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PubMed2006

Quantitative structure-toxicity relationships (QSTRs): a comparative study of various non linear methods. General regression neural network, radial basis function neural network and support vector machine in predicting toxicity of nitro- and cyano- aromatics to Tetrahymena pyriformis.

Prediction of toxicity of 203 nitro- and cyano-aromatic chemicals to Tetrahymena pyriformis was carried out by radial basis function neural network, general regression neural network and support vector machine, in non-linear response surface methodology. Toxicity was predicted from hydrophobicity parameter (log Kow) and maximum superdelocalizability (Amax). Special attention was drawn to prediction ability and robustness of the models, investigated both in a leave-one-out and 10-fold cross validation (CV) processes. The influence that the corresponding changes in the learning sets during these CV processes could have on a common external test set including 41 compounds was also examined. This allowed us to establish the stability of the models. The non linear results slightly outperform (as expected) multilinear relationships (MLR) and also favourably compete with various other non linear approaches recently proposed by Ren (J. Chem. Inf. Comput. Sci., 43 1679 (2003)).

Animals↗

PubMedDate not supplied

Different parameters support generalization and discrimination learning in Drosophila at the flight simulator.

We have used a genetically tractable model system, the fruit fly Drosophila melanogaster to study the interdependence between sensory processing and associative processing on learning performance. We investigated the influence of variations in the physical and predictive properties of color stimuli in several different operant-conditioning procedures on the subsequent learning performance. These procedures included context and stimulus generalization as well as color, compound, and conditional discrimination (colors and patterns). A surprisingly complex dependence of the learning performance on the colors' physical and predictive properties emerged, which was clarified by taking into account the fly-subjective perception of the color stimuli. Based on estimates of the stimuli's color and brightness values, we propose that the different tasks are supported by different parameters of the color stimuli; generalization occurs only if the chromaticity is sufficiently similar, whereas discrimination learning relies on brightness differences.

Animals↗

PubMed2004

Performance of the Alex AVX-2 MIMD architecture in learning the NetTalk database.

The process of training neural networks on parallel architectures has been used to assess the performance of so many parallel machines. In this paper, we are investigating the implementation of backpropagation (BP) on the Alex AVX-2 coarse-grained MIMD machine. A host-worker parallel implementation is carried out in order to train different networks to learn the NetTalk dictionary. First, a computational model is constructed using a single processor to complete the learning process. Also, a communication model for the host-worker topology is developed in order to compute the communication overhead in the broadcasting/gathering process. Both models are then used to predict the machine performance when p processors are used and a comparison with the actual measured performance of the parallel architecture implementation is carried out. Simulation results show that both models can be used effectively to predict the machine performance for the NetTalk problem. Finally, a comparison between the AVX-2 NetTalk implementation and the performance of other parallel platforms is presented.

Artificial Intelligence↗

PubMed2000

A model of response time effects in symbolic comparison.

A cognitive process model is developed that predicts the 3 major symbolic comparison response time effects (distance, end, and semantic congruity) found in the results of the linear syllogistic reasoning task. The model includes a simple connectionist learning component and dual evidence accumulation decision-making components. It assumes that responses can be based either on information concerning the positional difference between the presented stimulus items or on information concerning the endpoint status of each of these items. The model provides an excellent quantitative account of the mean correct response times obtained from 16 participants who performed paired comparisons of 6 ordered symbolic stimuli (3-letter names).

Cognition↗

PubMed1996

Experimental model and neural network based electrical conductivity estimation in soilless culture system.

The optimum management of nutrient solution in soilless culture needs the accurate control of nutrient solution, especially in recycled soilless culture system. To keep the electrical conductivity (EC) of nutrient solution within the adequate range after application of combined fertilizers, theoretically derived EC prediction methods are required. In this study, the experimental EC prediction equation, an extended form of the Robinson and Stroke's theoretical equation only available for a binary electrolyte, was developed for predicting the EC of the nutrient solution containing many kinds of inorganic compounds. And the multilayer perceptron consisting of three layers with the back propagation learning algorithm was developed for EC prediction. It consists of nine variables in the input layer for the concentrations of seven macro elements, Na+ and Cl, and one variable in the output layer for the EC of nutrient solution. The predicted ECs by experimental model as well as neural networks for the nutrient solution were compared to the measured ones and showed good agreements.

Culture Media↗

PubMed2004

Potential assessment of the "support vector machine" method in forecasting ambient air pollutant trends.

Monitoring and forecasting of air quality parameters are popular and important topics of atmospheric and environmental research today due to the health impact caused by exposing to air pollutants existing in urban air. The accurate models for air pollutant prediction are needed because such models would allow forecasting and diagnosing potential compliance or non-compliance in both short- and long-term aspects. Artificial neural networks (ANN) are regarded as reliable and cost-effective method to achieve such tasks and have produced some promising results to date. Although ANN has addressed more attentions to environmental researchers, its inherent drawbacks, e.g., local minima, over-fitting training, poor generalization performance, determination of the appropriate network architecture, etc., impede the practical application of ANN. Support vector machine (SVM), a novel type of learning machine based on statistical learning theory, can be used for regression and time series prediction and have been reported to perform well by some promising results. The work presented in this paper aims to examine the feasibility of applying SVM to predict air pollutant levels in advancing time series based on the monitored air pollutant database in Hong Kong downtown area. At the same time, the functional characteristics of SVM are investigated in the study. The experimental comparisons between the SVM model and the classical radial basis function (RBF) network demonstrate that the SVM is superior to the conventional RBF network in predicting air quality parameters with different time series and of better generalization performance than the RBF model.

Air Pollutants↗

PubMed1993

A simple (or simplistic?) cognitive model for schizophrenia.

An approach which views schizophrenia as a disturbance of information processing appears promising as a way of linking biological and clinical aspects of the disorder. A review of research in this area led to the suggestion that the basic disturbance in schizophrenia is "a weakening of the influences of stored memories of regularities of previous input on current perception". This formulation leads to the prediction that in certain circumstances, schizophrenics may perform better than normal subjects. Recent studies employing tasks derived from human experimental psychology provide evidence in support of the model. It is argued that the link between information processing disturbances and biological abnormalities may be facilitated by the use of paradigms derived from animal learning theory (latent inhibition and Kamin's blocking effect). On both tasks the pattern of performance of acute schizophrenics is consistent with the cognitive model. The ways in which such an information processing disturbance may lead to schizophrenic symptomatology are outlined, with particular reference to the formation and maintenance of delusional beliefs. The core cognitive abnormality may result from a disturbance in any of the brain structures involved in the prediction of subsequent sensory input. The proposed circuit, which draws heavily on Gray's model, implicates in particular the hippocampus and related areas and is consistent with studies of brain pathology in schizophrenia.

Animals↗

PubMed1994

'Cognitive inhibition' and positive symptomatology in schizotypy.

The negative priming paradigm (Tipper, 1985) was used to investigate the relationship between 'cognitive inhibition' and symptoms of reality distortion in schizotypy, after previous findings that the negative priming effect is reduced in both acute schizophrenics and high schizotypes (Beech, Powell, McWilliam & Claridge, 1989; Beech, Baylis, Smithson & Claridge, 1989). Following Frith's (1979) model, which suggests that the positive symptoms of schizophrenia are due to a failure of the inhibitory processes which normally limit the contents of consciousness, it was predicted that negative priming would be inversely correlated with levels of positive symptomatology, as measured by the CSTQ (Bentall, Claridge & Slade, 1989). The results supported the hypothesis, which confirms the usefulness of a symptom-oriented approach as well as providing some validation for the concept of schizotypy. It was concluded that high schizotypes, similarly to acute schizophrenics, show a reduction in 'cognitive inhibition', as was predicted by Frith's (1979) model.

Adult↗

PubMed1995

Learning and generalization with Minimerror, a temperature-dependent learning algorithm.

We study the numerical performances of Minimerror, a recently introduced learning algorithm for the perceptron that has analytically been shown to be optimal both on learning linearly and nonlinearly separable functions. We present its implementation on learning linearly separable boolean functions. Numerical results are in excellent agreement with the theoretical predictions.

Algorithms↗

PubMed2000

Interactive property attribution in concept combination.

We address the question of how people understand attributive noun-noun compounds. Alignment-and-comparison models suggest that the similarity of the constituent concepts guides interpretation. We propose, as an alternative, an interactive property attribution model wherein the modifier and head concepts have different functions: The head provides relevant dimensions, whereas the modifier provides candidate features for attribution. According to our model, the interaction of dimensions and features, rather than constituent similarity, guides interpretation. In this study, we empirically contrasted the two models by holding constituent similarity of compounds constant while varying the interaction of modifier feature salience and head dimension relevance. Compounds consisting of a head concept with a relevant dimension for attribution and a modifier with a salient property on that dimension were interpreted by means of property attribution. Other compounds with equivalent constituent similarity, but lacking the high salience-relevance interaction, were not interpreted by means of attribution. The interactive property attribution model more accurately predicted interpretation of noun-noun compounds.

Adult↗

PubMed1994

Context effects in visual pattern recognition by pigeons.

In the experiments described in this paper we examined the effects of contextual stimuli on pigeons' recognition of visual patterns. Experiment 1 showed a context-superiority effect. Specifically, two target forms that were identical except for location in the visual field were not discriminated when presented alone, but the compounds formed when each of these targets was placed between a nearby pair of flanking stimuli were readily discriminated. The size of the context-superiority effect decreased with increasing target-flanker separation. In Experiments 2 and 3 the two targets differed in form rather than spatial location and were readily discriminated in the absence of flankers. Under these circumstances, adding an identical pair of flankers to each target resulted in a context-inferiority effect; this is, the two target-plus-flankers compounds were less readily discriminated than the targets alone. The size of the context-inferiority effect decreased with increasing target-flanker separation. The observed effects of context are predictable from the Heinemann-Chase (1990) model of pattern recognition.

Animals↗

PubMed1992

Perceived control as a predictor of distress in children undergoing invasive medical procedures.

Children's anticipatory attributional assessment of the source(s) of perceived control is hypothesized to play an important role in the etiology of distress in children undergoing invasive medical procedures. Four perceived control types, based on learned helplessness theory, are specified by a conceptual model that guides this research: Mutual, Powerful Other, Personal, and Unknown. Among 73 children between the ages of 4 and 18 having their blood drawn, it was predicted that children with an attributional analysis of unknown perceived source of control prior to the impending medical procedures would experience a heightened level of anxiety (procedure-related distress). As predicted, children with an anticipatory attributional assessment of unknown perceived control interfered with or extended the medical procedure significantly more (41%) than children who could attribute some perceived source of control (13%). They were also rated by themselves, the parents, and a trained clinical observer as manifesting significantly greater (p < .05) anticipatory procedural distress using both cognitive (subjective) and behavioral (objective) assessment perspectives. These findings were independent of children's age. This paper supports the need for additional theory-driven research and the importance of investigating the role of attributional variables in the etiology of procedure-related distress in children.

Adaptation, Psychological↗

PubMed2002

Artificial intelligence techniques for bioinformatics.

This review provides an overview of the ways in which techniques from artificial intelligence (AI) can be usefully employed in bioinformatics, both for modelling biological data and for making new discoveries. The paper covers three techniques: symbolic machine learning approaches (nearest neighbour and identification tree techniques), artificial neural networks and genetic algorithms. Each technique is introduced and supported with examples taken from the bioinformatics literature. These examples include folding prediction, viral protease cleavage prediction, classification, multiple sequence alignment and microarray gene expression analysis.

Algorithms↗

PubMedDate not supplied

Implementing a predictive modeling program, part 1.

Effective predictive modeling is a powerful, statistically valid tool, which identifies individuals at increased future risk of an untoward health event. This article looks at one company's experience in developing a successful program; the issues related to an effective implementation strategy; and lessons learned during the process. The goal is to implement the right intervention, at the right time, for the right patient.

Algorithms↗

PubMed1995

A model of the neural basis of the rat's sense of direction.

In the last decade the outlines of the neural structures subserving the sense of direction have begun to emerge. Several investigations have shed light on the effects of vestibular input and visual input on the head direction representation. In this paper, a model is formulated of the neural mechanisms underlying the head direction system. The model is built out of simple ingredients, depending on nothing more complicated than connectional specificity, attractor dynamics, Hebbian learning, and sigmoidal nonlinearities, but it behaves in a sophisticated way and is consistent with most of the observed properties of real head direction cells. In addition it makes a number of predictions that ought to be testable by reasonably straightforward experiments.

Animals↗

PubMed1997

Competitive assessment of protein fold recognition and alignment accuracy.

The predictions made for fold recognition and modeling accuracy at the 1996 Critical Assessment of Structure Prediction meeting (CASP2) were assessed to discover which groups did best. With 32 groups making a total of 369 predictions, it was necessary to use simple criteria for distinguishing between the entries. By focusing on the predictors' ability to use the sequence of the unknown target structure to recognize the target fold from a database of known folds and also on the quality of the model judged by the accuracy of the predicted alignment, it is easy to determine the best predictions for a given target. Assessing overall performance of the predictors on all the targets is much more difficult and use was made of weighted averages of fold recognition and alignment accuracy with and without normalization for target difficulty. By plotting these results in two dimensions the winning groups stand out, allowing readers to focus their attention on the most promising methods. When the present results are compared with the results of the earlier CASP1 meeting, held in 1994, it is clear that threading predictions have progressed dramatically. For this assessor, the strongest lesson learned is that subjectivity is pervasive and affects us all. It is abundantly clear that the blind predictions made at CASP are essential if progress is to be made in predicting protein structure.

Models, Molecular↗

PubMed2020

Exploration of predictive and prognostic alternative splicing signatures in lung adenocarcinoma using machine learning methods.

BACKGROUND: Alternative splicing (AS) plays critical roles in generating protein diversity and complexity. Dysregulation of AS underlies the initiation and progression of tumors. Machine learning approaches have emerged as efficient tools to identify promising biomarkers. It is meaningful to explore pivotal AS events (ASEs) to deepen understanding and improve prognostic assessments of lung adenocarcinoma (LUAD) via machine learning algorithms. METHOD: RNA sequencing data and AS data were extracted from The Cancer Genome Atlas (TCGA) database and TCGA SpliceSeq database. Using several machine learning methods, we identified 24 pairs of LUAD-related ASEs implicated in splicing switches and a random forest-based classifiers for identifying lymph node metastasis (LNM) consisting of 12 ASEs. Furthermore, we identified key prognosis-related ASEs and established a 16-ASE-based prognostic model to predict overall survival for LUAD patients using Cox regression model, random survival forest analysis, and forward selection model. Bioinformatics analyses were also applied to identify underlying mechanisms and associated upstream splicing factors (SFs). RESULTS: Each pair of ASEs was spliced from the same parent gene, and exhibited perfect inverse intrapair correlation (correlation coefficient = - 1). The 12-ASE-based classifier showed robust ability to evaluate LNM status of LUAD patients with the area under the receiver operating characteristic (ROC) curve (AUC) more than 0.7 in fivefold cross-validation. The prognostic model performed well at 1, 3, 5, and 10 years in both the training cohort and internal test cohort. Univariate and multivariate Cox regression indicated the prognostic model could be used as an independent prognostic factor for patients with LUAD. Further analysis revealed correlations between the prognostic model and American Joint Committee on Cancer stage, T stage, N stage, and living status. The splicing network constructed of survival-related SFs and ASEs depicts regulatory relationships between them. CONCLUSION: In summary, our study provides insight into LUAD researches and managements based on these AS biomarkers.

Adenocarcinoma of Lung↗

PubMedDate not supplied

Modeling and reinforcement of the sick role during childhood predicts adult illness behavior.

Previous studies suggest that the ways in which parents respond to children's health complaints (reinforcement) and the ways in which they cope with their own illnesses (modeling) influence the frequency of symptoms, disability days, and health care visits made by these children when they grow up. However, previous studies have not controlled for the mediating influence of stress, neuroticism, and physical examination findings. This study investigated the influence of childhood social learning on adult illness behavior in 383 women aged 20 to 40 years. Illness behavior was measured prospectively for 12 months by the frequency of symptoms, disability days, and physician visits for menstrual, bowel, and cold (upper respiratory) symptoms. Childhood reinforcement and modeling was measured retrospectively by validated questionnaires. Other independent variables were stress, neuroticism, and selected demographic variables. Multiple regression analysis was used to assess the relative contribution of each independent variable to each category of illness behavior. The principal findings were as follows. First, childhood reinforcement of menstrual illness behavior significantly predicted adult menstrual symptoms and disability days, and childhood reinforcement of cold illness behavior significantly predicted adult cold symptoms and disability days. These effects were independent of stress and neuroticism. Second, childhood reinforcement scales were useful to predict which functional disorders (dysmenorrhea or irritable bowel syndrome) these subjects had even after we controlled for stress and neuroticism.

Adaptation, Psychological↗