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MicroRNAs are parts of nuclear and mitochondrial genomes which pertain to the not coding genome. They have a partly unspecific role of inhibition, preventing the weakest part of the genetic regulatory networks to be expressed and preventing the appearance of a too large number of attractors in these networks, i.e., forcing the network to have only one or two possible dynamic behaviours for fulfilling...
In this paper, a methodology of failure diagnosis in automotive systems based on log file similarity is presented. It supposes that we have a set of classes representing different failures of a vehicle, each class is composed of similar log files. Then, we define a similarity metric based on Kolmogorov complexity that measures the amount of shared information between the log file of a broken vehicle...
This paper presents a method to model biological systems decision. This method allows to reason about incomplete, revisable and uncertain information: an operator has partial information about his environment only and must revise his decisions. Our method uses a nonmonotonic logic: the rules of behavior are formalized with default logic, to which we added a consideration of time. Our method uses preferences...
Hypertension is a multifactorial and multi-gene abnormality that affects 25 percent of the world's population. Thus, in the last decades vast research has been conducted in order to determine the mechanisms of that disorder. This lead to the introduction of pathways that describe those mechanisms. Using that knowledge, scientists were able to design drugs that act directly on the pathway in order...
The aim of this work is to develop a methodology of failure diagnosis in automotive systems based on log file similarity. We propose a similarity metric based on Kolmogorov complexity to measure the amount of shared information between log files.
In automotive industry the safety of cars behavior is monitoring using computers. The information acquired on the bus communication is often redundant and not relevant. Therefore in the case of faults detection and isolation based on machine learning model, we need to reduce the number of variables according with their relevance and allowing taking decision in real time. In this paper, we propose...
This Fault detection is essential for the survivability of many systems. Since many systems present highly nonlinear dynamics, the applicability of general fault detection techniques designed mainly for linear systems is very questionable. The purpose of this communication is to investigate the usefulness of the differential flatness theory of a non linear system such as a four rotor aircraft to design...
Discretization is a key preprocessing step in knowledge discovery to make raw time series data applicable to symbolic data mining algorithms. To improve the comprehensibility of the mined results, or to help the induction step of the mining algorithms, in discretization, it is natural to prefer having discrete levels which can be mapped into intuitive symbols. In this paper, we aim to make smoothing...
Studying complex systems including biological systems is a multi-disciplinary research area. It must be derived by the recent explosion of ICT including high-performance computing, high-throughput experiments, the Internet, knowledge discovery and Artificial Intelligence (AI). The goal of this research is to establish a computational architecture and tools to deal with complex systems based on such...
The paper deals with supervision of biotechnological processes. Two main tasks are considered in this supervision scheme. The first one deals with Fault Detection and Isolation (FDI). The objective is to detect changes in the process dynamics using a model-based method. The second task concerns process physiological state recognition. A behavioral model is built using the expert knowledge along with...
In this paper, we present a new approach for selection of relevant parameters in fault detection process. The conflict notion is used in order to evaluate the relevance of any subset of parameters. This selection is divided in two steps. The first consists in making partitioning of data running smooth. During the second step, we calculate the conflict between each subset of parameters. The experimental...
We show in this paper that the metabolic chain can be supposed a potential-Hamiltonian system in which the dynamical flow can be shared between gradient dissipative and periodic conservative parts. If the chain is branched and if we know the fluxes at the extremities of each branch we can deduce information about the internal kinetics (e.g. place of allosteric and Michaelian step with respect to those...
The requires an integrative biological systems analysis as the quantitative description at the hierarchical level of molecular, cellular and phenotypic functions including their interaction with the environment is very complex. The growing awareness of the complex interplay between the genome and physiological functions of the cell needs a new holistic and full integrative view. In this paper we present...
This research aims to provide a tool to doctors in order to help for diagnosis of BRCA1 hereditary breast cancer. Our goal is to determine, if possible, profiles that are responsible for early cancer onset. In order to extract knowledge from the biological information above we will create a relational database that will allow prognosticating cancer apparition. We want to determine different types...
A common goal of biotechnological research and of commercial production is the definition of optimum conditions for achieving predetermined objectives. This goal is usually translated into the problem of finding the optimum control strategy that will produce the desired end-product. Today's fermentation controllers rely mostly on environmental state variables which only provide information on the...
The application of non negative matrix factorization to time series of medical images analyze is investigated in this paper. Time series images of the urinary system are acquired by intravenous pyelography (IVP). Factorial analysis and principal component analysis are often used to extract time signatures or factors and associated compartments of factor images. Blind source separation methods such...
The clustering capabilities of the Non Negative Matrix Factorization algorithm is studied. The basis images are considered like the membership degree of the data to a particular class. A hard clustering algorithm is easily derived based on these images. This algorithm is applied on a multivariate image to perform image segmentation. The results are compared with those obtained by Fuzzy K-means algorithm...
The paper deals with fault-isolation time problem of the adaptive observers based scheme. The Fault Detection and Isolation (FDI) method is applied to an alcoholic fermentation process using an Estimator based on the approach of the Model Reference (EMR). We propose in this work a new structure of the estimator gains in order to improve the convergence speed of the EMR observer and by the way the...
Dynamic models most of the time involve differential equations, which are "time-local". Such models can also be considered "globally", that is in the sense of "trajectories" in the "space-time" state. Up to adapted concepts, such a different interpretation reveals itself more flexible, namely because it allows to use various operatorial transformations whose...
This paper provides a method for estimating states of enzyme reactions in metabolic pathways. We first introduce a new model based on the logical viewpoint of enzyme function. The proposed model logically represents causal relations between concentration changes of metabolites and enzyme activities. When we observe the concentration changes of metabolites, we can assume which enzyme reactions are...
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