Alexandre Aussem

A novel Markov boundary based feature subset selection algorithm

We aim to identify the minimal subset of random variables that is relevant for probabilistic classification in data sets with many variables but few instances. A principled solution to this problem is to determine the Markov boundary of the class variable. In this paper, we propose a novel constraint-based Markov boundary discovery algorithm called MBOR with the objective of improving accuracy while still remaining scalable to very high dimensional data sets and theoretically correct under the so-called faithfulness condition. We report extensive empirical experiments on synthetic data sets scaling up to tens of thousand variables.

A novel scalable and data efficient feature subset selection algorithm

In this paper, we aim to identify the minimal subset of discrete random variables that is relevant for probabilistic classification in data sets with many variables but few instances. A principled solution to this problem is to determine the Markov boundaryof the class variable. Also, we present a novel scalable, data efficient and correct Markov boundary learning algorithm under the so-called faithfulnesscondition. We report extensive empiric experiments on synthetic and real data sets scaling up to 139,351 variables.

Neural-network metamodelling for the prediction of Caulerpa taxifolia development in the Mediterranean sea

Abstract This paper addresses the use of neural networks as a metamodelling technique for discrete event stochastic simulation to reduce significantly the computational burden involved by the simulations. A sophisticated computer model has been developed to anticipate the propagation of the green alga Caulerpa taxifolia in the northwestern Mediterranean sea. The simulation model provides reliable predictions, a couple of years in advance, of the covered surfaces. To reduce the heavy computational burden involved by the simulation, a neural network was successfully trained on artificially generated data provided by the simulation runs to provide accurate forecasts 12 years in advance, along with associated confidence intervals. The neural-network metamodel is competitive in accuracy when compared to the simulation itself and, once trained, can operate in nearly real time.

Wedding connectionist and algorithmic modelling towards forecasting Caulerpa taxifolia development in the north-western Mediterranean sea

We discuss the use of supervised neural networks as a metamodelling technique for discrete event stochastic simulation in order to reduce significantly the computational burden involved by discrete simulations. A sophisticated computer model, coupling a geographical information system with a stochastic discrete event simulator, has been developed to anticipate the propagation of the green alga Caulerpa taxifolia in the north-western Mediterranean sea. The simulation model provides reliable predictions, a couple of years in advance, of: (i) the local expansion patterns of the alga; (ii) the increase of C. taxifolia biomass and (iii) the covered surfaces. However because the algorithmic model accounts for spatial interactions and anthropic dispersion/activities such as eradication, introduction of specific predators etc., simulations are extremely time and memory consuming. Therefore, to reduce the computational burden, a neural network was successfully trained on artificially generated data provided by the simulation runs to provide accurate forecasts 12 years in advance along with associated confidence intervals. The ability of the neural networks to capture the underlying physics of the phenomena is clearly illustrated by several preliminary experiments on a large coastal area. The neural network is able to construct, on this site, estimates of the Caulerpa taxifolia expansion 12 years in advance in good agreement with the simulation trajectories.

Analysis of nasopharyngeal carcinoma risk factors with Bayesian networks

OBJECTIVES We propose a new graphical framework for extracting the relevant dietary, social and environmental risk factors that are associated with an increased risk of nasopharyngeal carcinoma (NPC) on a case-control epidemiologic study that consists of 1289 subjects and 150 risk factors. METHODS This framework builds on the use of Bayesian networks (BNs) for representing statistical dependencies between the random variables. We discuss a novel constraint-based procedure, called Hybrid Parents and Children (HPC), that builds recursively a local graph that includes all the relevant features statistically associated to the NPC, without having to find the whole BN first. The local graph is afterwards directed by the domain expert according to his knowledge. It provides a statistical profile of the recruited population, and meanwhile helps identify the risk factors associated to NPC. RESULTS Extensive experiments on synthetic data sampled from known BNs show that the HPC outperforms state-of-the-art algorithms that appeared in the recent literature. From a biological perspective, the present study confirms that chemical products, pesticides and domestic fume intake from incomplete combustion of coal and wood are significantly associated with NPC risk. These results suggest that industrial workers are often exposed to noxious chemicals and poisonous substances that are used in the course of manufacturing. This study also supports previous findings that the consumption of a number of preserved food items, like house made proteins and sheep fat, are a major risk factor for NPC. CONCLUSION BNs are valuable data mining tools for the analysis of epidemiologic data. They can explicitly combine both expert knowledge from the field and information inferred from the data. These techniques therefore merit consideration as valuable alternatives to traditional multivariate regression techniques in epidemiologic studies.

A conservative feature subset selection algorithm with missing data

This paper introduces a novel conservative feature subset selection method with incomplete data sets. The method is conservative in the sense that it selects the minimal subset of features that renders the rest of the features independent of the target (the class variable) without making any assumption about the missing data mechanism. This is achieved in the context of determining the Markov blanket of the target that reflects the worst-case assumption about the missing data mechanism, including the case when data are not missing at random. An application of the method on synthetic and real-world incomplete data is carried out to illustrate its practical relevance. The method is compared against state-of-the-art approaches such as the expectation-maximization (EM) algorithm and the available case technique.

Dynamical recurrent neural networks and pattern recognition methods for time series prediction: Application to seeing and temperature forecasting in the context of ESO's VLT astronomical weather station

Abstract The European Southern Observatorys planned Astronomical Weather Station for the Very Large Telescope which is currently under construction at Cerro Paranal in Chile includes (i) advance temperature prediction, which would permit air conditioning in the telescope enclosure to be preset as a function of the next nights expected temperature; and (ii) prediction of seeing, a few hours in advance, to allow flexible scheduling of the most appropriate instrumentation. Extensive data, collected since 1985, are being used to appraise various methodologies. A recurrent neural network is described, which uses arbitrary time-delayed connections to capture the dynamic of time series. This endows the model with a memory of its previous states. The resulting network is time- and space-recurrent, and generalizes most recurrent architectures. The performance of this network is discussed. The results are compared with the k-nearest neighbors method.

A Genome-Wide Association Study Identifies a Novel Locus for Bortezomib-Induced Peripheral Neuropathy in European Patients with Multiple Myeloma

Purpose: Painful peripheral neuropathy is a frequent toxicity associated with bortezomib therapy. This study aimed to identify loci that affect susceptibility to this toxicity. Experimental Design: A genome-wide association study (GWAS) of 370,605 SNPs was performed to identify risk variants for developing severe bortezomib-induced peripheral neuropathy (BiPN) in 469 patients with multiple myeloma who received bortezomib–dexamethasone therapy prior to autologous stem cell in randomized clinical trials of the Intergroupe Francophone du Myelome (IFM) and findings were replicated in 114 patients with multiple myeloma of the HOVON-65/GMMG-HD4 clinical trial. Results: An SNP in the PKNOX1 gene was associated with BiPN in the exploratory cohort [rs2839629; OR, 1.89, 95% confidence interval (CI), 1.45–2.44; P = 7.6 × 10−6] and in the replication cohort (OR, 2.04; 95% CI, = 1.11–3.33; P = 8.3 × 10−3). In addition, rs2839629 is in strong linkage disequilibrium (r2 = 0.87) with rs915854, located in the intergenic region between PKNOX1 and cystathionine-ß-synthetase (CBS). Expression quantitative trait loci mapping showed that both rs2839629 and rs915854 genotypes have an impact on PKNOX1 expression in nerve tissue, whereas rs2839629 affects CBS expression in skin and blood. Conclusions: The use of GWAS in multiple myeloma pharmacogenomics has identified a novel candidate genetic locus mapping to PKNOX1 and in the immediate vicinity of CBS at 21q22.3 associated with the severe bortezomib-induced toxicity. The proximity of these two genes involved in neurologic pain whose tissue-specific expression is modified by the two variants provides new targets for neuroprotective strategies. Clin Cancer Res; 22(17); 4350–5. ©2016 AACR.

Hip Fracture in the Elderly: A Re-Analysis of the EPIDOS Study with Causal Bayesian Networks

Objectives Hip fractures commonly result in permanent disability, institutionalization or death in elderly. Existing hip-fracture predicting tools are underused in clinical practice, partly due to their lack of intuitive interpretation. By use of a graphical layer, Bayesian network models could increase the attractiveness of fracture prediction tools. Our aim was to study the potential contribution of a causal Bayesian network in this clinical setting. A logistic regression was performed as a standard control approach to check the robustness of the causal Bayesian network approach. Setting EPIDOS is a multicenter study, conducted in an ambulatory care setting in five French cities between 1992 and 1996 and updated in 2010. The study included 7598 women aged 75 years or older, in which fractures were assessed quarterly during 4 years. A causal Bayesian network and a logistic regression were performed on EPIDOS data to describe major variables involved in hip fractures occurrences. Results Both models had similar association estimations and predictive performances. They detected gait speed and mineral bone density as variables the most involved in the fracture process. The causal Bayesian network showed that gait speed and bone mineral density were directly connected to fracture and seem to mediate the influence of all the other variables included in our model. The logistic regression approach detected multiple interactions involving psychotropic drug use, age and bone mineral density. Conclusion Both approaches retrieved similar variables as predictors of hip fractures. However, Bayesian network highlighted the whole web of relation between the variables involved in the analysis, suggesting a possible mechanism leading to hip fracture. According to the latter results, intervention focusing concomitantly on gait speed and bone mineral density may be necessary for an optimal prevention of hip fracture occurrence in elderly people.

Exploiting Data Missingness in Bayesian Network Modeling

This paper proposes a framework built on the use of Bayesian networks (BN) for representing statistical dependencies between the existing random variables and additional dummy boolean variables, which represent the presence/absence of the respective random variable value. We show how augmenting the BN with these additional variables helps pinpoint the mechanism through which missing data contributes to the classification task. The missing data mechanism is thus explicitly taken into account to predict the class variable using the data at hand. Extensive experiments on synthetic and real-world incomplete data sets reveals that the missingness information improves classification accuracy.