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At least 1,243 records · Page 69Linked to original sources
PubMed2002

Automatic recognition of alertness and drowsiness from EEG by an artificial neural network.

We present a novel method for classifying alert vs drowsy states from 1 s long sequences of full spectrum EEG recordings in an arbitrary subject. This novel method uses time series of interhemispheric and intrahemispheric cross spectral densities of full spectrum EEG as the input to an artificial neural network (ANN) with two discrete outputs: drowsy and alert. The experimental data were collected from 17 subjects. Two experts in EEG interpretation visually inspected the data and provided the necessary expertise for the training of an ANN. We selected the following three ANNs as potential candidates: (1) the linear network with Widrow-Hoff (WH) algorithm; (2) the non-linear ANN with the Levenberg-Marquardt (LM) rule; and (3) the Learning Vector Quantization (LVQ) neural network. We showed that the LVQ neural network gives the best classification compared with the linear network that uses WH algorithm (the worst), and the non-linear network trained with the LM rule. Classification properties of LVQ were validated using the data recorded in 12 healthy volunteer subjects, yet whose EEG recordings have not been used for the training of the ANN. The statistics were used as a measure of potential applicability of the LVQ: the t-distribution showed that matching between the human assessment and the network output was 94.37+/-1.95%. This result suggests that the automatic recognition algorithm is applicable for distinguishing between alert and drowsy state in recordings that have not been used for the training.

Adult↗
PubMed2000

Neural network recognition of chemical class information in mobility spectra obtained at high temperatures.

A minimal neural network was applied to a large library of high-temperature mobility spectra drawn from 16 chemical classes including 154 substances with 2000 spectra at various concentrations. A genetic algorithm was used to create a representative subset of points from the mobility spectrum as input to a cascade-type back-propagation network. This network demonstrated that significant information specific to chemical class was located in the spectral region near the reactant ions. This network failed to generalize the solution to unfamiliar compounds necessitating the use of complete spectra in network processing. An extended back-propagation network classified unfamiliar chemicals by functional group with a mean for average values of 0.83 without sulfides and 0.79 with sulfides. Further experiments confirmed that chemical class information was resident in the spectral region near the reactant ions. Deconvolution of spectra demonstrated the presence of ions, merged with the reactant ion peaks that originated from introduced samples. The ability of the neural network to generalize the solution to unfamiliar compounds suggests that these ions are distinct and class specific.

Algorithms↗
PubMed2006

Detection of marine toxins, brevetoxin-3 and saxitoxin, in seawater using neuronal networks.

There is a need for assay systems that can detect known and unanticipated neurotoxins associated with harmful algal blooms. The present work describes our attempt to monitor the presence of brevetoxin-3 (PbTx-3) and saxitoxin (STX) in a seawater matrix using the neuronal network biosensor (NNB). The NNB relies on cultured mammalian neurons grown over microelectrode arrays, where the inherent bioelectrical activity of the network manifested as extracellular action potentials can be monitored noninvasively. Spinal cord neuronal networks were prepared from embryonic mice and the mean spike rate across the network was analyzed before and during exposure to the toxins. Extracellular action potentials from the network are highly sensitive not only to purified STX and PbTx-3, but also when in combination with matrixes such as natural seawater and algal growth medium. Detection limits for STX and PbTx-3, respectively, are 0.031 and 0.33 nM in recording buffer and 0.076 and 0.48 nM in the presence of 25-fold-diluted seawater. Our results demonstrated that neuronal networks could be used for analysis of Alexandrium fundyense (STX-producer) and Karenia brevis (PbTx-producer) algal samples lysed directly in the seawater-based growth medium and appropriately diluted with HEPES-buffered recording medium. The cultured network responded by changes in mean spike rate to the presence of STX-or PbTx-producing algae but not to the samples of two non-STX and non-PbTx isolates of the same algal genera. This work provides evidence that the NNB has the capacity to rapidly detect toxins associated with cells of toxic algal species or as dissolved forms present in seawater and hasthe potential for monitoring toxin levels during harmful algal blooms.

Animals↗
PubMed2001

Provider-sponsored networks in public sector behavioral health.

Numerous behavioral health and social service providers (primarily not-for-profit community mental health and chemical dependency treatment centers) have been joining forces on a local and regional basis to create their own managed care contracting entities, referred to as provider-sponsored networks. Even though these networks are emerging as an alternative managed care model, little is known about them. This descriptive survey provides baseline information about the status of these networks. Using a structured interview questionnaire, a random sample of 15 networks from 12 states was surveyed. Participants were asked about various organizational characteristics of networks, reasons for formation, managed care readiness, obstacles to success, differential value of networks, and recommendations for success. Findings suggest that regional networks are making gradual progress, although many of the assumed benefits have been elusive.

Behavior Therapy↗
PubMed2003

Using improved neural network model to analyze RSP, NOx and NO2 levels in urban air in Mong Kok, Hong Kong.

As the health impact of air pollutants existing in ambient addresses much attention in recent years, forecasting of air pollutant parameters becomes an important and popular topic in environmental science. Airborne pollution is a serious, and will be a major problem in Hong Kong within the next few years. In Hong Kong, Respirable Suspended Particulate (RSP) and Nitrogen Oxides NOx and NO2 are major air pollutants due to the dominant diesel fuel usage by public transportation and heavy vehicles. Hence, the investigation and prediction of the influence and the tendency of these pollutants are of significance to public and the city image. The multi-layer perceptron (MLP) neural network is regarded as a reliable and cost-effective method to achieve such tasks. The works presented here involve developing an improved neural network model, which combines the principal component analysis (PCA) technique and the radial basis function (RBF) network, and forecasting the pollutant levels and tendencies based in the recorded data. In the study, the PCA is firstly used to reduce and orthogonalize the original input variables (data), these treated variables are then used as new input vectors in RBF neural network model established for forecasting the pollutant tendencies. Comparing with the general neural network models, the proposed model possesses simpler network architecture, faster training speed, and more satisfactory predicting performance. This improved model is evaluated by using hourly time series of RSP, NOx and NO2 concentrations collected at Mong Kok Roadside Gaseous Monitory Station in Hong Kong during the year 2000. By comparing the predicted RSP. NOx and NO2 concentrations with the actual data of these pollutants recorded at the monitory station, the effectiveness of the proposed model has been proven. Therefore, in authors' opinion, the model presented in the paper is a potential tool in forecasting air quality parameters and has advantages over the traditional neural network methods.

Air Pollutants↗
PubMed1999

A neural network approach to the biopsy diagnosis of early acute renal transplant rejection.

AIMS: To develop and test a neural network to assist in the histological diagnosis of early acute renal allograft rejection. METHODS AND RESULTS: We used three sets of biopsies to train and test the network: 100 'routine' biopsies from Leicester; 21 selected difficult biopsies which had already been evaluated by most of the renal transplant pathologists in the UK, in a study of the Banff classification of allograft pathology and 25 cases which had been classified as 'borderline' according to the Banff classification in a review of transplant biopsies from Oxford. The correct diagnosis for each biopsy was defined by careful retrospective clinical review. Biopsies where this review did not provide a clear diagnosis were excluded. Each biopsy was graded for 12 histological features and the data was entered into a simple single layer perception network, designed using the MATLAB neural network toolbox. Results were compared with logistic regression using the same data, and with 'conventional' histological diagnosis. If the network was trained only with the 100 'routine' cases, its performance with either of the other sets was poor. However, if either of the 'difficult' sets was added to the training group, testing with the other 'difficult' group improved dramatically; 19 of the 21 'Banff' study cases were diagnosed correctly. This was achieved using observations made by a trainee pathologist. The result is better than was achieved by any of the many experienced pathologists who had previously seen these biopsies (maximum 18/21 correct), and is considerably better than that achieved by using logistic regression with the same data. CONCLUSION: A neural network can provide a considerable improvement in the diagnosis of early acute allograft rejection, though further development work will be needed before this becomes a routine diagnostic tool. The selection of cases used to train the network is crucial to the quality of its performance. There is scope to improve the system further by incorporating clinical information. Other related areas where this approach is likely to be of value are discussed.

Acute Disease↗
PubMed2000

How to maintain blood supply during computer network breakdown: a manual backup system.

Electronic data management systems using computer network systems and client/server architecture are increasingly used in laboratories and transfusion services. Severe problems arise if there is no network access to the database server and critical functions are not available. We describe a manual backup system (MBS) developed to maintain the delivery of blood products to patients in a hospital transfusion service in case of a computer network breakdown. All data are kept on a central SQL database connected to peripheral workstations in a local area network (LAN). Request entry from wards is performed via machine-readable request forms containing self-adhesive specimen labels with barcodes for test tubes. Data entry occurs on-line by bidirectional automated systems or off-line manually. One of the workstations in the laboratory contains a second SQL database which is frequently and incrementally updated. This workstation is run as a stand-alone, read-only database if the central SQL database is not available. In case of a network breakdown, the time-graded MBS is launched. Patient data, requesting ward and ordered tests/requests, are photocopied through a template from the request forms on special MBS worksheets serving as laboratory journal for manual processing and result report (a copy is left in the laboratory). As soon as the network is running again the data from the off-line period are entered into the primary SQL server. The MBS was successfully used at several occasions. The documentation of a 90-min breakdown period is presented in detail. Additional work resulted from the copy work and the belated manual data entry after restoration of the system. There was no delay in issue of blood products or result reporting. The backup system described has been proven to be simple, quick and safe to maintain urgent blood supply and distribution of laboratory results in case of unexpected network breakdown.

Blood Transfusion↗
PubMed1998

Diagnostic classification of autoantibody repertoires in endocrine ophthalmopathy using an artificial neural network.

PURPOSE: The aim of this study was to classify the human IgG autoantibody repertoire of sera from patients suffering from endocrine ophthalmopathy (EOP) and healthy subjects (CTRL) for diagnostic purposes using the recently developed Megablot technique. This technique allows for the simultaneous and quantitative screening of a large set of antigens and uses multivariate statistical techniques and an artificial neural network. METHODS: Sera were tested against Western blots (WBs) of SDS-PAGE preparations of proteins from human extraorbital eye muscle (EOP: n = 16; CTRL: n = 11). Digital image analysis was performed. The blots were subsequently analyzed by multivariate statistical techniques (analysis of discriminance) and an artificial neural network (probalistic neural network). RESULTS: The sera of both the EOP and CTRL groups showed a complex staining pattern against WBs of SDS-PAGEs from human eye muscle. Using the multivariate statistical technique for classification, all of the known samples and 85% of the unknown samples (not presented during calculation) were assigned to their correct clinical group. Using the artificial neural network as classifier, all of the samples presented during training and 96.3% of the unknown samples (not trained) were assigned correctly. CONCLUSIONS: The artificial neural network exceeds the ability of multivariate statistical techniques such as analysis of discriminance to assign unknown samples to their correct predefined group. Thus, the neural network exceeds other methods in generalizing some similarities of blots used for classification. This study reveals that our new technique and its evaluation using a neural network can be used as a helpful diagnostic tool in autoimmune diseases such as endocrine ophthalmopathy.

Adult↗
PubMed1993

Protein secondary structure prediction with partially recurrent neural networks.

Partially recurrent neural networks with different topologies are applied for secondary structure prediction of proteins. The state of some activations in the network is available after a pattern presentation via feedback connections as additional input during the processing of the next pattern in a sequence. A reference data set containing 91 proteins in the training set and 15 non-homologous proteins in the test set is used for training and testing a network with a modified, hierarchical Elman architecture. The network predicts the secondary structures alpha-helix, beta-sheet, and "coil" for each amino acid. The percentage of correctly classified amino acids is 67.83% on the training set and 63.98% on the test set. The best performance of a three-layer feedforward network is 62.7% on the same test set. A cascaded network, where the outputs of the recurrent network are processed by a second net with 13 x 3 inputs, four hidden and three output units has a predictive performance of 64.49%. The best corresponding feedforward net has a performance of 64.3%.

Amino Acid Sequence↗
PubMed1999

A neural network to predict symptomatic lung injury.

A nonlinear neural network that simultaneously uses pre-radiotherapy (RT) biological and physical data was developed to predict symptomatic lung injury. The input data were pre-RT pulmonary function, three-dimensional treatment plan doses and demographics. The output was a single value between 0 (asymptomatic) and 1 (symptomatic) to predict the likelihood that a particular patient would become symptomatic. The network was trained on data from 97 patients for 400 iterations with the goal to minimize the mean-squared error. Statistical analysis was performed on the resulting network to determine the model's accuracy. Results from the neural network were compared with those given by traditional linear discriminate analysis and the dose-volume histogram reduction (DVHR) scheme of Kutcher. Receiver-operator characteristic (ROC) analysis was performed on the resulting network which had Az = 0.833 +/- 0.04. (Az is the area under the ROC curve.) Linear discriminate multivariate analysis yielded an Az = 0.813 +/- 0.06. The DVHR method had Az = 0.521 +/- 0.08. The network was also used to rank the significance of the input variables. Future studies will be conducted to improve network accuracy and to include functional imaging data.

Adult↗
PubMed1996

Improving prediction of protein secondary structure using structured neural networks and multiple sequence alignments.

The prediction of protein secondary structure by use of carefully structured neural networks and multiple sequence alignments has been investigated. Separate networks are used for predicting the three secondary structures alpha-helix, beta-strand, and coil. The networks are designed using a priori knowledge of amino acid properties with respect to the secondary structure and the characteristic periodicity in alpha-helices. Since these single-structure networks all have less than 600 adjustable weights, overfitting is avoided. To obtain a three-state prediction of alpha-helix, beta-strand, or coil, ensembles of single-structure networks are combined with another neural network. This method gives an overall prediction accuracy of 66.3% when using 7-fold cross-validation on a database of 126 nonhomologous globular proteins. Applying the method to multiple sequence alignments of homologous proteins increases the prediction accuracy significantly to 71.3% with corresponding Matthew's correlation coefficients C alpha = 0.59, C beta = 0.52, and Cc = 0.50. More than 72% of the residues in the database are predicted with an accuracy of 80%. It is shown that the network outputs can be interpreted as estimated probabilities of correct prediction, and, therefore, these numbers indicate which residues are predicted with high confidence.

Amino Acid Sequence↗
PubMed1994

A new concept of sequence data distribution on wide area networks.

Accepted concepts in distributed applications design have been applied in the development of a network-based system for the synchronization of remote sequence database access sites by an incremental update mechanism. Computer hardware requirements, network bandwidth, and stability considerations make centralized access to essential computerized resources undesirable. A network model has been developed to distribute access over a collection of remotely situated computer centers. The formally independent database-access nodes join to form a heterogeneous, long distance, co-operating network that can compensate for the deficiencies of unstable network links thereby ensuring uninterrupted access to the resource. In order to guarantee consistency among these nodes, several distributed transaction protocols have been investigated; based on these results, a prototype system has been implemented. A layered software architecture makes the distributed transaction protocol transparent to the individual database system and the underlying network. Individual components of this network communicate by means of Remote Procedure Calls (RPCs). A prototype software system operates to synchronize up to data copies of the PIR-International Protein Sequence Database (Barker et al., 1993) at a number of different sites using the public Internet as the transport vehicle.

Computer Communication Networks↗
PubMed2004

A Bayesian connectivity-based approach to constructing probabilistic gene regulatory networks.

MOTIVATION: We have hypothesized that the construction of transcriptional regulatory networks using a method that optimizes connectivity would lead to regulation consistent with biological expectations. A key expectation is that the hypothetical networks should produce a few, very strong attractors, highly similar to the original observations, mimicking biological state stability and determinism. Another central expectation is that, since it is expected that the biological control is distributed and mutually reinforcing, interpretation of the observations should lead to a very small number of connection schemes. RESULTS: We propose a fully Bayesian approach to constructing probabilistic gene regulatory networks (PGRNs) that emphasizes network topology. The method computes the possible parent sets of each gene, the corresponding predictors and the associated probabilities based on a nonlinear perceptron model, using a reversible jump Markov chain Monte Carlo (MCMC) technique, and an MCMC method is employed to search the network configurations to find those with the highest Bayesian scores to construct the PGRN. The Bayesian method has been used to construct a PGRN based on the observed behavior of a set of genes whose expression patterns vary across a set of melanoma samples exhibiting two very different phenotypes with respect to cell motility and invasiveness. Key biological features have been faithfully reflected in the model. Its steady-state distribution contains attractors that are either identical or very similar to the states observed in the data, and many of the attractors are singletons, which mimics the biological propensity to stably occupy a given state. Most interestingly, the connectivity rules for the most optimal generated networks constituting the PGRN are remarkably similar, as would be expected for a network operating on a distributed basis, with strong interactions between the components.

Algorithms↗
PubMed2002

EEG signal processing in anaesthesia. Use of a neural network technique for monitoring depth of anaesthesia.

BACKGROUND: The Bispectral Index (BIS) is a proprietary index of anaesthesia depth, which is correlated with the level of consciousness and probability of intraoperative recall. The present study investigates the use of a neural network technique to obtain a non-proprietary index of the depth of anaesthesia from the processed EEG data. METHODS: Two hundred patients, who underwent general abdominal surgery, were recruited for our trial. For anaesthesia we used a total i.v. technique, tracheal intubation, and artificial ventilation. Fourteen EEG variables, including the BIS, were extracted from the EEG, monitored with an EEG computerized monitor, and then stored on a computer. Data from 150 patients were used to train the neural network. All the variables, excluding the BIS, were used as input data in the neural network. The output targets of the network were provided by anaesthesia scores ranging from 10 to 100 assigned by the anaesthesiologist according to the observer's assessment of alertness and sedation (OAA/S) and other clinical means of assessing depth of anaesthesia. Data from the other 50 patients were used to test the model and for statistical analysis. RESULTS: The artificial neural network was successfully trained to predict an anaesthesia depth index, the NED (neural network evaluated depth), ranging from 0 to 100. The correlation coefficient between the NED and the BIS over the test set was 0.94 (P<0.0001). CONCLUSION: We have developed a neural network model, which evaluates 13 processed EEG parameters to produce an index of anaesthesia depth, which correlates very well with the BIS during total i.v. anaesthesia with propofol.

Abdomen↗
PubMedDate not supplied

A more biologically plausible learning rule than backpropagation applied to a network model of cortical area 7a.

Area 7a of the posterior parietal cortex of the primate brain is concerned with representing head-centered space by combining information about the retinal location of a visual stimulus and the position of the eyes in the orbits. An artificial neural network was previously trained to perform this coordinate transformation task using the backpropagation learning procedure, and units in its middle layer (the hidden units) developed properties very similar to those of area 7a neurons presumed to code for spatial location (Andersen and Zipser, 1988; Zipser and Andersen, 1988). We developed two neural networks with architecture similar to Zipser and Andersen's model and trained them to perform the same task using a more biologically plausible learning procedure than backpropagation. This procedure is a modification of the Associative Reward-Penalty (AR-P) algorithm (Barto and Anandan, 1985), which adjusts connection strengths using a global reinforcement signal and local synaptic information. Our networks learn to perform the task successfully to any degree of accuracy and almost as quickly as with backpropagation, and the hidden units develop response properties very similar to those of area 7a neurons. In particular, the probability of firing of the hidden units in our networks varies with eye position in a roughly planar fashion, and their visual receptive fields are large and have complex surfaces. The synaptic strengths computed by the AR-P algorithm are equivalent to and interchangeable with those computed by backpropagation. Our networks also perform the correct transformation on pairs of eye and retinal positions never encountered before. All of these findings are unaffected by the interposition of an extra layer of units between the hidden and output layers. These results show that the response properties of the hidden units of a layered network trained to perform coordinate transformations, and their similarity with those of area 7a neurons, are not a specific result of backpropagation training. The fact that they can be obtained by a more biologically plausible learning rule corroborates the validity of this neural network's computational algorithm as a plausible model of how area 7a may perform coordinate transformations.

Algorithms↗
PubMed2006

Evidence of interaction network evolution by whole-genome duplications: a case study in MADS-box proteins.

Recent investigations on metazoan transcription factors (TFs) indicate that single-gene duplication events and the gain and loss of protein domains are 2 crucial factors in shaping their protein-protein interaction networks. Plant genomes, on the other hand, have a history of polyploidy and whole-genome duplications (WGDs), and thus, their study helps to understand whether WGDs have also had a significant influence on protein network evolution. Here we investigate the evolution of the interaction network in the well-studied MADS domain MIKC-type proteins, a TF family which plays an important role in both the vegetative and the reproductive phases of plant life. We combine phylogenetic reconstruction, protein domain analysis, and interaction data from different species. We show that, unlike previously analyzed interaction networks, the MIKC-type protein network displays a characteristic topology, with overall high inter-subfamily connectivity, shared interactors between paralogs, and conservation of interaction patterns across species. The evaluation of the number of MIKC-type proteins at key time points throughout the evolution of land plants in the lineage leading to Arabidopsis suggested that most duplicates were retained after each round of WGD. We provide evidence that an initial network, formed by 9-11 homodimerizing proteins interacting with each other, existed in the common ancestor of all seed plants. This basic structure has been conserved after each round of WGD, adding layers of paralogs with similar interaction patterns. We thus present the first model where we can show that a network of eukaryotic TFs has evolved via rounds of WGD. Furthermore, we found that in subfamilies in which the K domain is most diverged, the interactions with other subfamilies have been largely lost. We discuss the possibility that such a high proportion of genes were retained after each WGD because of their capacity to form higher order complexes involving proteins from different subfamilies. The simultaneous duplications allowed for the conservation of the quantitative balance between the constituents and facilitated sub- and neofunctionalization through differential expression of whole units.

Computational Biology↗
PubMed1994

Analysis of E.coli promoter structures using neural networks.

Backpropagation neural network is trained to identify E.coli promoters of all spacing classes (15 to 21). A three module approach is employed wherein the first neural net module predicts the consensus boxes, the second module aligns the promoters to a length of 65 bases and the third neural net module predicts the entire sequence of 65 bases taking care of the possible interdependencies between the bases in the promoters. The networks were trained with 106 promoters and random sequences which were 60% AT rich and tested on 126 promoters (Bacterial, Mutant and Phage promoters). The network was 98% successful in promoter recognition and 90.2% successful in non-promoter recognition when tested on 5000 randomly generated sequences. The network was further trained with 11 mutated non-promoters and 8 mutated promoters of the p22ant promoter. The testing set with 7 mutated promoters and 13 mutated non-promoters of p22ant were identified. The network was upgraded using total 1665 data of promoters and non-promoters to identify any promoter sequences in the gene sequences. The network identified the locations of P1, P2 and P3 promoters in the pBR322 plasmid. A search for the start codon, Ribosomal Binding Site and the stop codon by a string search procedure has also been added to find the possible promoters that can yield protein products. The network was also successfully tested on a synthetic plasmid pWM528.

Base Sequence↗
PubMed1998

Use of neural networks to model complex immunogenetic associations of disease: human leukocyte antigen impact on the progression of human immunodeficiency virus infection.

Complex immunogenetic associations of disease involving a large number of gene products are difficult to evaluate with traditional statistical methods and may require complex modeling. The authors evaluated the performance of feed-forward backpropagation neural networks in predicting rapid progression to acquired immunodeficiency syndrome (AIDS) for patients with human immunodeficiency virus (HIV) infection on the basis of major histocompatibility complex variables. Networks were trained on data from patients from the Multicenter AIDS Cohort Study (n = 139) and then validated on patients from the DC Gay cohort (n = 102). The outcome of interest was rapid disease progression, defined as progression to AIDS in <6 years from seroconversion. Human leukocyte antigen (HLA) variables were selected as network inputs with multivariate regression and a previously described algorithm selecting markers with extreme point estimates for progression risk. Network performance was compared with that of logistic regression. Networks with 15 HLA inputs and a single hidden layer of five nodes achieved a sensitivity of 87.5% and specificity of 95.6% in the training set, vs. 77.0% and 76.9%, respectively, achieved by logistic regression. When validated on the DC Gay cohort, networks averaged a sensitivity of 59.1% and specificity of 74.3%, vs. 53.1% and 61.4%, respectively, for logistic regression. Neural networks offer further support to the notion that HIV disease progression may be dependent on complex interactions between different class I and class II alleles and transporters associated with antigen processing variants. The effect in the current models is of moderate magnitude, and more data as well as other host and pathogen variables may need to be considered to improve the performance of the models. Artificial intelligence methods may complement linear statistical methods for evaluating immunogenetic associations of disease.

Acquired Immunodeficiency Syndrome↗