PubMed Health⌕ Search

SEARCH · PubMed Health

Results for “Neural Networks, Computer”

Explore indexed PubMed citations for clinical trials, systematic reviews and public health research. Read source abstracts and follow each citation to its original PubMed record.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 1,225 records · Page 68Linked to original sources
PubMed2002

The role of DNA deformation energy at individual base steps for the identification of DNA-protein binding sites.

We examine the use of deformation propensity at individual base steps for the identification of DNA-protein binding sites. We have previously demonstrated that estimates of the total energy to bend DNA to its bound conformation can partially explain indirect DNA-protein interactions. We now show that the deformation propensities at each base step are not equally informative for classifying a sequence as a binding site, and that applying non-uniform weights to the contribution of each base step to aggregate deformation propensity can greatly improve classification accuracy. We show that a perceptron can be trained to use the deformation propensity at each step in a sequence to generate such weights.

Binding Sites↗
PubMed1994

[Information improving secondary evaluation procedures in neuroradiology].

The development of special computer and software systems has exerted great influence on computer-assisted image processing in neuroradiology. In this article we try to give an overview of the present and future applications. The applications can be subdivided into segmentation, image analysis, three-dimensional imaging and image fusion. Segmentation plays an important role, because it is frequently the basis for the other methods. Image analysis, like planimetry, volumetry or the calculation of angles, is already used in clinical applications. In this way the course of diseases can be checked objectively. Three-dimensional imaging is especially useful in the planning of surgical and stereotactic approaches. The results and value of three-dimensional imaging depend on the segmentation technique. Therefore, they are evaluated differently. Image fusion allows the simultaneous presentation of image data of different modalities. In particular, the image fusion of morphological findings and functional parameters is applied in clinical neuroradiology. Our experience with these methods suggests that computer-assisted image processing will be of considerable help in neuroradiology.

Brain↗
PubMed1993

Automated decision making: the role of expert computer systems in the future of optometry.

BACKGROUND: Artificial intelligence (AI) refers to the ability of a machine, or system, or computer program to make intelligent decisions using the same methods that humans would use. By definition, and expert system (ES) is a computer program that uses knowledge and inference procedures to solve problems which would otherwise require the application of human expertise. METHODS: The role of expert systems in current and future applications in a number of fields were reviewed. RESULTS: Expert systems have and will be making an impact in many fields, including optometry in the future. CONCLUSIONS: Expert systems present an opportunity for the profession of optometry to become more efficient or accurate, provided that their development cost could be justified.

Artificial Intelligence↗
PubMed1995

A formal model for quality assurance in coloproctologic surgery.

In the last 30 years the advanced countries experienced a large growth of financial and social resources devoted to Health Care (HC) [1] The need of better co-ordinating activities of a composite large set of specialists, and the need of avoiding further growth of financial and social resources without reducing benefits, stimulated the growth of Health Care Evaluation (HCE) and Quality Assurance (QA) studies. In this paper we show how system analysis and precise assessment of the clinical process can be largely enhanced by using formal techniques. The proposed methodology takes advantage of positive experiences in computer science. It is based on a formal model of the clinical process that provides the physicians with support for assessing diagnostic procedures in a co-operative surgical environment [7]. Diagnostic, grading, and staging procedures are integrated in a single model that can be formally expressed and analyzed. The approach is exemplified by presenting an elementary model of the diagnosis, grading and staging procedures for the colo-rectal cancer and ulcerative colitis.

Colitis, Ulcerative↗
PubMed2005

Large-scale prediction of cationic metabolite identity and migration time in capillary electrophoresis mass spectrometry using artificial neural networks.

We developed a computational technique to assist in the large-scale identification of charged metabolites. The electrophoretic mobility of metabolites in capillary electrophoresis-mass spectrometry (CE-MS) was predicted from their structure, using an ensemble of artificial neural networks (ANNs). Comparison between relative migration times of 241 various cations measured by CE-MS and predicted by a trained ANN ensemble produced a correlation coefficient of 0.931. When we used our technique to characterize all metabolites listed in the KEGG ligand database, the correct compounds among the top three candidates were predicted in 78.0% of cases. We suggest that this approach can be used for the prediction of the migration time of any cation and that it represents a powerful method for the identification of uncharacterized CE-MS peaks in metabolome analysis.

Cations↗
PubMed1998

A locally inhibited lateral neural network: on the fundamental features of a network consisting of neurons with a restricted range of interaction.

A network model that consists of neurons with a restricted range of interaction is presented. The neurons are connected mutually by inhibition weights. The inhibition of the whole network can be controlled by the range of interaction of a neuron. By this local inhibition mechanism, the present network can produce patterns with a small activity from input patterns with various large activities. Moreover, it is shown in simulation that the network has attractors for input patterns. The appearance of attractors is caused by the local interaction of neurons. Thus, we expect that the network not only works as a kind of filter, but also as a memory device for storing the produced patterns. In the present paper, the fundamental features and behavior of the network are studied by using a simple network structure and a simple rule of interaction of neurons. In particular, the relation between the interaction range of a neuron and the activity of input-output patterns is shown in simulation. Furthermore, the limit of the transformation and the size of basin are studied numerically.

Computer Simulation↗
PubMed2000

Computerized analysis of the likelihood of malignancy in solitary pulmonary nodules with use of artificial neural networks.

PURPOSE: To develop a computer-aided diagnostic scheme by using an artificial neural network (ANN) to assist radiologists in the distinction of benign and malignant pulmonary nodules. MATERIALS AND METHODS: Fifty-six chest radiographs of 34 primary lung cancers and 22 benign nodules were digitized with a 0.175-mm pixel size and a 10-bit gray scale. Eight subjective image features were evaluated and recorded by radiologists in each case. A computerized method was developed to extract objective features that could be correlated with the subjective features. An ANN was used to distinguish benign from malignant nodules on the basis of subjective or objective features. The performance of the ANN was compared with that of the radiologists by means of receiver operating characteristic (ROC) analysis. RESULTS: Performance of the ANN was considerably greater with objective features (area under the ROC curve, Az = 0.854) than with subjective features (Az = 0.761). Performance of the ANN was also greater than that of the radiologists (Az = 0.752). CONCLUSION: The computerized scheme has the potential to improve the diagnostic accuracy of radiologists in the distinction of benign and malignant solitary pulmonary nodules.

Adult↗
PubMed2005

Globally multimodal problem optimization via an estimation of distribution algorithm based on unsupervised learning of Bayesian networks.

Many optimization problems are what can be called globally multimodal, i.e., they present several global optima. Unfortunately, this is a major source of difficulties for most estimation of distribution algorithms, making their effectiveness and efficiency degrade, due to genetic drift. With the aim of overcoming these drawbacks for discrete globally multimodal problem optimization, this paper introduces and evaluates a new estimation of distribution algorithm based on unsupervised learning of Bayesian networks. We report the satisfactory results of our experiments with symmetrical binary optimization problems.

Algorithms↗
PubMed2003

Cytoscape: a software environment for integrated models of biomolecular interaction networks.

Cytoscape is an open source software project for integrating biomolecular interaction networks with high-throughput expression data and other molecular states into a unified conceptual framework. Although applicable to any system of molecular components and interactions, Cytoscape is most powerful when used in conjunction with large databases of protein-protein, protein-DNA, and genetic interactions that are increasingly available for humans and model organisms. Cytoscape's software Core provides basic functionality to layout and query the network; to visually integrate the network with expression profiles, phenotypes, and other molecular states; and to link the network to databases of functional annotations. The Core is extensible through a straightforward plug-in architecture, allowing rapid development of additional computational analyses and features. Several case studies of Cytoscape plug-ins are surveyed, including a search for interaction pathways correlating with changes in gene expression, a study of protein complexes involved in cellular recovery to DNA damage, inference of a combined physical/functional interaction network for Halobacterium, and an interface to detailed stochastic/kinetic gene regulatory models.

Algorithms↗
PubMed2025

IGCN: integrative graph convolution networks for patient level insights and biomarker discovery in multi-omics integration.

MOTIVATION: Developing computational tools for integrative analysis across multiple types of omics data has been of immense importance in cancer molecular biology and precision medicine research. While recent advancements have yielded integrative prediction solutions for multi-omics data, these methods lack a comprehensive and cohesive understanding of the rationale behind their specific predictions. To shed light on personalized medicine and unravel previously unknown characteristics within integrative analysis of multi-omics data, we introduce a novel integrative neural network approach for cancer molecular subtype and biomedical classification applications, named Integrative Graph Convolutional Networks (IGCN). RESULTS: To demonstrate the superiority of IGCN, we compare its performance with other state-of-the-art approaches across different cancer subtype and biomedical classification tasks. Our experimental results show that our proposed model outperforms the state-of-the-art and baseline methods. IGCN identifies which types of omics data receive more emphasis for each patient when predicting a specific class. Additionally, IGCN has the capability to pinpoint significant biomarkers from a range of omics data types. AVAILABILITY AND IMPLEMENTATION: The source code is available at https://github.com/bozdaglab/IGCN.

Humans↗
PubMed2002

Neural networks with local receptive fields and superlinear VC dimension.

Local receptive field neurons comprise such well-known and widely used unit types as radial basis function (RBF) neurons and neurons with center-surround receptive field. We study the Vapnik-Chervonenkis (VC) dimension of feedforward neural networks with one hidden layer of these units. For several variants of local receptive field neurons, we show that the VC dimension of these networks is superlinear. In particular, we establish the bound Omega(W log k) for any reasonably sized network with W parameters and k hidden nodes. This bound is shown to hold for discrete center-surround receptive field neurons, which are physiologically relevant models of cells in the mammalian visual system, for neurons computing a difference of gaussians, which are popular in computational vision, and for standard RBF neurons, a major alternative to sigmoidal neurons in artificial neural networks. The result for RBF neural networks is of particular interest since it answers a question that has been open for several years. The results also give rise to lower bounds for networks with fixed input dimension. Regarding constants, all bounds are larger than those known thus far for similar architectures with sigmoidal neurons. The superlinear lower bounds contrast with linear upper bounds for single local receptive field neurons also derived here.

Algorithms↗
PubMed1993

Evaluation of a neural network model of amnesia in diffuse cerebral atrophy.

A digital computer program generating a simulated neural network was used to construct a model which can show behaviour resembling human associative memory. The experimental network uses distributed storage, and, in this respect, its functional organisation resembles that suggested by reported observations of neuronal activity in the human temporal lobe during memory storage and recall. Inactivation of increasing numbers of randomly distributed network units simulated advancing cerebral atrophy. This caused progressive impairment of performance, resembling the gradual deterioration of memory function observed in chronic diffuse cerebral degeneration. Unit inactivation had similar effects on recall whether the same units were inactivated before or after learning. This differs from most relevant observations of amnesia resulting from diffuse cerebral disease. While the model may functionally resemble long-term information storage sites in the brain, other cerebral mechanisms participating in learning and remembering are also damaged by diffuse cerebral atrophy.

Amnesia↗
PubMed2005

Computer-aided diagnostic scheme for distinction between benign and malignant nodules in thoracic low-dose CT by use of massive training artificial neural network.

Low-dose helical computed tomography (LDCT) is being applied as a modality for lung cancer screening. It may be difficult, however, for radiologists to distinguish malignant from benign nodules in LDCT. Our purpose in this study was to develop a computer-aided diagnostic (CAD) scheme for distinction between benign and malignant nodules in LDCT scans by use of a massive training artificial neural network (MTANN). The MTANN is a trainable, highly nonlinear filter based on an artificial neural network. To distinguish malignant nodules from six different types of benign nodules, we developed multiple MTANNs (multi-MTANN) consisting of six expert MTANNs that are arranged in parallel. Each of the MTANNs was trained by use of input CT images and teaching images containing the estimate of the distribution for the "likelihood of being a malignant nodule," i.e., the teaching image for a malignant nodule contains a two-dimensional Gaussian distribution and that for a benign nodule contains zero. Each MTANN was trained independently with ten typical malignant nodules and ten benign nodules from each of the six types. The outputs of the six MTANNs were combined by use of an integration ANN such that the six types of benign nodules could be distinguished from malignant nodules. After training of the integration ANN, our scheme provided a value related to the "likelihood of malignancy" of a nodule, i.e., a higher value indicates a malignant nodule, and a lower value indicates a benign nodule. Our database consisted of 76 primary lung cancers in 73 patients and 413 benign nodules in 342 patients, which were obtained from a lung cancer screening program on 7847 screenees with LDCT for three years in Nagano, Japan. The performance of our scheme for distinction between benign and malignant nodules was evaluated by use of receiver operating characteristic (ROC) analysis. Our scheme achieved an Az (area under the ROC curve) value of 0.882 in a round-robin test. Our scheme correctly identified 100% (76/76) of malignant nodules as malignant, whereas 48% (200/413) of benign nodules were identified correctly as benign. Therefore, our scheme may be useful in assisting radiologists in the diagnosis of lung nodules in LDCT.

Algorithms↗
PubMed2004

Detection of skin cancer by classification of Raman spectra.

Skin lesion classification based on in vitro Raman spectroscopy is approached using a nonlinear neural network classifier. The classification framework is probabilistic and highly automated. The framework includes a feature extraction for Raman spectra and a fully adaptive and robust feedforward neural network classifier. Moreover, classification rules learned by the neural network may be extracted and evaluated for reproducibility, making it possible to explain the class assignment. The classification performance for the present data set, involving 222 cases and five lesion types, was 80.5%+/-5.3% correct classification of malignant melanoma, which is similar to that of trained dermatologists based on visual inspection. The skin cancer basal cell carcinoma has a classification rate of 95.8%+/-2.7%, which is excellent. The overall classification rate of skin lesions is 94.8%+/-3.0%. Spectral regions, which are important for network classification, are demonstrated to reproduce. Small distinctive bands in the spectrum, corresponding to specific lipids and proteins, are shown to hold the discriminating information which the network uses to diagnose skin lesions.

Artificial Intelligence↗
PubMed2006

Symmetric interactions within a homogeneous starburst cell network can lead to robust asymmetries in dendrites of starburst amacrine cells.

Starburst amacrine cells in the mammalian retina respond asymmetrically to movement along their dendrites; centrifugal movement elicits stronger responses in each dendrite than centripetal movement. It has been suggested that the asymmetrical response can be attributed to intrinsic properties of the processes themselves. But starburst cells are known to release and have receptors for both GABA and acetylcholine. We tested whether interactions within the starburst cell network can contribute to their directional response properties. In a computational model of interacting starburst amacrine cells, we simulated the response of individual dendrites to moving light stimuli. By setting the model parameters for "synaptic connection strength" (cs) to positive or negative values, overlapping starburst dendrites could either excite or inhibit each other. For some values of cs, we observed a very robust inward/outward asymmetry of the starburst dendrites consistent with the reported physiological findings. This is the case, for example, if a starburst cell receives inhibition from other starburst cells located in its surround. For other values of cs, individual dendrites can respond best either to inward movement or respond symmetrically. A properly wired network of starburst cells can therefore account for the experimentally observed asymmetry of their response to movement, independent of any internal biophysical or biochemical properties of starburst cell dendrites.

Acetylcholine↗
PubMed2006

Classification of EMG signals using wavelet neural network.

An accurate and computationally efficient means of classifying electromyographic (EMG) signal patterns has been the subject of considerable research effort in recent years. Quantitative analysis of EMG signals provides an important source of information for the diagnosis of neuromuscular disorders. Following the recent development of computer-aided EMG equipment, different methodologies in the time domain and frequency domain have been followed for quantitative analysis. In this study, feedforward error backpropagation artificial neural networks (FEBANN) and wavelet neural networks (WNN) based classifiers were developed and compared in relation to their accuracy in classification of EMG signals. In these methods, we used an autoregressive (AR) model of EMG signals as an input to classification system. A total of 1200 MUPs obtained from 7 normal subjects, 7 subjects suffering from myopathy and 13 subjects suffering from neurogenic disease were analyzed. The success rate for the WNN technique was 90.7% and for the FEBANN technique 88%. The comparisons between the developed classifiers were primarily based on a number of scalar performance measures pertaining to the classification. The WNN-based classifier outperformed the FEBANN counterpart. The proposed WNN classification may support expert decisions and add weight to EMG differential diagnosis.

Adolescent↗
PubMed2005

Two-cell to N-cell heterogeneous, inhibitory networks: precise linking of multistable and coherent properties.

Inhibitory networks are now recognized as being the controllers of several brain rhythms. However, experimental work with inhibitory cells is technically difficult not only because of their smaller percentage of the neuronal population, but also because of their diverse properties. As such, inhibitory network models with tight links to the experimental data are needed to understand their contributions to population rhythms. However, mathematical analyses of network models with more than two cells is challenging when the cellular models involve biophysical details. We use bifurcation analyses and simulations to show that two-cell analyses can quantitatively predict N-cell (N = 20, 50, 100) network dynamics for heterogeneous, inhibitory networks. Interestingly, multistable states in the two-cell system are manifest as different and distinct coherent network patterns in the N-cell networks for the same parameter sets.

Action Potentials↗