René Natowicz

Adult rat vigilance states discrimination by artificial neural networks using a single EEG channel

Two multilayer neural networks were designed to discriminate vigilance states (waking, paradoxical sleep, and non-REM sleep) in the rat using a single parieto-occipital EEG derivation. After filtering (bandwidth 3.18-25 Hz) and digitization at 512 HZ, the EEG signal was segmented into eight second epochs. Five variables (three statistical, two temporal) were extracted from each epoch. The first network computed an epoch by epoch classification, while the second network also utilized contextual information from contiguous epochs. A specific postprocessing procedure was developed to enhance the vigilance state discrimination of the neural networks designed and especially paradoxical sleep state estimation. The classifications made by the networks (with or without the postprocessing procedure) for six rats were compared to these made by two human experts using EMG and EEG informations on 63,000 epochs. High rates of agreement (> 90%) between humans and neural networks classifications were obtained. In view of its development possibilities and its applicability to other signals, this method could prove of value in biomedical research.

Hyperstructures, genome analysis and I-Cells

New concepts may prove necessary to profit from the avalanche of sequence data on the genome, transcriptome, proteome and interactome and to relate this information to cell physiology. Here, we focus on the concept of large activity-based structures, or hyperstructures, in which a variety of types of molecules are brought together to perform a function. We review the evidence for the existence of hyperstructures responsible for the initiation of DNA replication, the sequestration of newly replicated origins of replication, cell division and for metabolism. The processes responsible for hyperstructure formation include changes in enzyme affinities due to metabolite-induction, lipid-protein affinities, elevated local concentrations of proteins and their binding sites on DNA and RNA, and transertion. Experimental techniques exist that can be used to study hyperstructures and we review some of the ones less familiar to biologists. Finally, we speculate on how a variety of in silico approaches involving cellular automata and multi-agent systems could be combined to develop new concepts in the form of an Integrated cell (I-cell) which would undergo selection for growth and survival in a world of artificial microbiology.

Energy Optimal Real-Time Navigation System

The rapid development of Mobile Internet and Smart Devices and advent of a new generation of Intelligent Transportation Systems (ITS) increase information about present driving conditions and make its prediction possible. Real time traffic information systems (TIS) like SYTADIN help in route to destination planning and traffic state prediction. Energy-optimal routing for electric vehicles creates novel algorithmic challenges where the computation complexity and the quality of information on traffic state are the main issues. This complexity is induced by the possible negative values of edge energy as well as the variability of route and vehicle variables which render the standard algorithms unsuitable. In this paper we present an Energy Optimal Real Time Navigation System (EORTNS), implemented on Samsung Galaxy Tab, capable of calculating the route to destination based on information flow obtained from SYTADIN. As an application example we propose a real time energy management for a Hybrid Electrical Vehicle (HEV) composed of batteries and Super-Capacitors (SC). The EORTNS is not only capable of energy optimal route to destination calculation with respect to traffic state but also operates the On-Board power splitting between batteries and Super-Capacitors.

Statistical measures of transcriptional diversity capture genomic heterogeneity of cancer

BackgroundMolecular heterogeneity of tumors suggests the presence of multiple different subclones that may limit response to targeted therapies and contribute to acquisition of drug resistance, but its quantification has remained challenging.ResultsWe performed simulations to evaluate statistical measures that best capture the molecular diversity within a group of tumors for either continuous (gene expression) or discrete (mutations, copy number alterations) molecular data. Dispersion based metrics in the principal component space best captured the underlying heterogeneity. To demonstrate utility of these measures, we characterized the diversity in transcriptional and genomic profiles of different breast tumor subtypes, and showed that basal-like or triple-negative breast cancers (TNBC) are significantly more heterogeneous molecularly than other subtypes. Our analysis also suggests that transcriptional diversity is a global characteristic of the tumors observed across the majority of molecular pathways. Among basal-like tumors, those that were resistant to multi-agent chemotherapy showed greater transcriptional diversity compared to chemotherapy-sensitive tumors, suggesting that potentially multiple mechanisms may be contributing to chemotherapy resistance.ConclusionsWe proposed and validated measures of transcriptional and genomic diversity that can quantify the molecular diversity of tumors. We applied the new measures to genomic data from breast tumors and demonstrated that basal-like breast cancers are significantly more diverse than other breast cancers. The observation that chemo-resistant tumors are significantly more diverse molecularly than chemosensitive tumors implies that multiple resistance mechanisms may be active, thus limiting the sensitivity and accuracy of predictive markers of chemotherapy response.

Prediction of the outcome of preoperative chemotherapy in breast cancer using DNA probes that provide information on both complete and incomplete responses

BackgroundDNA microarray technology has emerged as a major tool for exploring cancer biology and solving clinical issues. Predicting a patients response to chemotherapy is one such issue; successful prediction would make it possible to give patients the most appropriate chemotherapy regimen. Patient response can be classified as either a pathologic complete response (PCR) or residual disease (NoPCR), and these strongly correlate with patient outcome. Microarrays can be used as multigenic predictors of patient response, but probe selection remains problematic. In this study, each probe set was considered as an elementary predictor of the response and was ranked on its ability to predict a high number of PCR and NoPCR cases in a ratio similar to that seen in the learning set. We defined a valuation function that assigned high values to probe sets according to how different the expression of the genes was and to how closely the relative proportions of PCR and NoPCR predictions to the proportions observed in the learning set was. Multigenic predictors were designed by selecting probe sets highly ranked in their predictions and tested using several validation sets.ResultsOur method defined three types of probe sets: 71% were mono-informative probe sets (59% predicted only NoPCR, and 12% predicted only PCR), 25% were bi-informative, and 4% were non-informative. Using a valuation function to rank the probe sets allowed us to select those that correctly predicted the response of a high number of patient cases in the training set and that predicted a PCR/NoPCR ratio for validation sets that was similar to that of the whole learning set. Based on DLDA and the nearest centroid method, bi-informative probes proved more successful predictors than probes selected using a t test.ConclusionPrediction of the response to breast cancer preoperative chemotherapy was significantly improved by selecting DNA probe sets that were successful in predicting outcomes for the entire learning set, both in terms of accurately predicting a high number of cases and in correctly predicting the ratio of PCR to NoPCR cases.

Energy optimal real-time navigation system: Application to a hybrid electrical vehicle

In this paper we present an Optimal Real Time Navigation System (ORTNS) implemented on an Android Smart Device capable of calculating the route to destination based on a permanent Internet connection and information flow obtained from SYTADIN, a traffic Information System. A multi-objective criterion is defined in order to offer the drivers different route to destination parameterization. The ORTNS is not only capable of optimal route to destination calculation with respect to traffic state but also makes the vehicle On-Board energy optimization and/or gas emission reduction possible. As an application example we propose a real time energy management for a Hybrid Electrical Vehicle (HEV) composed of batteries and Super-Capacitors (SC). Based on calculated 3D route to destination and average speeds for each road segment the state of charge (SOC) for batteries and Super-Capacitors (SC) for each receding horizon are optimaly predicted and modified in real time.

Real time energy management algorithm for hybrid electric vehicle

The rapid development of mobile Internet and smart devices and advent of new generation of Intelligent Transportation Systems (ITS) increase information about future driving conditions and makes on-board energy management of Hybrid Electrical Vehicle (HEV) realistic. In this paper we propose a real time energy management algorithm RTEMA for a HEV composed of batteries and Super-Capacitors (SC). We suppose that the On-board Dedicated Computer (ODC) has a permanent Internet connection and has the possibility to perform Google-Maps application queries to calculate 3D route to destination. Based on 3D route to destination and average speeds for each road segment the state of charge (SOC) for battery and SC at the end of each receding horizon are predicted. A cost function considering the efficiency of battery, SC, DC/DC converter and electric motor as well as the battery life cycles increase is presented and an optimization problem is formulated and resolved. As ODC for real-time communication and computation we use Samsung Galaxy Pad which is an Android smart device. Performance results in terms of energy optimization, increased battery life and computing time are given which show the practical usefulness of this approach and application. Such a system can be made available to potential users of HEV as well as to the Android community driver members who share information on the state of traffic flow via their Android mobile phones [30].

Direct comparison of logistic regression and recursive partitioning to predict chemotherapy response of breast cancer based on clinical pathological variables

The purpose was to compare logistic regression model (LRM) and recursive partitioning (RP) to predict pathologic complete response to preoperative chemotherapy in patients with breast cancer. The two models were built in a same training set of 496 patients and validated in a same validation set of 337 patients. Model performance was quantified with respect to discrimination (evaluated by the areas under the receiver operating characteristics curves (AUC)) and calibration. In the training set, AUC were similar for LRM and RP models (0.77 (95% confidence interval, 0.74–0.80) and 0.75 (95% CI, 0.74–0.79), respectively) while LRM outperformed RP in the validation set (0.78 (95% CI, 0.74–0.82) versus 0.64 (95% CI, 0.60–0.67). LRM model also outperformed RP model in term of calibration. In these real datasets, LRM model outperformed RP model. It is therefore more suitable for clinical use.

Computing Molecular Signatures as Optima of a Bi-Objective Function: Method and Application to Prediction in Oncogenomics

Background Filter feature selection methods compute molecular signatures by selecting subsets of genes in the ranking of a valuation function. The motivations of the valuation functions choice are almost always clearly stated, but those for selecting the genes according to their ranking are hardly ever explicit. Method We addressed the computation of molecular signatures by searching the optima of a bi-objective function whose solution space was the set of all possible molecular signatures, ie, the set of subsets of genes. The two objectives were the size of the signature-to be minimized–and the interclass distance induced by the signature-to be maximized–. Results We showed that: 1) the convex combination of the two objectives had exactly n optimal non empty signatures where n was the number of genes, 2) the n optimal signatures were nested, and 3) the optimal signature of size k was the subset of k top ranked genes that contributed the most to the interclass distance. We applied our feature selection method on five public datasets in oncology, and assessed the prediction performances of the optimal signatures as input to the diagonal linear discriminant analysis (DLDA) classifier. They were at the same level or better than the best-reported ones. The predictions were robust, and the signatures were almost always significantly smaller. We studied in more details the performances of our predictive modeling on two breast cancer datasets to predict the response to a preoperative chemotherapy: the performances were higher than the previously reported ones, the signatures were three times smaller (11 versus 30 gene signatures), and the genes member of the signature were known to be involved in the response to chemotherapy. Conclusions Defining molecular signatures as the optima of a bi-objective function that combined the signature size and the interclass distance was well founded and efficient for prediction in oncogenomics. The complexity of the computation was very low because the optimal signatures were the sets of genes in the ranking of their valuation. Software can be freely downloaded from http://gardeux-vincent.eu/DeltaRanking.php

Semi-supervised model applied to the prediction of the response to preoperative chemotherapy for breast cancer

Breast cancer is the second most frequent one, and the first one affecting the women. The standard treatment has three main stages: a preoperative chemotherapy followed by a surgery operation, then an post-operatory chemotherapy. Because the response to the preoperative chemotherapy is correlated to a good prognosis, and because the clinical and biological information do not yield to efficient predictions of the response, a lot of research effort is being devoted to the design of predictors relying on the measurement of genes’ expression levels. In the present paper, we report our works for designing genomic predictors of the response to the preoperative chemotherapy, making use of a semi-supervised machine learning approach. The method is based on margin geometric information of patterns of low density areas, computed on a labeled dataset and on an unlabeled one.