Evolutionary Approach for Relative Gene Expression Algorithms

A Relative Expression Analysis (RXA) uses ordering relationships in a small collection of genes and is successfully applied to classiffication using microarray data. As checking all possible subsets of genes is computationally infeasible, the RXA algorithms require feature selection and multiple restrictive assumptions. Our main contribution is a specialized evolutionary algorithm (EA) for top-scoring pairs called EvoTSP which allows finding more advanced gene relations. We managed to unify the major variants of relative expression algorithms through EA and introduce weights to the top-scoring pairs. Experimental validation of EvoTSP on public available microarray datasets showed that the proposed solution significantly outperforms in terms of accuracy other relative expression algorithms and allows exploring much larger solution space

Classification of hepatic metastasis in enhanced CT images by dipolar decision tree

- Cette étude a pour but de réaliser une classification des métastases hépatiques, en imagerie scanner. Les régions d'intérêt analysées représentent du tissu sain, et quatre types de métastases. Pour chaque patient, trois acquisitions sont réalisées (sans injection de produit de contraste, aux phases artérielle et portale après injection). La méthode comporte une première étape de caractérisation par analyse de texture, suivie d'une classification des régions. La méthode de classification utilisée est basée sur les arbres de décision dipolaires. Dans cette méthode, chaque noeud de l'arbre correspond à un test multivariable (hyperplan). La recherche de l'hyperplan optimal est basée sur la séparation des dipôles (paire de vecteurs de paramètres de l'ensemble d'apprentissage). Les résultats préliminaires montrent que la qualité de classification augmente quand le temps d'acquisition des images est pris en compte, et qu'elle est supérieure à celle obtenue par d'autres méthodes de classification

Couplage d'un modèle vasculaire bi-niveau et d'un modèle d'acquisition d'images : application à la simulation d'IRM dynamique du Carcinome Hépatocellulaire

La modélisation physiologique permet de mieux comprendre les images médicales et de mettre en évidence, dans l'image, des marqueurs de la pathologie. Dans cet article, nous proposons de coupler un modèle de la vascularisation hépatique à un modèle d'acquisition d'Images de - Résonance Magnétique (IRM), et d'appliquer ces modèles à la simulation d'IRM dynamique du Carcinome Hépatocellulaire (CHC). Le modèle vasculaire intègre les propriétés anatomiques et fonctionnelles clos vaisseaux, modifiées au cours du développement tumoral (densité vasculaire, débits, perméabilité, etc). Il permet de simuler la propagation de différents produits de contraste, ou tenant compte de leurs principales propriétés physiques et magnétiques, aux niveaux macro- et micro-vasculaire. Les images simulées à clos temps d'acquisition différents (phase artérielle, phase portale) présentent clos contrastes proches de ceux observés sur clos images réelles

Multi-test Decision Tree and its Application to Microarray Data Classification

Objective: The desirable property of tools used to investigate biological data is easy to understand models and predictive decisions. Decision trees are particularly promising in this regard due to their comprehensible nature that resembles the hierarchical process of human decision making. However, existing algorithms for learning decision trees have tendency to underfit gene expression data. The main aim of this work is to improve the performance and stability of decision trees with only a small increase in their complexity. Methods: We propose a multi-test decision tree (MTDT); our main contribution is the application of several univariate tests in each non-terminal node of the decision tree. We also search for alternative, lower-ranked features in order to obtain more stable and reliable predictions. Results: Experimental validation was performed on several real-life gene expression datasets. Comparison results with eight classifiers show that MTDT has a statistically significantly higher accuracy than popular decision tree classifiers, and it was highly competitive with ensemble learning algorithms. The proposed solution managed to outperform its baseline algorithm on $14$ datasets by an average $6$ percent. A study performed on one of the datasets showed that the discovered genes used in the MTDT classification model are supported by biological evidence in the literature. Conclusion: This paper introduces a new type of decision tree which is more suitable for solving biological problems. MTDTs are relatively easy to analyze and much more powerful in modeling high dimensional microarray data than their popular counterparts

Physiological modeling of tumor-affected renal circulation.

International audienceOne way of gaining insight into what can be observed in medical images is through physiological modeling. For instance, anatomical and functional modifications occur in the organ during the appearance and the growth of a tumor. Some of these changes concern the vascularization. We propose a computational model of tumor-affected renal circulation that represents the local heterogeneity of different parts of the kidney (cortex, medulla). We present a simulation of vascular modifications related to vessel structure, geometry, density, and blood flow in case of renal cell carcinoma. We also use our model to simulate computed tomography scans of a kidney affected by the renal cell carcinoma, at two acquisition times after injection of a contrast product. This framework, based on a physiological model of the organ and physical model of medical image acquisition, offers an opportunity to help radiologists in their diagnostic tasks. This includes the possibility of linking image descriptors with physiological perturbations and markers of pathological processes

Texture-based characterization of arterialization in simulated MRI of hypervascularized liver tumors.

International audienceThe use of quantitative imaging for the characterization of hepatic tumors in MRI can improve the diagnosis and therefore the treatment. However, image parameters remain difficult to interpret because they result from a mixture of complex processes related to pathophysiology and to acquisition. In particular, the lesion arterialization is prominent in the resulting contrast between normal and tumoral tissues in contrast-enhanced images. In order to identify this influence, we propose a multiscale model of liver dynamic contrast-enhanced MRI, consisting of a model of the organ coupled with a model of the image acquisition. A sensitivity analysis of the model to the arterial flow has enabled us to emphasize the existence of relationships between texture parameters in simulated arterial-phase MR images, and the arterialization phenomena involved in carcinogenesis