Disease relevance of Neural Networks (Computer)

• CONCLUSIONS: A neural network model for the dosing of the HMG-CoA-reductase inhibitors simvastatin and atorvastatin demonstrated an ability to predict appropriate dosing, but inclusion of other factors (eg, age, body weight, sex) and a larger sample size may be necessary for development of a more accurate model [1].
• A neural network model of movement control in normal and Parkinson's disease (PD) conditions is proposed to simulate the time-varying dose-response relationship underlying the effects of levodopa on movement amplitude and movement duration in PD patients [2].
• The two neural network models are used in a case study on the diagnosis of polycystic ovary syndrome, which is a common female endocrine disorder characterized by menstrual abnormalities, hirsutism and infertility [3].
• A neural network computer program, trained to predict secondary structure of proteins by exposing it to matching sets of primary and secondary structures from a database, was used to analyze the human immunodeficiency virus (HIV) proteins p17, gp120, and gp41 from their amino acid sequences [4].

Psychiatry related information on Neural Networks (Computer)

• A previously described neural-network model (Desmond 1991; Desmond and Moore 1988; Moore et al. 1989) predicts that both CS-onset-evoked and CS-offset-evoked stimulus trace processes acquire associative strength during classical conditioning, and that CR waveforms can be altered by manipulating the time at which the processes are activated [5].

High impact information on Neural Networks (Computer)

• Accordingly, neural network models are efficiently trained using a dopamine-like reinforcement signal [6].
• Using a purely feed forward neural network model, we show that following repeated directional activation, NMDA-dependent long-term potentiation/long-term depotentiation (LTP/LTD) could result in an experience-dependent asymmetrization of receptive fields [7].
• Based on five risk factors, that is, gender, tumor number, lobar tumor distribution, tumor size, grade of vascular invasion, artificial neural network models predicting the likelihood of HCC recurrence within 1, 2, and 3 consecutive years after transplantation were developed [8].
• Thirty-six per cent of the variance in baseline BNT performance was explained by a neural network model using left and right (1)H-MRS ratios(creatine/N-acetylaspartate) as input [9].
• This longitudinal data was analyzed through predictive modeling using artificial neural network feed-forward/back-propagation algorithms with multilayer perceptron architecture.RESULTS: Eleven of the 26 patients showed statistically significant, prolonged decreases in their PSA velocity (PSAV) [10].

Biological context of Neural Networks (Computer)

• We identified potentially significant new mutations associated with lopinavir resistance and demonstrated the utility of neural network models in predicting phenotypic susceptibility from complex genotypes [11].
• The applicability of the neural network computer package PSDD (Perceptron Simulator for Drug Design/Perceptron-type Neural Network Simulator) in structure-activity relationship (SAR) studies was investigated [12].
• We expect that a new generation of data mining tools will be required to support recursive fractal geometrical, combinatorial, and neural network models of the genomic basis of morphogenesis [13].

Anatomical context of Neural Networks (Computer)

• The present study investigates how such a mechanism might operate in a large-scale connectionist model of this circuitry that takes into account the major hippocampal subdivisions, oscillatory population dynamics and the time scale on which ACh exerts its effects in the hippocampus [14].
• A neural network model of how dopamine and prefrontal cortex activity guides short- and long-term information processing within the cortico-striatal circuits during reward-related learning of approach behavior is proposed [15].
• METHODS: The applicability of multilayer perceptron neural networks (MLP) to coronary artery bypass grafting risk prediction was assessed using The Society of Thoracic Surgeons database of 80,606 patients who underwent coronary artery bypass grafting in 1993 [16].

Associations of Neural Networks (Computer) with chemical compounds

• Two neural network models were constructed: 1 based on changes at 11 positions in the protease that were previously recognized as being significant for lopinavir resistance and another based on a newly derived set of 28 mutations that were identified by performing category prevalence analysis [11].
• 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 [17].
• METHODS: The Multi-Layered Perceptron (MLP) neural network was used to predict the dissolution profiles of theophylline pellets containing different ratios of microcrystalline cellulose (MCC) and glyceryl monostearate (GMS) [18].
• Statistical approach to neural network model building for gentamicin peak predictions [19].
• The resulting neural network models were validated by successfully predicting DMSO solubility of compounds in independent test selections [20].

Gene context of Neural Networks (Computer)

• Neural network models were able to accurately predict log IC(50) values for the three types of DHFR to within +/-0.65 log units (data sets ranged approximately 5.5 log units) of the experimentally determined values [21].
• Explorations of Cohen, Dunbar, and McClelland's (1990) connectionist model of Stroop performance [22].
• Dopamine and the mechanisms of cognition: Part I. A neural network model predicting dopamine effects on selective attention [23].
• Theoretical consideration of olfactory axon projection with an activity-dependent neural network model [24].
• To examine this hypothesis, we propose a neural network model of the corticohippocampal system, including central-peripheral visual pathways and theta phase coding in the hippocampus [25].

Analytical, diagnostic and therapeutic context of Neural Networks (Computer)

• A simple neural network model is proposed for kindling--the phenomenon of generating epilepsy by means of repeated electrical stimulation [26].
• Application of serum protein fingerprinting coupled with artificial neural network model in diagnosis of hepatocellular carcinoma [27].

References