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PubMed2002

Ecologic niche modeling and differentiation of populations of Triatoma brasiliensis neiva, 1911, the most important Chagas' disease vector in northeastern Brazil (hemiptera, reduviidae, triatominae).

Ecologic niche modeling has allowed numerous advances in understanding the geographic ecology of species, including distributional predictions, distributional change and invasion, and assessment of ecologic differences. We used this tool to characterize ecologic differentiation of Triatoma brasiliensis populations, the most important Chagas' disease vector in northeastern Brazil. The species' ecologic niche was modeled based on data from the Fundação Nacional de Saúde of Brazil (1997-1999) with the Genetic Algorithm for Rule-Set Prediction (GARP). This method involves a machine-learning approach to detecting associations between occurrence points and ecologic characteristics of regions. Four independent "ecologic niche models" were developed and used to test for ecologic differences among T. brasiliensis populations. These models confirmed four ecologically distinct and differentiated populations, and allowed characterization of dimensions of niche differentiation. Patterns of ecologic similarity matched patterns of molecular differentiation, suggesting that T. brasiliensis is a complex of distinct populations at various points in the process of speciation.

Animals↗

PubMed2006

Model-based drug development: the road to quantitative pharmacology.

High development costs and low success rates in bringing new medicines to the market demand more efficient and effective approaches. Identified by the FDA as a valuable prognostic tool for fulfilling such a demand, model-based drug development is a mathematical and statistical approach that constructs, validates, and utilizes disease models, drug exposure-response models, and pharmacometric models to facilitate drug development. Quantitative pharmacology is a discipline that learns and confirms the key characteristics of new molecular entities in a quantitative manner, with goal of providing explicit, reproducible, and predictive evidence for optimizing drug development plans and enabling critical decision making. Model-based drug development serves as an integral part of quantitative pharmacology. This work reviews the general concept, basic elements, and evolving role of model-based drug development in quantitative pharmacology. Two case studies are presented to illustrate how the model-based drug development approach can facilitate knowledge management and decision making during drug development. The case studies also highlight the organizational learning that comes through implementation of quantitative pharmacology as a discipline. Finally, the prospects of quantitative pharmacology as an emerging discipline are discussed. Advances in this discipline will require continued collaboration between academia, industry and regulatory agencies.

Adult↗

PubMedDate not supplied

Cross-cultural similarities in the predictors of reading acquisition.

Measures of Chinese character/English word recognition, phonological awareness, speeded naming, visual-spatial skill, and processing speed were administered to 190 kindergarten students in Hong Kong and 128 kindergarten and grade 1 students in the United States. Across groups, the strongest predictor of reading itself was phonological awareness; visual processing did not predict reading. For both groups, speed of processing strongly predicted speeded naming, visual processing, and phonological awareness. Despite diversities of culture, language, and orthography to be learned, models of early reading development were remarkably similar across cultures and first and second language orthographies.

Child↗

PubMedDate not supplied

A model of evolution and learning.

We study a model of evolving populations of self-learning agents and analyze the interaction between learning and evolution. We consider an agent-broker that predicts stock price changes and uses its predictions for selecting actions. Each agent is equipped with a neural network adaptive critic design for behavioral adaptation. We discuss three cases in which either evolution or learning, or both, are active in our model. We show that the Baldwin effect can be observed in our model, viz. originally acquired adaptive policy of best agent-brokers becomes inherited over the course of the evolution. We also compare the behavioral tactics of our agents to the searching behavior of simple animals.

Algorithms↗

PubMedDate not supplied

Neurodevelopmental characteristics of children with learning impairments classified according to the double-deficit hypothesis.

The double-deficit model has been examined primarily in relation to reading. We investigated whether children classified according to the double-deficit model would exhibit differences in other neuropsychological domains. Children referred for learning problems (N = 188), ages 7 to 11, were classified by double-deficit subtype. Only three of the four groups predicted by the model could be identified. There were no group differences in IQ or attention problems. The three groups showed different neuropsychological profiles, involving functional domains other than reading and language. Differences also emerged in nonverbal low-level information processing. The double-deficit group was generally most severely affected. The double-deficit groupings identify children with different neuropsychological profiles and variation in the efficiency of basic online information processing, extending beyond the oral and written language domain.

Anomia↗

PubMed2005

Suboptimal tradeoffs in information seeking.

Explicit information-seeking actions are needed to evaluate alternative actions in problem-solving tasks. Information-seeking costs are often traded off against the utility of information. We present three experiments that show how subjects adapt to the cost and information structures of environments in a map-navigation task. We found that subjects often stabilize at suboptimal levels of performance. A Bayesian satisficing model (BSM) is proposed and implemented in the ACT-R architecture to predict information-seeking behavior. The BSM uses a local decision rule and a global Bayesian learning mechanism to decide when to stop seeking information. The model matched the human data well, suggesting that adaptation to cost and information structures can be achieved by a simple local decision rule. The local decision rule, however, often limits exploration of the environment and leads to suboptimal performance. We propose that suboptimal performance is an emergent property of the dynamic interactions between cognition and the environment.

Bayes Theorem↗

PubMed1992

Cross-validation performance of mortality prediction models.

Mortality prediction models hold substantial promise as tools for patient management, quality assessment, and, perhaps, health care resource allocation planning. Yet relatively little is known about the predictive validity of these models. We report here a comparison of the cross-validation performance of seven statistical models of patient mortality: (1) ordinary-least-squares (OLS) regression predicting 0/1 death status six months after admission; (2) logistic regression; (3) Cox regression; (4-6) three unit-weight models derived from the logistic regression, and (7) a recursive partitioning classification technique (CART). We calculated the following performance statistics for each model in both a learning and test sample of patients, all of whom were drawn from a nationally representative sample of 2558 Medicare patients with acute myocardial infarction: overall accuracy in predicting six-month mortality, sensitivity and specificity rates, positive and negative predictive values, and per cent improvement in accuracy rates and error rates over model-free predictions (i.e., predictions that make no use of available independent variables). We developed ROC curves based on logistic regression, the best unit-weight model, the single best predictor variable, and a series of CART models generated by varying the misclassification cost specifications. In our sample, the models reduced model-free error rates at the patient level by 8-22 per cent in the test sample. We found that the performance of the logistic regression models was marginally superior to that of other models. The areas under the ROC curves for the best models ranged from 0.61 to 0.63. Overall predictive accuracy for the best models may be adequate to support activities such as quality assessment that involve aggregating over large groups of patients, but the extent to which these models may be appropriately applied to patient-level resource allocation planning is less clear.

Discriminant Analysis↗

PubMed2005

Prediction of the tissue/blood partition coefficients of organic compounds based on the molecular structure using least-squares support vector machines.

The accurate nonlinear model for predicting the tissue/blood partition coefficients (PC) of organic compounds in different tissues was firstly developed based on least-squares support vector machines (LS-SVM), as a novel machine learning technique, by using the compounds' molecular descriptors calculated from the structure alone and the composition features of tissues. The heuristic method (HM) was used to select the appropriate molecular descriptors and build the linear model. The prediction result of the LS-SVM model is much better than that obtained by HM method and the prediction values of tissue/blood partition coefficients based on the LS-SVM model are in good agreement with the experimental values, which proved that nonlinear model can simulate the relationship between the structural descriptors, the tissue composition and the tissue/blood partition coefficients more accurately as well as LS-SVM was a powerful and promising tool in the prediction of the tissue/blood partition behaviour of compounds. Furthermore, this paper provided a new and effective method for predicting the tissue/blood partition behaviour of the compounds in the different tissues from their structures and gave some insight into structural features related to the partition process of the organic compounds in different tissues.

Least-Squares Analysis↗

PubMed1997

The early use of alcohol and tobacco: its relation to children's competence and parents' behavior.

OBJECTIVES: Use of tobacco and alcohol during childhood predicts heavy use of these substances and use of illicit drugs during adolescence. This study aims to identify developmental correlates of tobacco and alcohol use among elementary-school children. METHODS: Cross-sectional surveys were used to measure tobacco and alcohol use, multiple indicators of child competence, parenting behaviors, and parental modeling of tobacco and alcohol use in a sample of 1470 third- and fifth-grade children. Both self-report and teacher-rated assessments were obtained, which allowed collateral testing of study hypotheses. RESULTS: Children's tobacco and alcohol use was strongly related to low scores on several measures of child competence, both self-reported and teacher rated. Children's tobacco and alcohol use was also associated with less effective parenting behaviors and with parental use of tobacco and alcohol. CONCLUSIONS: Children's early experience with tobacco and alcohol is associated with weak competence development and exposure to socialization factors that promote risk taking. Interventions to prevent early use of tobacco and alcohol are needed.

Adolescent↗

PubMed2004

Wiener filter estimation of transfer functions.

The use of a Wiener filter estimate for the linear transfer function can significantly improve the description of behavioral dynamics. This report presents a two-pass, Monte-Carlo-based algorithm that is well suited to repeated-trials local average measurements. The Wiener filter transfer functions strongly suppress noise artifacts as well as allow reliable transfer function determination under a much wider class of reinforcement schedules. Implications of expanding the possible form of experimental design are considered along with improvements in the fidelity of resulting predictions.

Animals↗

PubMed2005

Simulations of a modified SOP model applied to retrospective revaluation of human causal learning.

Dickinson and Burke (1996) proposed a modified version of Wagner's (1981) SOP associative theory to explain retrospective revaluation of human causal judgments. In this modified SOP (MSOP), excitatory learning occurs when cue and outcome representations are either both directly activated or both associatively activated. By contrast, inhibitory learning occurs when one representation is directly activated while the other is associatively activated. Finite node simulations of MSOP yielded simple acquisition, overshadowing, blocking, and inhibitory learning under forward contingencies. Importantly, retrospective revaluation was predicted in the form of unovershadowing and backward inhibitory learning. However, MSOP did not yield backward blocking. These predictions are evaluated against the relevant empirical evidence and contrasted with the predictions of other associative theories that have been applied to retrospective revaluation of human causal and predictive learning.

Forecasting↗

PubMed2001

Genetic adaptive neural network to predict biochemical failure after radical prostatectomy: a multi-institutional study.

BACKGROUND AND PURPOSE: Despite many new procedures, radical prostatectomy remains one of the commonest methods of treating clinically localized prostate cancer. Both from the physician's and the patient's point of view, it is important to have objective estimation of the likelihood of recurrence, which forms the foundation for treatment selection for an individual patient. Currently, it is difficult to predict the probability of biochemical recurrence (rising serum prostate specific antigen [PSA] concentration) in an individual patient, and approximately 30% of the patients do experience recurrence. Tools predicting the recurrence will be of immense practical utility in the treatment selection and planning follow up. We have utilized preoperative parameters through a computer based genetic adaptive neural network model to predict recurrence in such patients, which can help primary care physicians and urologists in making management recommendations. PATIENTS AND METHODS: Fourteen hundred patients who underwent radical prostatectomy at participating institutions form the subjects of this study. Demographic data such as age, race, preoperative PSA, systemic biopsy based staging and Gleason scores were used to construct a neural network model. This model simulated the functioning of a trained human mind and learned from the database. Once trained, it was used to predict the outcomes in new patients. RESULTS: The patients in this comprehensive database were representative of the average prostate cancer patients as seen in USA. Their mean age was 68.4 years, the mean PSA concentration before surgery was 11.6 ng/mL, and 67% patients had a Gleason sum of 5 to 7. The mean length of follow-up was 41.5 months. Eighty percent of the cancers were clinical stage T2 and 5% T3. In our series, 64% of patients had pathologically organ-confined cancer, 33% positive margins, and 14% had seminal vesicle invasion. Lymph node positive patients were not included in this series. Progression as judged by serum PSA was noted in 30.6%. With entry of a few routinely used parameters, the model could correctly predict recurrence in 76% of the patients in the validation set. The area under the curve was 0.831. The sensitivity was 85%, the specificity 74%, the positive predictive value 77%, and the negative predictive value of 83%. CONCLUSION: It was possible to predict PSA recurrence with a high accuracy (76%). Physicians desiring objective treatment counseling can use this model, and significant cost savings are anticipated because of appropriate treatment selection and patient-specific follow-up protocols. This technology can be extended to other treatments such as watchful waiting, external-beam radiation, and brachytherapy.

Aged↗

PubMed2005

Imitation dynamics predict vaccinating behaviour.

There exists an interplay between vaccine coverage, disease prevalence and the vaccinating behaviour of individuals. Moreover, because of herd immunity, there is also a strategic interaction between individuals when they are deciding whether or not to vaccinate, because the probability that an individual becomes infected depends upon how many other individuals are vaccinated. To understand this potentially complex interplay, a game dynamic model is developed in which individuals adopt strategies according to an imitation dynamic (a learning process), and base vaccination decisions on disease prevalence and perceived risks of vaccines and disease. The model predicts that oscillations in vaccine uptake are more likely in populations where individuals imitate others more readily or where vaccinating behaviour is more sensitive to changes in disease prevalence. Oscillations are also more likely when the perceived risk of vaccines is high. The model reproduces salient features of the time evolution of vaccine uptake and disease prevalence during the whole-cell pertussis vaccine scare in England and Wales during the 1970s. This suggests that using game theoretical models to predict, and even manage, the population dynamics of vaccinating behaviour may be feasible.

Computer Simulation↗

PubMed2025

Carafe enables high quality in silico spectral library generation for data-independent acquisition proteomics.

Data-independent acquisition (DIA)-based mass spectrometry is becoming an increasingly popular mass spectrometry acquisition strategy for carrying out quantitative proteomics experiments. Most of the popular DIA search engines make use of in silico generated spectral libraries. However, the generation of high-quality spectral libraries for DIA data analysis remains a challenge, particularly because most such libraries are generated directly from data-dependent acquisition (DDA) data or are from in silico prediction using models trained on DDA data. In this study, we developed Carafe, a tool that generates high-quality experiment-specific in silico spectral libraries by training deep learning models directly on DIA data. We demonstrate the performance of Carafe on a wide range of DIA datasets, where we observe improved fragment ion intensity prediction and peptide detection relative to existing pretrained DDA models. To make Carafe more accessible to the community, we have integrated Carafe into the widely used Skyline tool.

Journal Article↗

PubMed2006

Numerical investigation on the flow characteristics and aerodynamic force of the upper airway of patient with obstructive sleep apnea using computational fluid dynamics.

Developing a mathematical model to predict the abnormal flow characteristics that are produced by obstructive sleep apnea is an important step in learning the pathophysiology of the obstructive sleep apnea (OSA) disease. The present study provides detailed calculations of flow in the pharyngeal airway of a patient with obstructive sleep apnea. To achieve this goal, a computational fluid dynamics model was constructed using raw data from three-dimensional computed tomogram (CT) images of an OSA patient. To reproduce the important transition from laminar to turbulent flow in the pharyngeal airway, the low Reynolds number k-epsilon model was adopted and successfully validated using previous open literature. The results show that the flow in the pharyngeal airway of patients with OSA comprises a turbulent jet formed by area restriction at the velopharynx. This turbulent jet causes higher shear and pressure forces in the vicinity of the velopharynx. From the results, It may be deduced that the most collapsible area in the pharyngeal airway of OSA patients is the velopharynx where minimum intraluminal pressure and maximum aerodynamic force lie.

Adult↗

PubMed2005

Cue interaction effects in causal judgement: an interpretation in terms of the evidential evaluation model.

In judging the extent to which a cue causes an outcome, judgement can be affected by information about other cues that are correlated with the one being judged. These cue interaction effects have usually been interpreted in terms of associative learning processes. I propose that a different model of causal judgement, the evidential evaluation model, offers a viable alternative interpretation of cue interaction phenomena. Under the evidential evaluation model, instances of contingency information are interpreted as evidence, which is confirmatory, disconfirmatory, or irrelevant for the cue being judged. When two cues co-occur in a set of instances the evidential value of the instances for one of them is determined by three factors: the proportion of confirming instances in the set; disambiguation value, which concerns the relation between the set of information and prior beliefs about the co-occurring cue; and confirmation value, which concerns the relation between the set of information and prior beliefs about the cue being judged. Any previous judgement of the cue is then modified in the light of these. It is shown that this model can account for all the cue interaction phenomena that have been investigated in studies of human causal judgement. The model also generates novel predictions, and the results of three experiments give support to these predictions. It is also shown that several other current models of causal judgement fail to predict a key result from Experiment 3.

Analysis of Variance↗

PubMed1994

Spatial planning deficits in limb apraxia.

Geschwind (1975) proposed a disconnection model in which an apraxic subject is unable to carry out movements to command because the left hemisphere that comprehended the verbal command is disconnected from the right premotor and motor areas which controls the left hand. An alternate model, however, proposes that apraxia results from destruction of spatiotemporal representations of learned movement stored in the left hemisphere (Heilman, 1979). The disconnection hypothesis would predict that apraxic subjects should be able to correctly imitate gestures and correctly use actual tools since these tasks do not require language. The movement representation model predicts that imitation and actual tool use would also be impaired. Motion analyses were performed on the trajectories of repetitive 'slicing' gestures made in a series of conditions in which contextual cues were introduced in a graded fashion. Four cue conditions were presented: no cues (verbal command), object present, tool present and both object and tool present. Positions of the hand, wrist, elbow, and shoulder were digitized from neighbouring views, reconstructed in three dimensions and analysed with respect to specific spatiotemporal features of the trajectories. Three subjects with limb apraxia, who had lesions that included left parietal cortex, and four neurologically intact subjects participated. The apraxic subjects showed disturbances in planning the movement of the hand in space across the cue conditions. For example, they showed deficits in the plane of motion, the shape of the trajectory and in the coupling of hand speed and trajectory shape even when given full contextual cues. These data support the hypothesis that apraxia can result from the destruction of spatiotemporal representations of learned movement, rather than from a disconnection between the receptive language areas in the left hemisphere and the contralateral motor cortices.

Aged↗

PubMed2026

RCoxNet: A Deep Learning Framework Integrating Random Walk with Restart, Mutation, and Clinical Data for Cancer Survival Prediction.

Accurate survival prediction in cancer remains challenging due to the sparsity of somatic mutation profiles and the failure of existing models to capture higher-order gene-gene dependencies. Network diffusion methods such as Random Walk with Restart (RWR) can propagate mutation signals across protein-protein interaction (PPI) networks to address sparsity, yet their integration within a deep learning Cox survival framework has not been comprehensively benchmarked across multiple cancer cohorts. We present RCoxNet, a deep learning framework that maps somatic mutation profiles onto a ConsensusPathDB-derived PPI network via RWR, selects prognostic genes by log-rank filtering, and processes network-informed mutation scores through three fully connected hidden layers feeding into a Cox proportional hazards output. RCoxNet was evaluated on The Cancer Genome Atlas (TCGA) cohorts for four cancer types (breast invasive carcinoma [BRCA], lung adenocarcinoma [LUNG], glioblastoma multiforme [GBM], and ovarian serous cystadenocarcinoma [OV]) using 20 independent random splits. The model achieved mean C-index values of 0.807 ± 0.044 (BRCA), 0.750 ± 0.039 (LUNG), 0.704 ± 0.041 (GBM), and 0.668 ± 0.036 (OV), consistently outperforming DeepSurv, Cox-nnet, SurvivalNet, Cox Elastic-Net (Cox-EN), and DeepHit, with statistically significant gains over Cox-EN, Cox-nnet, SurvivalNet, and DeepHit across the majority of cohorts. RCoxNet demonstrates that embedding sparse mutation profiles into a PPI network context substantially improves cancer survival prediction and yields biologically interpretable prognostic features relevant to precision oncology.

cancer survival prediction↗