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1.
Am J Hum Genet ; 81(3): 475-91, 2007 Sep.
Article in English | MEDLINE | ID: mdl-17701894

ABSTRACT

Down syndrome caused by chromosome 21 trisomy is the most common genetic cause of mental retardation in humans. Disruption of the phenotype is thought to be the result of gene-dosage imbalance. Variations in chromosome 21 gene expression in Down syndrome were analyzed in lymphoblastoid cells derived from patients and control individuals. Of the 359 genes and predictions displayed on a specifically designed high-content chromosome 21 microarray, one-third were expressed in lymphoblastoid cells. We performed a mixed-model analysis of variance to find genes that are differentially expressed in Down syndrome independent of sex and interindividual variations. In addition, we identified genes with variations between Down syndrome and control samples that were significantly different from the gene-dosage effect (1.5). Microarray data were validated by quantitative polymerase chain reaction. We found that 29% of the expressed chromosome 21 transcripts are overexpressed in Down syndrome and correspond to either genes or open reading frames. Among these, 22% are increased proportional to the gene-dosage effect, and 7% are amplified. The other 71% of expressed sequences are either compensated (56%, with a large proportion of predicted genes and antisense transcripts) or highly variable among individuals (15%). Thus, most of the chromosome 21 transcripts are compensated for the gene-dosage effect. Overexpressed genes are likely to be involved in the Down syndrome phenotype, in contrast to the compensated genes. Highly variable genes could account for phenotypic variations observed in patients. Finally, we show that alternative transcripts belonging to the same gene are similarly regulated in Down syndrome but sense and antisense transcripts are not.


Subject(s)
Chromosomes, Human, Pair 21/genetics , Down Syndrome/genetics , Gene Expression , Genetic Variation , Base Sequence , Female , Humans , Male , Molecular Sequence Data , Oligonucleotide Array Sequence Analysis , Phenotype , Transcription, Genetic
2.
J Neurochem ; 97 Suppl 1: 104-9, 2006 Apr.
Article in English | MEDLINE | ID: mdl-16635258

ABSTRACT

To understand the aetiology and the phenotypic severity of Down syndrome, we searched for transcriptional signatures in a substructure of the brain (cerebellum) during post-natal development in a segmental trisomy 16 model, the Ts1Cje mouse. The goal of this study was to investigate the effects of trisomy on changes in gene expression across development time. The primary gene-dosage effect on triplicated genes (approximately 1.5) was observed at birth [post-natal day 0 (P0)], at P15 and P30. About 5% of the non-triplicated genes were significantly differentially expressed between trisomic and control cerebellum, while 25% of the transcriptome was modified during post-natal development of the cerebellum. Indeed, only 165, 171 and 115 genes were dysregulated in trisomic cerebellum at P0, P15 and P30, respectively. Surprisingly, there were only three genes dysregulated in development and in trisomic animals in a similar or opposite direction. These three genes (Dscr1, Son and Hmg14) were, quite unexpectedly, triplicated in the Ts1Cje model and should be candidate genes for understanding the aetiology of the phenotype observed in the cerebellum.


Subject(s)
Cerebellum/growth & development , Cerebellum/metabolism , Down Syndrome/genetics , Transcription, Genetic , Animals , Disease Models, Animal , Gene Expression , Gene Expression Regulation, Developmental , Growth/genetics , Mice , Mice, Mutant Strains , Oligonucleotide Array Sequence Analysis , Trisomy
3.
Hum Mol Genet ; 14(3): 373-84, 2005 Feb 01.
Article in English | MEDLINE | ID: mdl-15590701

ABSTRACT

The central nervous system of persons with Down syndrome presents cytoarchitectural abnormalities that likely result from gene-dosage effects affecting the expression of key developmental genes. To test this hypothesis, we have investigated the transcriptome of the cerebellum of the Ts1Cje mouse model of Down syndrome during postnatal development using microarrays and quantitative PCR (qPCR). Genes present in three copies were consistently overexpressed, with a mean ratio relative to euploid of 1.52 as determined by qPCR. Out of 63 three-copy genes tested, only five, nine and seven genes had ratios >2 or <1.2 at postnatal days 0 (P0), P15 and P30, respectively. This gene-dosage effect was associated with a dysregulation of the expression of some two-copy genes. Out of 8258 genes examined, the Ts1Cje/euploid ratios differed significantly from 1.0 for 406 (80 and 154 with ratios above 1.5 and below 0.7, respectively), 333 (11 above 1.5 and 55 below 0.7) and 246 genes (59 above 1.5 and 69 below 0.7) at P0, P15 and P30, respectively. Among the two-copy genes differentially expressed in the trisomic cerebellum, six homeobox genes, two belonging to the Notch pathway, were severely repressed. Overall, at P0, transcripts involved in cell differentiation and development were over-represented among the dysregulated genes, suggesting that cell differentiation and migration might be more altered than cell proliferation. Finally, global gene profiling revealed that transcription in Ts1Cje mice is more affected by the developmental changes than by the trisomic state, and that there is no apparent detectable delay in the postnatal development of the cerebellum of Ts1Cje mice.


Subject(s)
Cerebellum/metabolism , Down Syndrome/genetics , Gene Expression Profiling , Animals , Cell Differentiation , Cerebellum/growth & development , Disease Models, Animal , Down Syndrome/metabolism , Gene Dosage , Gene Expression Regulation, Developmental , Mice , Mice, Inbred C57BL , Principal Component Analysis
4.
IEEE Trans Neural Netw ; 14(4): 804-19, 2003.
Article in English | MEDLINE | ID: mdl-18238061

ABSTRACT

We study how statistical tools which are commonly used independently can advantageously be exploited together in order to improve neural network estimation and selection in nonlinear static modeling. The tools we consider are the analysis of the numerical conditioning of the neural network candidates, statistical hypothesis tests, and cross validation. We present and analyze each of these tools in order to justify at what stage of a construction and selection procedure they can be most useful. On the basis of this analysis, we then propose a novel and systematic construction and selection procedure for neural modeling. We finally illustrate its efficiency through large-scale simulations experiments and real-world modeling problems.

5.
IEEE Trans Neural Netw ; 14(6): 1553-9, 2003.
Article in English | MEDLINE | ID: mdl-18244599

ABSTRACT

In nonlinear regression theory, the sandwich estimator of the covariance matrix of the model parameters is known as a consistent estimator, even when the parameterized model does not contain the regression. However, in the latter case, we emphasize the fact that the consistency of the sandwich holds only if the inputs of the training set are the values of independent identically distributed random variables. Thus, in the frequent practical modeling situation involving a training set whose inputs are deliberately chosen and imposed by the designer, we question the opportunity to use the sandwich estimator rather than the simple estimator based on the inverse squared Jacobian.

6.
Neural Netw ; 13(4-5): 463-84, 2000.
Article in English | MEDLINE | ID: mdl-10946394

ABSTRACT

We present the theoretical results about the construction of confidence intervals for a nonlinear regression based on least squares estimation and using the linear Taylor expansion of the nonlinear model output. We stress the assumptions on which these results are based, in order to derive an appropriate methodology for neural black-box modeling; the latter is then analyzed and illustrated on simulated and real processes. We show that the linear Taylor expansion of a nonlinear model output also gives a tool to detect the possible ill-conditioning of neural network candidates, and to estimate their performance. Finally, we show that the least squares and linear Taylor expansion based approach compares favorably with other analytic approaches, and that it is an efficient and economic alternative to the nonanalytic and computationally intensive bootstrap methods.


Subject(s)
Least-Squares Analysis , Neural Networks, Computer , Confidence Intervals , Linear Models , Nonlinear Dynamics
7.
IEEE Trans Neural Netw ; 11(1): 80-90, 2000.
Article in English | MEDLINE | ID: mdl-18249741

ABSTRACT

We propose a design procedure of neural internal model control systems for stable processes with delay. We show that the design of such nonadaptive indirect control systems necessitates only the training of the inverse of the model deprived from its delay, and that the presence of the delay thus does not increase the order of the inverse. The controller is then obtained by cascading this inverse with a rallying model which imposes the regulation dynamic behavior and ensures the robustness of the stability. A change in the desired regulation dynamic behavior, or an improvement of the stability, can be obtained by simply tuning the rallying model, without retraining the whole model reference controller. The robustness properties of internal model control systems being obtained when the inverse is perfect, we detail the precautions which must be taken for the training of the inverse so that it is accurate in the whole space visited during operation with the process. In the same spirit, we make an emphasis on neural models affine in the control input, whose perfect inverse is derived without training. The control of simulated processes illustrates the proposed design procedure and the properties of the neural internal model control system for processes without and with delay.

8.
Neural Comput ; 11(4): 863-70, 1999 May 15.
Article in English | MEDLINE | ID: mdl-10226186

ABSTRACT

In response to Zhu and Rower (1996), a recent communication (Goutte, 1997) established that leave-one-out cross validation is not subject to the "no-free-lunch" criticism. Despite this optimistic conclusion, we show here that cross validation has very poor performances for the selection of linear models as compared to classic statistical tests. We conclude that the statistical tests are preferable to cross validation for linear as well as for nonlinear model selection.


Subject(s)
Models, Statistical , Probability , Linear Models , Nonlinear Dynamics , Reproducibility of Results
9.
IEEE Trans Neural Netw ; 5(2): 178-84, 1994.
Article in English | MEDLINE | ID: mdl-18267789

ABSTRACT

The paper first summarizes a general approach to the training of recurrent neural networks by gradient-based algorithms, which leads to the introduction of four families of training algorithms. Because of the variety of possibilities thus available to the "neural network designer," the choice of the appropriate algorithm to solve a given problem becomes critical. We show that, in the case of process modeling, this choice depends on how noise interferes with the process to be modeled; this is evidenced by three examples of modeling of dynamical processes, where the detrimental effect of inappropriate training algorithms on the prediction error made by the network is clearly demonstrated.

10.
IEEE Trans Neural Netw ; 3(4): 529-39, 1992.
Article in English | MEDLINE | ID: mdl-18276455

ABSTRACT

The definition of the requirements for the design of a neural network associative memory, with on-chip training, in standard digital CMOS technology is addressed. Various learning rules that can be integrated in silicon and the associative memory properties of the resulting networks are investigated. The relationships between the architecture of the circuit and the learning rule are studied in order to minimize the extra circuitry required for the implementation of training. A 64-neuron associative memory with on-chip training has been manufactured, and its future extensions are outlined. Beyond the application to the specific circuit described, the general methodology for determining the accuracy requirements can be applied to other circuits and to other autoassociative memory architectures.

11.
IEEE Trans Neural Netw ; 3(6): 962-8, 1992.
Article in English | MEDLINE | ID: mdl-18276492

ABSTRACT

It is shown that neural network classifiers with single-layer training can be applied efficiently to complex real-world classification problems such as the recognition of handwritten digits. The STEPNET procedure, which decomposes the problem into simpler subproblems which can be solved by linear separators, is introduced. Provided appropriate data representations and learning rules are used, performance comparable to that obtained by more complex networks can be achieved. Results from two different databases are presented: an European database comprising 8700 isolated digits and a zip code database from the US Postal Service comprising 9000 segmented digits. A hardware implementation of the classifier is briefly described.

12.
Phys Rev A Gen Phys ; 38(12): 6365-6372, 1988 Dec 15.
Article in English | MEDLINE | ID: mdl-9900395
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