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Neural network analysis of combined conventional and experimental prognostic markers in prostate cancer: a pilot study.

Prostate cancer is the second most common malignancy in men in the UK. The disease is unpredictable in its behaviour and, at present, no single investigative method allows clinicians to differentiate between tumours that will progress and those that will remain quiescent. There is an increasing need for novel means to predict prognosis and outcome of the disease. The aim of this study was to assess the value of artificial neural networks in predicting outcome in prostate cancer in comparison with statistical methods, using a combination of conventional and experimental biological markers. Forty-one patients with different stages and grades of prostate cancer undergoing a variety of treatments were analysed. Artificial neural networks were used as follows: eight input neurons consisting of six conventional factors (age, stage, bone scan findings, grade, serum PSA, treatment) and two experimental markers (immunostaining for bcl-2 and p53, which are both apoptosis-regulating genes). Twenty-one patients were used for training and 20 for testing. A total of 80% of the patients were correctly classified regarding outcome using the combination of factors. When both bcl-2 and p53 immunoreactivity were excluded from the analysis, correct prediction of the outcome was achieved in only 60% of the patients (P = 0.0032). This study was able to demonstrate the value of artificial neural networks in the analysis of prognostic markers in prostate cancer. In addition, the potential for using this technology to evaluate novel markers is highlighted. Further large-scale analyses are required to incorporate this methodology into routine clinical practice.

Aged↗

[Developmental mechanisms of neural network in the cerebellar system].

Various cell adhesion molecules and neurotrophic factors are involved in cell migration, axonal elongation and synaptogenesis during the development of neural networks. The developmental mechanisms of neural networks were investigated in the mouse cerebellar system in relation to the neuron-glia interactions and the expression of cell adhesion molecules. Radial migration of Purkinje cells in the cerebellar primordium is guided by contact with radial glial processes, and expression of the neuron-glia cell adhesion molecule tenascin is involved in this process. Purkinje cell migration is obstructed in the reeler mutant mouse, and the disorganized arrangement of radial glial processes is considered to be responsible for this defect. Pontine nuclei and inferior olivary nuclei constitute the cerebellar afferent system, and these neurons are generated from the rhombic lip which is originated from the alar plate of the metencephalon in common with the cerebellar primordium. Pontine nuclei migrate tangentially from the rhombic lip under the pia mater, and the binding sites for peanut lectin are specifically expressed on the migratory route. Thy-1 molecule, a member of the immunoglobulin superfamily is expressed transiently in the synaptogenesis between the cerebellar afferent fibers and their target neurons in the developing cerebellar cortex. The homophilic binding activity of this molecule is considered to be involved in the recognition between presynaptic terminals and postsynaptic structures.

Animals↗

[Use of neural network computer technology in the determination of therapeutic strategy in patients with stomach and duodenal ulcer].

The authors present the expert neural network programmed complex "Axon" allowing the prognosis of the course of ulcer disease of the stomach and duodenum to be made. On the basis of the prognosis obtained certain medical strategy can be recommended. The neural network in which the data of treatment of 909 patients with ulcer disease of the stomach and duodenum had been introduced was taken as a basis of the complex. The program is registered in the Russian copyright agency for PC programs, data base and topologies of integrated circuits. The authors consider that the approach proposed will allow the correct medical policy to be chosen which can lead to better results of treatment of patients with ulcer disease and thus the 100 years long contradictions between different specialists in determining medical policy for such category of patients will be overcome.

Duodenal Ulcer↗

A neural network aid for the early diagnosis of cardiac ischemia in patients presenting to the emergency department with chest pain.

STUDY OBJECTIVE: Chest pain is the second most common chief complaint presented to the emergency department. Although the causes of chest pain span the clinical spectrum from the trivial to the life threatening, it is often difficult to identify which patients have the most common life-threatening cause, cardiac ischemia. Because of the potential for poor outcome if this diagnosis is missed, physicians have had a low threshold for admitting patients with chest pain to the hospital, the vast majority of whom are found not to have cardiac ischemia. In an earlier study with a large chest pain patient registry, an artificial neural network was shown to be able to identify the subset of patients who present to the ED with chest pain who have sustained acute myocardial infarction. The objective of this study was to use the same registry to determine whether a network could be trained accurately to identify the larger subset of patients who have cardiac ischemia. METHODS: Two thousand two hundred four adult patients presenting to the ED with chest pain who received an ECG were used to train and test an artificial neural network to recognize the presence of cardiac ischemia. Only the data available at the time of initial patient contact were used to replicate the conditions of real-time evaluation. Forty variables from patient history, physical examination, ECG, and the first set of chemical cardiac marker determinations were used to train and subsequently test the network. The network was trained and tested by using the jackknife variance technique to allow for the network to be trained on as many of the features of the small subset of ischemic patients as possible. Network accuracy was compared with 2 existing aids to the diagnosis of cardiac ischemia, as well as a derived regression model. RESULTS: The network had a sensitivity of 88.1% (95% confidence interval [CI] 84.8% to 91.4%) and a specificity of 86.2% (95% CI 84.6% to 87.7%) for cardiac ischemia despite the fact that a mean of 5% of all required network input data and 41% of cardiac chemical marker data were missing. The network also performed more accurately than the 3 other tested approaches. CONCLUSION: These data suggest that an artificial neural network might be able to identify which patients who present to the ED with chest pain have cardiac ischemia with useful sensitivities and specificities.

Chest Pain↗

Prediction of hyperkalemia in dogs from electrocardiographic parameters using an artificial neural network.

OBJECTIVE: To predict severe hyperkalemia from single electrocardiogram (ECG) tracings. METHODS: Ten conditioned dogs each underwent this protocol three times: Under isoflurane anesthesia, 2 mEq/kg/hr of potassium chloride was given intravenously until P-waves were absent from the ECG and ventricular rates decreased > or =20% in < or =5 minutes. Serum potassium levels (K(+)) were measured at regular intervals with concurrent digital storage of lead II of the surface ECG. A three-layer artificial neural network with four hidden nodes was trained to predict K(+) from 15 separate elements of corresponding ECG data. Data were divided into a training set and a test set. Sensitivity, specificity, and diagnostic accuracy for recognizing hyperkalemia were calculated for the test set based on a prospectively defined K(+) = 7.5. RESULTS: The model produced data for 189 events; 139 were placed in the training set and 50 in the test set. The test set had 37 potassium levels at or above 7.5 mmol/L. The neural network had a sensitivity of 89% (95% CI = 75% to 97%) and a specificity of 77% (95% CI = 46% to 95%) in recognizing these. The positive likelihood ratio was 3.87. Overall accuracy of this model was 86% (95% CI = 73% to 94%). Mean (+/-SD) difference between predicted and actual K(+) values was 0.4 +/- 2.0 (95% CI = -0.2 to 1.0). CONCLUSIONS: An artificial neural network can accurately diagnose experimental hyperkalemia using ECG parameters. Further work could potentially demonstrate its usefulness in bedside diagnosis of human subjects.

Animals↗

A neural network applied to criminal psychological profiling: an Italian initiative.

The author presents a brief discussion of criminal profiling followed by an introduction to the Italian Neural Network for Psychological Criminal Profiling (NNPCP) project. This project, based on a so-called neural network and data mining, is an innovative technique being developed with the intention of extending criminal profiling to single serious crimes through the use of a computerized database.

Crime↗

Neural network based on the input organization of an identified neuron signaling impending collision.

1. We describe a four-layered neural network (Fig. 1), based on the input organization of a collision signaling neuron in the visual system of the locust, the lobula giant movement detector (LGMD). The 250 photoreceptors ("P" units) in layer 1 are excited by any change in illumination, generated when an image edge passes over them. Layers 2 and 3 incorporate both excitatory and inhibitory interactions, and layer 4 consists of a single output element, equivalent to the locust LGMD. 2. The output element of the neural network, the "LGMD", responds directionally when challenged with approaching versus receding objects, preferring approaching objects (Figs. 2-4). The time course and shape of the "LGMD" response matches that of the LGMD (Fig. 4). Directionality is maintained with objects of various sizes and approach velocities. The network is tuned to direct approach (Fig. 5). The "LGMD" shows no directional selectivity for translatory motion at a constant velocity across the "eye", but its response increases with edge velocity (Figs. 6 and 9). 3. The critical image cues for a selective response to object approach by the "LGMD" are edges that change in extent or in velocity as they move (Fig. 7). Lateral inhibition is crucial to the selectivity of the "LGMD" and the selective response is abolished or else much reduced if lateral inhibition is taken out of the network (Fig. 7). We conclude that lateral inhibition in the neuronal network for the locust LGMD also underlies the experimentally observed critical image cues for its directional response. 4. Lateral inhibition shapes the velocity tuning of the network for objects moving in the X and Y directions without approaching the eye (see Fig. 1). As an edge moves over the eye at a constant velocity, a race occurs between the excitation that is caused by edge movement and which passes down the network and the inhibition that passes laterally. Excitation must win this race for units in layer 3 to reach threshold (Fig. 8). The faster the edge moves over the eye the more units in layer 3 reach threshold and pass excitation on to the "LGMD" (Fig. 9). 5. Lateral inhibition shapes the tuning of the network for objects moving in the Z direction, toward or away from the eye (see Fig. 1). As an object approaches the eye there is a buildup of excitation in the "LGMD" throughout the movement whereas the response to object recession is often brief, particularly for high velocities. During object motion, a critical race occurs between excitation passing down the network and inhibition directed laterally, excitation must win this race for the rapid buildup in excitation in the "LGMD" as seen in the final stages of object approach (Figs. 10-12). The buildup is eliminated if, during object approach, excitation cannot win this race (as happens when the spread of inhibition laterally takes < 1 ms Fig. 13, D and E). Taking all lateral inhibition away increases the "LGMD" response to object approach, but overall directional selectivity is reduced as there is also a lot of residual network excitation following object recession (Fig. 13B). 6. Directional selectivity for rapidly approaching objects is further enhanced at the level of the "LGMD" by the timing of a feed-forward, inhibitory loop onto the "LGMD", activated when a large number of receptor units are excited in a short time. The inhibitory loop is activated at the end of object approach, truncating the excitatory "LGMD" response after approach has ceased, but at the initiation of object recession (*Fig. 2, 3, and 13). Eliminating the feed-forward, inhibitory loop prolongs the "LGMD" response to both receding and approaching objects (Fig. 13F).

Animals↗

High-throughput cDNA screening utilizing a low order neural network filter.

A low order neural network-based filter was designed as a rapid screening agent for single-spanning transmembrane regions in an integrated informatics system. A rapid screening algorithm was seen as a compromise between costly structure-specific techniques and simple rules that gave a high false-positive rate for cDNA. The filter was applied to a library of 2123 anonymous cDNA sequences, which resulted in 61 detections. Evaluation of the detections with two other dissimilar computer prediction algorithms yielded strong transmembrane predictions for 15 of the detections, while 8 of the detections resulted in a definitive negative result. Homology searches performed on the sequences with detection reports yielded 13 homologs in the predicted reading frame, four of which are membrane associated.

Animals↗

Evidence of interaction between human lumbosacral and cervical neural networks during gait.

Changes in H-max amplitude resulting from alteration in arm position have been used with changes in gait double support durations resulting from similar changes in arm posture to investigate interaction, if any, between the lumbosacral and cervical neural networks in seven normal subjects. Since the reflex and gait responses of each lower limb were altered by changes in upper limb position, it was suggested that in humans also, there may be interaction not only in a static situation, but also during gait between the lumbosacral and cervical neural networks. The responses of the right leg were always greater than those of the left leg irrespective of the arm position; This retention of inter-limb relation may indicate a dominance of the lumbosacral neural network over its cervical counterpart.

Adult↗

Modeling the pharmacokinetics and pharmacodynamics of a unique oral hypoglycemic agent using neural networks.

PURPOSE: To develop a predictive population pharmacokinetic/ pharmacodynamic (PK/PD) model for repaglinide (REP), an oral hypoglycemic agent, using artificial neural networks (ANNs). METHODS: REP, glucose concentrations, and demographic data from a dose ranging Phase 2 trial were divided into a training set (70%) and a test set (30%). NeuroShell Predictor was used to create predictive PK and PK/PD models using population covariates: evaluate the relative significance of different covariates; and simulate the effect of covariates on the PK/PD of REP. Predictive performance was evaluated by calculating root mean square error and mean error for the training and test sets. These values were compared to naive averaging (NA) and randomly generated numbers (RN). RESULTS: Covariates found to have an influence on PK of REP include dose, gender. race, age, and weight. Covariates affecting the glucose response included dose, gender, and weight. These differences are not expected to be clinically significant. CONCLUSIONS: We came to the following three conclusions: 1) ANNs are more precise than NA and RN for both PK and PD; 2) the bias was acceptable for ANNs as compared with NA and RN; and 3) neural networks offer a quick and simple method for predicting, for identifying significant covariates, and for generating hypotheses.

Blood Glucose↗

[Bayesian regularized BP neural network model for quantitative relationship between the electrochemical reduction potential and molecular structures of chlorinated aromatic compounds].

Bayesian regularized BP neural network (BRBPNN) technique was applied in QSPR model in environmental field. The BRBPNN model for quantitative relationship between the electrochemical reduction potential (ERP) and chemical structures of 87 chlorinated aromatic compounds was established. The structure descriptor pool is consisted of Cl number (Cl), molecular weight (MW) and 6 quantum chemistry parameters which are calculated by MOPAC2000 built in ChemOffice2004, including energy of the highest occupied molecular orbital (E(HOMO)), energy of the lowest occupied molecular orbital (E(LUMO)), heat of formation(HF), dipole(DIP), electronic energy(EE), core-core repulsion(CCR). The achieved optimal network structure was 6-20-1, which possessed stronger fitting and prediction capacity than that of the stepwise linear regression and with the correlation coefficients square and the mean square error for the training set and the test set as 0.999 and 0.000105, 0.965 and 0.00159 respectively. The sum of square weights between each input neuron and the hidden layer of BRBPNN(6-20-1) indicate the effect of descriptor on the electric potential declining in the order of ELUMO > EHOMO > HF> CCR > EE > DIP. The scatter diagrams show that the EE descriptors had positive effect on ERP, and ELUMO, HF, DIP had negative effects, and EHOMO and CCR showed ambiguous effects. Results show that Bayesian regularized BP neural network is of automated regularization parameter selection capability and thus may ensure the excellent generation ability and robustness. This study threw more light on the applicability of electrochemical treatment for the chlorinated aromatic compounds and the analysis on electrochemical reduction mechanism.

Bayes Theorem↗

Design and test of neural networks and statistical classifiers in computer-aided movement analysis: a case study on gait analysis.

OBJECTIVE: To describe the methods for designing and testing diagnostic systems in movement analysis and to verify the clinical usefulness of neural networks and statistical classifiers in a case study. DESIGN: Connectionist and statistical models trained and tested with measured data. BACKGROUND: A basic need in rehabilitation and related fields is to efficiently manage the vast information obtained from a movement analysis laboratory. Many studies have dealt with the interpretation of measured variables in order to correlate objective descriptors to the presence and/or severity of specific neuromusculoskeletal disorders or their consequences. This traditional analytical approach has been complemented in the last decade by new non-linear classification tools called neural networks. METHODS: A gait analysis study on 148 lower limb arthrosis patients and 88 age-matched control subjects. Pathological and healthy gait patterns obtained from force plates wer discriminated by means of multilayer perceptrons and statistical classifiers. RESULTS: Ten input features were enough to train a multilayer perceptron with six hidden neurons. The discrimination rate of the neural net was 80% after cross-validation, significantly higher (P<0.05) than the performance of a Bayes quadratic classifier (about 75%). A great variance due to a small cross-validation set could be demonstrated. CONCLUSIONS: Strict statistical requirements must be observed for designing a neural network. Although these models attain a better performance than conventional statistical approaches, the benefits they bring are sometimes not sufficient to justify their use. Furthermore, clinicians routinely involved in critical decisions may not consider such diagnostic systems reliable enough.

Journal Article↗

A back-propagation neural network predicts absorption maxima of chimeric human red/green visual pigments.

The absorption spectra of human red and green visual pigments have peak wavelengths, lambda max, that differ by 31 nm, yet the opsins differ in only 15 amino acids. Mutagenesis studies have demonstrated that seven of the 15 amino acids determine the spectral shift. We trained neural networks to predict the lambda max of any red/green chimeric protein. Seven mutants were excluded from the original training set. The trained networks were able to predict the lambda max for the excluded mutants. As an additional test, five new chimeric pigments were constructed and lambda max determined. The neural networks correctly predicted the lambda max of all five mutants. The use of neural networks is a novel approach to the problem of wavelength modulation in visual pigments.

Amino Acids↗

Artificial neural network predictions of urinary calculus compositions analyzed with infrared spectroscopy.

Infrared (IR) spectroscopy is used to analyze urinary calculus (renal stone) constituents. However, interpretation of IR spectra for quantifying urinary calculus constituents in mixtures is difficult, requiring expert knowledge by trained technicians. In our laboratory IR spectra of unknown calculi are compared with references spectra in a computerized library search of 235 reference spectra from various mixtures of constituents in different proportions, followed by visual interpretation of band intensities for more precise semiquantitative determination of the composition. To minimize the need for this last step, we tested artificial neural network models for detecting the most frequently occurring compositions of urinary calculi. Using constrained mixture designs, we prepared various samples containing ammonium hydrogen urate, brushite, carbonate apatite, cystine, struvite, uric acid, weddellite, and whewellite for use as a training set. We assayed known artificial mixtures as well as selected patients' samples from which the semiquantitative compositions were determined by computerized library search followed by visual interpretation. Neural network analysis was more accurate than the library search and required less expert knowledge because careful visual inspection of the band intensities could be omitted. We conclude that neural networks are promising tools for routine quantification of urinary calculus compositions and for other related types of analyses in the clinical laboratory.

Evaluation Studies as Topic↗

Inaccessibility in online learning of recurrent neural networks.

We apply nonlinear dynamical system techniques to recurrent neural networks. In particular, we numerically analyze the dynamical system characteristics of the online learning process. By introducing the notion of inaccessibility, we show that the learning process is well characterized by strong nonhyperbolicity and inaccessibility, which is a greater uncertainty than chaotic unpredictability. These results are clearly contrasted with a gradient descent dynamics, or ordinary chaos.

Artificial Intelligence↗

Efficient Partition of Learning Data Sets for Neural Network Training.

This study investigates the emerging possibilities of combining unsupervised and supervised learning in neural network ensembles. Such strategy is used to get an efficient partition of a noisy input data set in order to focus the training of neural networks on the most complex and informative domains of the data set and accelerate the learning phase. The proposed algorithm provides a good prediction accuracy using fewer cases from non-informative domains according to a correlative measure of dependency between cases of the training set. This measure takes into account internal relationships amid analyzed data and can be used to cluster neighbor cases in a multidimensional space and to filter out the outliers. The possible relation of the proposed algorithm to brain processing occurring in the thalamo-cortical pathway is discussed.

Journal Article↗

A neural network of obsessive- compulsive disorder: modelling cognitive disinhibition and neurotransmitter dysfunction.

While significant advances have been made in documenting both neurotransmitter and neuropsychological dysfunction in obsessive-compulsive disorder (OCD), there remains a need for theoretical models to account for their relationship. A neural network model of OCD was developed to provide a rigorous simulation of the relationship between the cognitive disinhibition and serotonin/dopamine dysfunction that characterize this disorder. An architecture-specific recurrent neural network of the Elman type was able to model the cognitive disinhibition that is apparent when OCD patients are compared with other anxiety disorder patients on a modified Stroop (Temporal Stroop) test, with OCD patients showing reduced negative priming (shorter reaction times to previously ignored stimuli). Lesions to either the color gain parameter (reflective of serotonergic dysfunction) or to the context gain parameter (reflective of dopaminergic dysfunction) resulted in decreased semantic negative priming. Neural network modelling provides a surprisingly coherent perspective of the psychobiology of OCD, simulating both reduced cognitive disinhibition as well as neurotransmitter dysfunction.

Cognition Disorders↗