is to minimze a loss function l(w, xi, yi) on the training instance

Stability of feature selection algorithms: a study on high-dimensional spaces

With the proliferation of extremely high-dimensional data, feature selection algorithms have become indispensable components of the learning process. Strangely, despite extensive work on the stability of learning algorithms, the stability of feature selection algorithms has been relatively neglected. This study is an attempt to fill that gap by quantifying the sensitivity of feature selection algorithms to variations in the training set. We assess the stability of feature selection algorithms based on the stability of the feature preferences that they express in the form of weights-scores, ranks, or a selected feature subset. We examine a number of measures to quantify the stability of feature preferences and propose an empirical way to estimate them. We perform a series of experiments with several feature selection algorithms on a set of proteomics datasets. The experiments allow us to explore the merits of each stability measure and create stability profiles of the feature selection algorithms. Finally, we show how stability profiles can support the choice of a feature selection algorithm