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At least 163 records · Page 9Linked to original sources
PubMed2003

Combinative neural network and its applications.

A new approach named combinative neural network (CN) using partial least squares (PLS) analysis to modify the hidden layer in the multi-layered feed forward (MLFF) neural networks (NN) was proposed in this paper. The significant contributions of PLS in the proposed CN are to reorganize the outputs of hidden nodes such that the correlation of variables could be circumvented, to make the network meet the non-linear relationship best between the input and output data of the NN, and to eliminate the risk of over-fitting problem at the same time. The performance of the proposed approach was demonstrated through two examples, a well defined nonlinear approximation problem, and a practical nonlinear pattern classification problem with unknown relationship between the input and output data. The results were compared with those by conventional MLFF NNs. Good performance and time-saving implementation make the proposed method an attractive approach for non-linear mapping and classification.

Journal Article↗
PubMed1993

Adapting the structure of a neural network to extract chemical information. Application to structure-odour relationships.

Two types of neural networks were used to establish relationships between chemical structure and musk odour of 79 nitrobenzenic compounds. Substituents on the five free sites of the benzene ring (one position was always occupied by a t-butyl group) were described using three volume descriptors and three electronegativity descriptors. Musk odour was coded by a binary variable. First a classical network with two hidden layers containing six and three neurons was used. This network gave a better classification (94%) than that obtained by linear discriminant analysis (81%). The odour was then predicted using a leave-ten-out procedure, with 77% of correct prediction for the whole sample. Then a dual two-way network was built to mimic the symmetry of the problem (two sides on a molecule, two muskophore patterns). This network recognized both patterns already known to chemists and gave 99% of correct classifications by taking into account substitution in all positions. As a side benefit of the modified network structure it was possible to evaluate the influence of each of 19 substituents in each of the five possible positions.

Discriminant Analysis↗
PubMed2000

Localization of epileptogenic zones in F-18 FDG brain PET of patients with temporal lobe epilepsy using artificial neural network.

For an objective interpretation of cerebral metabolic pattern to find epileptogenic zones in patients with temporal lobe epilepsy (TLE), we developed a computer-aided classifier using an artificial neural network (ANN). We studied 261 epilepsy patients diagnosed as no abnormal findings (NA, n = 64), left TLE (n = 116), or right TLE (n = 81) on interictal brain F-18-fluorodeoxyglucose positron emission tomography (FDG PET) by the consensus of two expert physicians. Seventeen asymmetry indexes between the mean counts of the 34 mirrored regions were extracted from the spatially normalized images and used as input parameters. The three diagnoses of NA, left TLE, and right TLE were used as outputs of the ANN. The structure of the ANN was optimized with variable error goals and the number of hidden units. On the criteria of agreement of diagnoses with those of expert viewers, the best classifier was chosen, which yielded a maximum average agreement of 85% for the test set when we used an error goal of 20 (sum of squared error) and ten hidden units. We could devise an ANN that performed as well in diagnosing left or right TLE on FDG PET as human experts and could be used as a clinical decision support tool.

Adult↗
PubMed1994

Storage capacity bounds in multilayer neural networks.

General analytic expressions are given for the lower and upper storage capacity bounds of multilayer neural networks which have variable weights between input layer and first hidden layer, and a fixed output function implemented between first hidden and output layer. The special cases of committee and parity machines as well as the limiting cases of networks with minimum and maximum storage capacities are discussed. The results are compared with replica calculations and simulations. An explanation is given as to why the latter have a storage capacity just slightly above the lower limit and how this can be improved.

Algorithms↗
PubMed2026

Population-scale detection of methylation outliers from long-read genome sequencing.

BACKGROUND: Aberrant DNA methylation can mediate the functional effects of rare genetic variation and contribute to imprinting disorders, repeat expansion diseases, and other pathogenic regulatory mechanisms. Long-read sequencing technologies now enable genome-wide detection of CpG methylation alongside genetic variation from a single assay. However, methods for systematic identification and interpretation of methylation outliers from long-read sequencing data remain limited. METHODS: We developed METAFORA, a computational workflow for detecting methylation outlier regions from PacBio and Oxford Nanopore long-read sequencing data. METAFORA constructs population-level methylation references, segments the genome into correlated CpG blocks, infers technical and biological sources of variation through hidden factor estimation, models uncertainty due to variable depth sequencing, and computes covariate-adjusted methylation outlier scores for individual samples. We applied METAFORA across large long-read sequencing cohorts and integrated methylation outliers with multi-omic data. METAFORA is implemented as a snakemake workflow available at https://github.com/tjense25/METAFORA. RESULTS: METAFORA identified methylation outlier regions associated with rare structural variants, tandem repeat expansions, and imprinting abnormalities. We found outlier regions were enriched for molecular outliers across transcriptomic and chromatin accessibility datasets, supporting their functional relevance in gene regulation. In a representative case, METAFORA identified an imprinting defect affecting the GNAS locus associated with an STX16 deletion. CONCLUSIONS: METAFORA enables scalable detection and interpretation of methylation outliers from long-read sequencing data and provides a framework for integrating epigenetic outliers with genomic and multi-omic analyses. These approaches may improve interpretation of rare regulatory variation and support discovery of clinically relevant epigenetic abnormalities in genomic medicine.

DNA methylation↗
PubMed2005

Use of multiple profiles corresponding to a sequence alignment enables effective detection of remote homologues.

MOTIVATION: Position specific scoring matrices (PSSMs) corresponding to aligned sequences of homologous proteins are commonly used in homology detection. A PSSM is generated on the basis of one of the homologues as a reference sequence, which is the query in the case of PSI-BLAST searches. The reference sequence is chosen arbitrarily while generating PSSMs for reverse BLAST searches. In this work we demonstrate that the use of multiple PSSMs corresponding to a given alignment and variable reference sequences is more effective than using traditional single PSSMs and hidden Markov models. RESULTS: Searches for proteins with known 3-D structures have been made against three databases of protein family profiles corresponding to known structures: (1) One PSSM per family; (2) multiple PSSMs corresponding to an alignment and variable reference sequences for every family; and (3) hidden Markov models. A comparison of the performances of these three approaches suggests that the use of multiple PSSMs is most effective. CONTACT: ns@mbu.iisc.ernet.in.

Algorithms↗
PubMed2006

The population biology of bacterial plasmids: a hidden Markov model approach.

Horizontal plasmid transfer plays a key role in bacterial adaptation. In harsh environments, bacterial populations adapt by sampling genetic material from a horizontal gene pool through self-transmissible plasmids, and that allows persistence of these mobile genetic elements. In the absence of selection for plasmid-encoded traits it is not well understood if and how plasmids persist in bacterial communities. Here we present three models of the dynamics of plasmid persistence in the absence of selection. The models consider plasmid loss (segregation), plasmid cost, conjugative plasmid transfer, and observation error. Also, we present a stochastic model in which the relative fitness of the plasmid-free cells was modeled as a random variable affected by an environmental process using a hidden Markov model (HMM). Extensive simulations showed that the estimates from the proposed model are nearly unbiased. Likelihood-ratio tests showed that the dynamics of plasmid persistence are strongly dependent on the host type. Accounting for stochasticity was necessary to explain four of seven time-series data sets, thus confirming that plasmid persistence needs to be understood as a stochastic process. This work can be viewed as a conceptual starting point under which new plasmid persistence hypotheses can be tested.

Bacteria↗
PubMed2003

Bounds on the number of hidden neurons in three-layer binary neural networks.

This paper investigates an important problem concerning the complexity of three-layer binary neural networks (BNNs) with one hidden layer. The neuron in the studied BNNs employs a hard limiter activation function with only integer weights and an integer threshold. The studies are focused on implementations of arbitrary Boolean functions which map from [0, 1]n into [0, 1]. A deterministic algorithm called set covering algorithm (SCA) is proposed for the construction of a three-layer BNN to implement an arbitrary Boolean function. The SCA is based on a unit sphere covering (USC) of the Hamming space (HS) which is chosen in advance. It is proved that for the implementation of an arbitrary Boolean function of n-variables (n > or = 3) by using SCA, [3L/2] hidden neurons are necessary and sufficient, where L is the number of unit spheres contained in the chosen USC of the n-dimensional HS. It is shown that by using SCA, the number of hidden neurons required is much less than that by using a two-parallel hyperplane method. In order to indicate the potential ability of three-layer BNNs, a lower bound on the required number of hidden neurons which is derived by using the method of estimating the Vapnik-Chervonenkis (VC) dimension is also given.

Neural Networks, Computer↗
PubMed1988

["Hidden" organic causes of impotence. Diagnostic methods and critical evaluation of their responsibility].

In five per cent of impotent, sexual dysfunction obviously results from an organic factor which can be today qualified as "classical". "Hidden" organic factors have also been implicated in a variable ratio of the other cases. This paper constitutes a critical review of the role they play in impotence, and of the methods allowing their detection. On one hand, methods allowing evaluation of the respective responsibilities of organic and psychogenic factors (the most important of which remain nocturnal penile tumescence monitoring); on the other hand, specific investigations of each factor. It is no longer possible to deny the existence of "hidden" organic factors in impotence. These factors intervene to varying degrees in one third of cases. Among them erectile failure only by themselves. Hyperprolactinaemia, moderate hypogonadism, infraclinical neuropathies, arterial stenoses and moderate venous leakages seem to play a partial role of organic starter or cofactor, the sexual consequences of which are amplified by psychological factors, partly secondary to the initial sexual failures. Nevertheless, the conjunction of several "hidden" organic factors may probably be sufficient to induce an impotence without involvement of psychological factors.

Erectile Dysfunction↗
PubMed2006

Application of nonlinear analysis methods for identifying relationships between microbial community structure and groundwater geochemistry.

The relationship between groundwater geochemistry and microbial community structure can be complex and difficult to assess. We applied nonlinear and generalized linear data analysis methods to relate microbial biomarkers (phospholipids fatty acids, PLFA) to groundwater geochemical characteristics at the Shiprock uranium mill tailings disposal site that is primarily contaminated by uranium, sulfate, and nitrate. First, predictive models were constructed using feedforward artificial neural networks (NN) to predict PLFA classes from geochemistry. To reduce the danger of overfitting, parsimonious NN architectures were selected based on pruning of hidden nodes and elimination of redundant predictor (geochemical) variables. The resulting NN models greatly outperformed the generalized linear models. Sensitivity analysis indicated that tritium, which was indicative of riverine influences, and uranium were important in predicting the distributions of the PLFA classes. In contrast, nitrate concentration and inorganic carbon were least important, and total ionic strength was of intermediate importance. Second, nonlinear principal components (NPC) were extracted from the PLFA data using a variant of the feedforward NN. The NPC grouped the samples according to similar geochemistry. PLFA indicators of Gram-negative bacteria and eukaryotes were associated with the groups of wells with lower levels of contamination. The more contaminated samples contained microbial communities that were predominated by terminally branched saturates and branched monounsaturates that are indicative of metal reducers, actinomycetes, and Gram-positive bacteria. These results indicate that the microbial community at the site is coupled to the geochemistry and knowledge of the geochemistry allows prediction of the community composition.

Bacteria↗
PubMed2001

Effect of pH on the sorption of uranium in soils.

This work was undertaken to study the influence of soil type and chemical composition on uranium sorption ratios (SR in 1 kg-1) in order to reduce the uncertainty associated with this parameter in risk assessment models. Thirteen soil samples were collected from three different locations in France under different geological conditions. Clay content varied from 7.0 to 50.0%, pH ranged from 5.5 to 8.8 and organic matter content from 1.0 to 4.6%. Soils were incubated at room temperature in polyethylene packets for 28 days in the presence of 1 mg U kg-1 soil. Sorption ratio values varied from 0.9 to 3198 for all soils with no significant effect of soil texture or of organic matter. However, soil pH was highly linearly correlated with (log SR) as a probable consequence of the existence of different uranium complexes as a function of soil pH. The sorption behaviour differences between UO2(2+) and UO2(2+)-carbonate complexes are so great that any other effect of soil properties on U sorption is hidden. Thus, soil pH should be the focus variable for reduction of the uncertainty associated with the soil Kd value used in environmental risk assessments, even for reducing the uncertainty in site-specific Kd values.

Hydrogen-Ion Concentration↗
PubMed2000

Chemometrics approach to the determination of polymorphism of a drug compound by infrared spectroscopy.

A chemometrics approach, multivariate calibration in particular, was used to determine the polymorphism of a drug compound based on Fourier transform infrared (FTIR) spectroscopy. The partial least-squares projection to latent structure makes use of all of the data, and the latent variables created by the method make use of hidden or partially separated peaks for quantitation. This paper illustrates the usefulness of the partial least-squares multivariate calibration method as an efficient tool to determine the polymorphism of a drug. Also, the analysis suggests the use of information from the modeling as diagnostic tools to gain more insight from the data. In particular, the diagnostic tools allow an analyst to assess design characteristics and any shortcomings of a calibration experiment for the polymorphism of a drug compound.

Least-Squares Analysis↗
PubMed2000

An artificial neural network as a model for prediction of survival in trauma patients: validation for a regional trauma area.

BACKGROUND: To develop and validate an artificial neural network (ANN) for predicting survival of trauma patients based on standard prehospital variables, emergency room admission variables, and Injury Severity Score (ISS) using data derived from a regional area trauma system, and to compare this model with known trauma scoring systems. PATIENT POPULATION: The study was composed of 10,609 patients admitted to 24 hospitals comprising a seven-county suburban/rural trauma region adjacent to a major metropolitan area. The data was generated as part of the New York State trauma registry. Study period was from January 1993 through December 1996 (1993-1994: 5,168 patients; 1995: 2,768 patients; 1996: 2,673 patients). METHODS: A standard feed-forward back-propagation neural network was developed using Glasgow Coma Scale, systolic blood pressure, heart rate, respiratory rate, temperature, hematocrit, age, sex, intubation status, ICD-9-CM Injury E-code, and ISS as input variables. The network had a single layer of hidden nodes. Initial network development of the model was performed on the 1993-1994 data. Subsequent models were generated using the 1993, 1994, and 1995 data. The model was tested first on the 1995 and then on the 1996 data. The ANN model was tested against Trauma and Injury Severity Score (TRISS) and ISS using the receiver operator characteristic (ROC) area under the curve [ROC-A(z)], Lemeshow-Hosmer C-statistic, and calibration curves. RESULTS: The ANN showed good clustering of the data, with good separation of nonsurvivors and survivors. The ROCA(z) was 0.912 for the ANN, 0.895 for TRISS, and 0.766 for ISS. The ANN exceeded TRISS with respect to calibration (Lemeshow-Hosmer C-statistic: 7.4 for ANN; 17.1 for TRISS). The prediction of survivors was good for both models. The ANN exceeded TRISS in nonsurvivor prediction. CONCLUSION: An ANN developed for trauma patients using prehospital, emergency room admission data, and ISS gave good prediction of survival. It was accurate and had excellent calibration. This study expands our previous results developed at a single Level I trauma center and shows that an ANN model for predicting trauma deaths can be applied across hospitals with good results

Adolescent↗
PubMed2001

Ideal observer approximation using Bayesian classification neural networks.

It is well understood that the optimal classification decision variable is the likelihood ratio or any monotonic transformation of the likelihood ratio. An automated classifier which maps from an input space to one of the likelihood ratio family of decision variables is an optimal classifier or "ideal observer." Artificial neural networks (ANNs) are frequently used as classifiers for many problems. In the limit of large training sample sizes, an ANN approximates a mapping function which is a monotonic transformation of the likelihood ratio, i.e., it estimates an ideal observer decision variable. A principal disadvantage of conventional ANNs is the potential over-parameterization of the mapping function which results in a poor approximation of an optimal mapping function for smaller training samples. Recently, Bayesian methods have been applied to ANNs in order to regularize training to improve the robustness of the classifier. The goal of training a Bayesian ANN with finite sample sizes is, as with unlimited data, to approximate the ideal observer. We have evaluated the accuracy of Bayesian ANN models of ideal observer decision variables as a function of the number of hidden units used, the signal-to-noise ratio of the data and the number of features or dimensionality of the data. We show that when enough training data are present, excess hidden units do not substantially degrade the accuracy of Bayesian ANNs. However, the minimum number of hidden units required to best model the optimal mapping function varies with the complexity of the data.

Bayes Theorem↗
PubMed2005

An alternative approach for neural network evolution with a genetic algorithm: crossover by combinatorial optimization.

In this work we present a new approach to crossover operator in the genetic evolution of neural networks. The most widely used evolutionary computation paradigm for neural network evolution is evolutionary programming. This paradigm is usually preferred due to the problems caused by the application of crossover to neural network evolution. However, crossover is the most innovative operator within the field of evolutionary computation. One of the most notorious problems with the application of crossover to neural networks is known as the permutation problem. This problem occurs due to the fact that the same network can be represented in a genetic coding by many different codifications. Our approach modifies the standard crossover operator taking into account the special features of the individuals to be mated. We present a new model for mating individuals that considers the structure of the hidden layer and redefines the crossover operator. As each hidden node represents a non-linear projection of the input variables, we approach the crossover as a problem on combinatorial optimization. We can formulate the problem as the extraction of a subset of near-optimal projections to create the hidden layer of the new network. This new approach is compared to a classical crossover in 25 real-world problems with an excellent performance. Moreover, the networks obtained are much smaller than those obtained with classical crossover operator.

Algorithms↗
PubMed2005

Expectation enhances autonomic responses to stimulation of the human subthalamic limbic region.

Recent studies show that the placebo component of a treatment can be investigated by administering therapies either overtly or covertly, without the administration of any placebo. Here, we analyze the effects of open (i.e., expected) versus hidden (i.e., unexpected) stimulations of the human subthalamic region on autonomic responses in Parkinson patients. To do this, we mapped the whole subthalamic region, from the dorsal to the ventral part, and recorded both heart rate and sympathetic responses by using spectral analysis of heart rate variability. We found that open stimulations were more effective than hidden ones only in the ventral subthalamic region, whereas no difference between the two conditions was found in the dorsal aspect. By analyzing the stimulus-response curves in the dorsal, middle, and ventral subthalamic regions, we found that the autonomic response threshold was higher in the hidden than open condition for both heart rate and sympathetic responses only in the ventral part. As this ventralmost portion of the subthalamic region is involved in associative-limbic functions, these data suggest that expectation enhances autonomic responses only if these are elicited in the limbic system. These results extend previous findings on the open-hidden paradigm in deep brain stimulation [Benedetti, F., Colloca, L., Lanotte, M., Bergamasco, B., Torre, E., Lopiano, L., 2004a. Autonomic and emotional responses to open and hidden stimulations of the human subthalamic region. Brain Res. Bull. 63, 203-211.], and indicate that expectation plays a major role in the therapeutic outcome. In light of the interactions between the sympathetic adrenergic system and the immune system, the open-hidden difference in autonomic responses might be relevant to the understanding of how expectations might affect the immune system.

Aged↗
PubMedDate not supplied

Effect of high-frequency spectral components in computer recognition of dysarthric speech based on a Mel-cepstral stochastic model.

Computer speech recognition of individuals with dysarthria, such as cerebral palsy patients, requires a robust technique that can handle conditions of very high variability and limited training data. In this study, a hidden Markov model (HMM) was constructed and conditions investigated that would provide improved performance for a dysarthric speech (isolated word) recognition system intended to act as an assistive/control tool. In particular, we investigated the effect of high-frequency spectral components on the recognition rate of the system to determine if they contributed useful additional information to the system. A small-size vocabulary spoken by three cerebral palsy subjects was chosen. Mel-frequency cepstral coefficients extracted with the use of 15 ms frames served as training input to an ergodic HMM setup. Subsequent results demonstrated that no significant useful information was available to the system for enhancing its ability to discriminate dysarthric speech above 5.5 kHz in the current set of dysarthric data. The level of variability in input dysarthric speech patterns limits the reliability of the system. However, its application as a rehabilitation/control tool to assist dysarthric motor-impaired individuals such as cerebral palsy subjects holds sufficient promise.

Artificial Intelligence↗
PubMed1999

Bayesian neural networks for classification: how useful is the evidence framework?

This paper presents an empirical assessment of the Bayesian evidence framework for neural networks using four synthetic and four real-world classification problems. We focus on three issues; model selection, automatic relevance determination (ARD) and the use of committees. Model selection using the evidence criterion is only tenable if the number of training examples exceeds the number of network weights by a factor of five or ten. With this number of available examples, however, cross-validation is a viable alternative. The ARD feature selection scheme is only useful in networks with many hidden units and for data sets containing many irrelevant variables. ARD is also useful as a hard feature selection method. Results on applying the evidence framework to the real-world data sets showed that committees of Bayesian networks achieved classification accuracies similar to the best alternative methods. Importantly, this was achievable with a minimum of human intervention.

Journal Article↗