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Results for “Model performance”

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At least 667 records · Page 37Linked to original sources

PubMed2004

Effects of meteorological factors on epidemic malaria in Ethiopia: a statistical modelling approach based on theoretical reasoning.

This study was conducted to quantify the association between meteorological variables and incidence of Plasmodium falciparum in areas with unstable malaria transmission in Ethiopia. We used morbidity data pertaining to microscopically confirmed cases reported from 35 sites throughout Ethiopia over a period of approximately 6-7 years. A model was developed reflecting biological relationships between meteorological and morbidity variables. A model that included rainfall 2 and 3 months earlier, mean minimum temperature of the previous month and P. falciparum case incidence during the previous month was fitted to morbidity data from the various areas. The model produced similar percentages of over-estimation (19.7% of predictions exceeded twice the observed values) and under-estimation (18.6%, were less than half the observed values). Inclusion of maximum temperature did not improve the model. The model performed better in areas with relatively high or low incidence (>85% of the total variance explained) than those with moderate incidence (55-85% of the total variance explained). The study indicated that a dynamic immunity mechanism is needed in a prediction model. The potential usefulness and drawbacks of the modelling approach in studying the weather-malaria relationship are discussed, including a need for mechanisms that can adequately handle temporal variations in immunity to malaria.

Altitude↗

PubMed2004

Numerical simulation of light backscattering by spheres with off-center inclusion. Application to the lidar case.

A Mie backscattering model for spherical particles with off-center inclusion has been developed and tested. The program is capable of dealing with size parameter values up to approximately 1000, thus allowing one to simulate the optical behavior of a large variety of atmospheric aerosols, as well as cloud and precipitation particles. On the basis of this model, we simulated the optical properties of polydisperse composite atmospheric particles as observed by ground-based and airborne lidar systems. We have characterized optical properties in terms of host and inclusion radii, considering water particles with different composition inclusions. The performed modeling provides some insight into the so-called lidar bright- and dark-band phenomenon.

Journal Article↗

PubMed1992

Human T-lymphotropic virus type I/II. Status of enzyme immunoassay and western blot testing in the United States in 1989 and 1990.

In three performance evaluation surveys, panels that consisted of human T-lymphotropic virus type I or type II (HTLV-I/II) antibody-positive and -negative plasma samples were mailed to laboratories that voluntarily participated in the Centers for Disease Control Model Performance Evaluation Program. Donor samples were identical among surveys. In each survey, more than 98% of the laboratories reported enzyme immunoassay (EIA) test results; about 11% also reported results of Western blot (WB) testing. Variation in analytic sensitivity (96.7% to 99.4%) and specificity (98.3% to 99.5%) of EIA tests was noted in the three surveys. For WB testing, no nonreactive interpretations were reported for HTLV-I/II antibody-positive samples in any survey; however, indeterminate interpretations were reported for 35.2% to 40.7% of the WB tests that were performed on HTLV-I/II antibody-positive samples. More than 95% of these indeterminate WB test interpretations were reported for HTLV-II antibody-positive samples. Although HTLV-I/II antibody tests are generally sensitive and specific, their accuracy could be further improved by increasing the specificity of EIA tests and the sensitivity of WB tests.

Blotting, Western↗

PubMedDate not supplied

What visual information is used for navigation around obstacles in a cluttered environment?

The goal of this study was to determine what visual information is used to navigate around barriers in a cluttered terrain. Twelve traffic pylons were arranged randomly in a 4.55 x 3.15 m travel area: there were 20 different arrangements. For each arrangement, individuals (N = 6) were positioned in 1 of 3 locations on the outside border with their eyes closed: on verbal command they were instructed to open their eyes and quickly go to 1 of 2 specified goals (2 vertical posts defining a door) located on one edge of the travel area. The movement of the body was tracked using the OPTOTRAK system, with the IREDS placed on a collar worn by the subjects. Experimental data of travel path chosen were compared with those predicted by models that incorporated different types of visual information to control path trajectory. The 6 models basically use 2 different strategies for route selection: reactive control based on visual input about the obstacle encountered in the line-of-sight travel path (Model # 1) and path planning based on different visual information (Model # 2, 3, 4, 5, and 6). The models that involve path planning are grouped into 2 categories: models 2, 3, 4, and 5 need detailed geometrical configuration of the obstacles to plan a route while model 6 plans a route based on identifying and avoiding a cluster of obstacles in the travel path. Two measures were used to compare model performance with the actual travel path: the difference in area between predicted and actual travel path and the number of trials that accurately predicted the number of turns during travel. The results suggest that route selection is not based on reactive control, but does involve path planning. The model that best predicts the travel paths taken by the individuals uses visual information about cluster of obstacles and identification of safe corridors to plan a route.

Adolescent↗

PubMed2006

Structural equation models of memory performance across noise conditions and age groups.

Competing models of declarative memory were tested with structural equation models to analyze whether a second-order latent variable structure for episodic and semantic memory was invariant across age groups and across noise exposure conditions. Data were taken from three previous experimental noise studies that were performed with the same design, procedure, and dependent measures, and with participants from four age groups (13-14, 18-20, 35-45, and 55-65 years). Two noise conditions, road traffic noise and meaningful irrelevant speech, were compared to a quiet control group. The structural models put to the test were taken from Nyberg et al. (2003), which employed several memory tests that were the same as ours and studied age-groups that partly overlapped with our groups. In addition we also varied noise exposure conditions. Our analyses replicated and supported the second-order semantic-episodic memory models in Nyberg et al. (2003). The latent variable structures were invariant across age groups, with the exception of our youngest group, which by itself showed a less clear latent structure. The obtained structures were also invariant across noise exposure conditions. We also noted that our text memory items, which did not have a counterpart in the study by Nyberg et al. (2003), tend to form a separate latent variable loading on episodic memory.

Adolescent↗

PubMed2001

Psychological factors in sport performance: the Mental Health Model revisited.

The Mental Health Model (MHM) of sport performance purports that an inverse relationship exists between psychopathology and sport performance. The model postulates that as an athlete's mental health either worsens or improves performance should fall or rise accordingly, and there is now considerable support for this view. Studies have shown that between 70 and 85% of successful and unsuccessful athletes can be identified using general psychological measures of personality structure and mood state, a level superior to chance but insufficient for the purpose of selecting athletes. Longitudinal MHM research indicates that the mood state responses of athletes exhibit a dose-response relationship with their training load, a finding that has shown potential for reducing the incidence of the staleness syndrome in athletes who undergo intensive physical training. The MHM also has implications for the general care of athletes as support services have traditionally been limited to preventing or treating physical problems. Despite its simple premise and empirical support, the MHM has often been mischaracterised in the sport psychology literature and recently some authors have questioned its validity. This overview will summarise MHM research, including the more recent work involving the model's dynamic features in an effort to resolve disputes surrounding the model.

Affect↗

PubMed2026

PepGen: conditional generation of peptides for MHC binding.

MOTIVATION: Peptide-MHC II binding drives adaptive immunity, yet discovery of novel binder peptides remains challenging due to open binding grooves of MHC-II that accommodate variable-length peptides. While discriminative models perform well, they are unfeasible for generation via enumeration due to vast peptide space (2013≈8×1016 for peptides of length 13 amino acids). Generative AI approaches could accelerate binder design to enable vaccines targeted to particular MHC-II alleles or optimize other peptide chemical properties. RESULTS: We introduce PepGen, the first protein language model for MHC II peptide generation building on Generalized Language Modeling. PepGen conditions on alleles, arbitrary partial peptides including putative TCR-interacting motifs, and continuous binding affinity. Across multiple benchmarks including infilling and de novo generation, PepGen outperformed frequency sampling, Gibbs clustering, and autoregressive baselines. Adjusted log-probabilities enable good classification performance. Experimental validation confirmed that the SARS-CoV-2 peptide TEGALNTPKDHIGTR binding the HLA-DQA101:03-DQB106:03 allele can be redesigned to bind the HLA-DQA101:02-DQB105:02 allele. PepGen generated three putative TCR-motif-preserving binders gaining up to 70% of original MFI. Overall, PepGen provides scalable, motif-constrained MHC II peptide redesign and de novo generation, validated through thorough benchmarks and functional assays. AVAILABILITY AND IMPLEMENTATION: Code and Data are available at https://github.com/DaniTheOrange/PepGen.

Peptides↗

PubMed2005

Scoring functions for transcription factor binding site prediction.

BACKGROUND: Transcription factor binding site (TFBS) prediction is a difficult problem, which requires a good scoring function to discriminate between real binding sites and background noise. Many scoring functions have been proposed in the literature, but it is difficult to assess their relative performance, because they are implemented in different software tools using different search methods and different TFBS representations. RESULTS: Here we compare how several scoring functions perform on both real and semi-simulated data sets in a common test environment. We have also developed two new scoring functions and included them in the comparison. The data sets are from the yeast (S. cerevisiae) genome. Our new scoring function LLBG (least likely under the background model) performs best in this study. It achieves the best average rank for the correct motifs. Scoring functions based on positional bias performed quite poorly in this study. CONCLUSION: LLBG may provide an interesting alternative to current scoring functions for TFBS prediction.

Algorithms↗

PubMed1997

Linkage analysis of complex disorders with multiple phenotypic categories: simulation studies and application to bipolar disorder data.

The problem of linkage analysis of disorders with multiple possible phenotypes (diagnostic spectrum) is considered. A modification is proposed to Ott's [1994] method of down-weighting the contribution of broader diagnoses by reducing penetrance ratios for affected cases. A "robust weighting" strategy considers only the robustness of a set of ratios across a range of true genetic models. Practical models for lod-score analysis will typically employ a high penetrance ratio (> 10) for "core" cases, and ratios between 2 and 5 for broader cases. Results suggest that an additive parametric analysis correlates highly with dominant, recessive and nonparametric linkage (NPL) analyses. A weighted, additive model is then applied to a modified NIMH bipolar chromosome 18 data set (Genetic Analysis Workshop 10) and compared with NPL analyses under narrow and broad diagnostic models. The weighted model performed well. The introduction of similar weights into nonparametric analyses may prove more useful.

Bipolar Disorder↗

PubMed1996

Development and validation of a Bayesian model for perioperative cardiac risk assessment in a cohort of 1,081 vascular surgical candidates.

OBJECTIVES: This study sought to develop and validate a Bayesian risk prediction model for vascular surgery candidates. BACKGROUND: Patients who require surgical treatment of peripheral vascular disease are at increased risk of perioperative cardiac morbidity and mortality. Existing prediction models tend to underestimate risk in vascular surgery candidates. METHODS: The cohort comprised 1,081 consecutive vascular surgery candidates at five medical centers. Of these, 567 patients from two centers ("training" set) were used to develop the model, and 514 patients from three centers were used to validate it ("validation" set). Risk scores were developed using logistic regression for clinical variables: advanced age (>70 years), angina, history of myocardial infarction, diabetes mellitus, history of congestive heart failure and prior coronary revascularization. A second model was developed from dipyridamole-thallium predictors of myocardial infarction (i.e., fixed and reversible myocardial defects and ST changes). Model performance was assessed by comparing observed event rates with risk estimates and by performing receiver-operating characteristic curve (ROC) analysis. RESULTS: The postoperative cardiac event rate was 8% for both sets. Prognostic accuracy (i.e., ROC area) was 74 +/- 3% (mean +/- SD) for the clinical and 81 +/- 3% for the clinical and dipyridamole-thallium models. Among the validation sets, areas were 74 +/- 9%, 72 +/- 7% and 76 +/- 5% for each center. Observed and estimated rates were comparable for both sets. By the clinical model, the observed rates were 3%, 8% and 18% for patients classified as low, moderate and high risk by clinical factors (p<0.0001). The addition of dipyridamole-thallium data reclassified >80% of the moderate risk patients into low (3%) and high (19%) risk categories (p<0.0001) but provided no stratification for patients classified as low or high risk according to the clinical model. CONCLUSIONS: Simple clinical markers, weighted according to prognostic impact, will reliably stratify risk in vascular surgery candidates referred for dipyridamole-thallium testing, thus obviating the need for the more expensive testing. Our prediction model retains its prognostic accuracy when applied to the validation sets and can reliably estimate risk in this group.

Aged↗

PubMed2005

Mass balance modelling of contaminants in river basins: a flexible matrix approach.

A novel and flexible approach is described for simulating the behaviour of chemicals in river basins. A number (n) of river reaches are defined and their connectivity is described by entries in an n x n matrix. Changes in segmentation can be readily accommodated by altering the matrix entries, without the need for model revision. Two models are described. The simpler QMX-R model only considers advection and an overall loss due to the combined processes of volatilization, net transfer to sediment and degradation. The rate constant for the overall loss is derived from fugacity calculations for a single segment system. The more rigorous QMX-F model performs fugacity calculations for each segment and explicitly includes the processes of advection, evaporation, water-sediment exchange and degradation in both water and sediment. In this way chemical exposure in all compartments (including equilibrium concentrations in biota) can be estimated. Both models are designed to serve as intermediate-complexity exposure assessment tools for river basins with relatively low data requirements. By considering the spatially explicit nature of emission sources and the changes in concentration which occur with transport in the channel system, the approach offers significant advantages over simple one-segment simulations while being more readily applicable than more sophisticated, highly segmented, GIS-based models.

Environmental Monitoring↗

PubMed2003

Validation subset selections for extrapolation oriented QSPAR models.

One of the most important features of QSPAR models is their predictive ability. The predictive ability of QSPAR models should be checked by external validation. In this work we examined three different types of external validation set selection methods for their usefulness in in-silico screening. The usefulness of the selection methods was studied in such a way that: 1) We generated thousands of QSPR models and stored them in 'model banks'. 2) We selected a final top model from the model banks based on three different validation set selection methods. 3) We predicted large data sets, which we called 'chemical universe sets', and calculated the corresponding SEPs. The models were generated from small fractions of the available water solubility data during a GA Variable Subset Selection procedure. The external validation sets were constructed by random selections, uniformly distributed selections or by perimeter-oriented selections. We found that the best performing models on the perimeter-oriented external validation sets usually gave the best validation results when the remaining part of the available data was overwhelmingly large, i.e., when the model had to make a lot of extrapolations. We also compared the top final models obtained from external validation set selection methods in three independent and different sizes of 'chemical universe sets'.

Computer Simulation↗

PubMed2005

New approaches to identification of bacterial pathogens by surface enhanced laser desorption/ionization time of flight mass spectrometry in concert with artificial neural networks, with special reference to Neisseria gonorrhoeae.

Surface enhanced laser desorption/ionization-time of flight mass spectrometry (SELDI-TOF MS) has been applied in large numbers of oncological studies but the microbiological field has not been extensively explored to date. This paper describes the application of SELDI-TOF MS in concert with a multi-layer perceptron artificial neural network (ANN) with a back propagation algorithm for the identification of Neisseria gonorrhoeae. N. gonorrhoeae, the aetiological agent of gonorrhoea, is the second most common sexually transmitted disease in the UK and USA. Analysis of over 350 strains of N. gonorrhoeae and closely related species by SELDI-TOF MS facilitated the design of an ANN model and revealed 20 ion peak descriptors of positive, negative and secondary nature that were paramount for the identification of the pathogen. The model performed with over 96 % efficiency when based on these 20 ion peak descriptors and exhibited a sensitivity of 95.7 % and a specificity of 97.1 %, with an area under the curve value of 0.996. The technology has the potential to link several ANN models for a comprehensive rapid identification platform for clinically important pathogens.

Bacteria↗

PubMed2004

[An algorithm study on telecardiogram diagnosis based on multivariate autoregressive model and two-lead ECG signals].

OBJECTIVE: In view of the time delay caused by reconstruction of signals at remote sites, a direct classification method with high accuracy suitable for telediagnosis of electrocardiogram (ECG) signals is studied. METHOD: The data for analysis and classification was obtained from MIT-BIH database, including 300 samples each of normal sinus rhythm (NSR), atria premature contraction (APC), premature ventricular contraction (PVC), ventricular tachycardia (VT), ventricular fibrillation (VF) and superventricular tachycardia (SVT). An multivariate autoregressive (MAR) model based technique that could combine the signals of two ECG leads was presented to classify the ECGs directly, including MAR modeling performed on ECGs, and quadratic discrimination function (QDF) based classification by using MAR coefficients and K-L MAR coefficients. RESULT: Besides quick and convenient diagnosis, the accuracy of the proposed classification algorithm was as high as 98.3%-100%. CONCLUSION: The MAR modeling based technique is suitable for telecardiogram diagnosis. Comparing with single-lead ECGs, better classification results can be obtained through the combination of two-lead ECG signals.

Algorithms↗

PubMedDate not supplied

[Elasticity of the foot when modelling man's movements].

An anthropomorphic model was elaborated, which permitted estimation of foot elasticity in man's movements. The foot in the model is presented as an elastic rod. In this model it turned necessary to introduce additional limitations as the zero moment point in support, and the effect of foot elasticity on the dynamics of man's movement was estimated. The movements of the model performing a jump synthetized on a computer proved to be adequate to similar movement of the man.

Computers↗

PubMed1996

A generalized hidden Markov model for the recognition of human genes in DNA.

We present a statistical model of genes in DNA. A Generalized Hidden Markov Model (GHMM) provides the framework for describing the grammar of a legal parse of a DNA sequence (Stormo & Haussler 1994). Probabilities are assigned to transitions between states in the GHMM and to the generation of each nucleotide base given a particular state. Machine learning techniques are applied to optimize these probabilities using a standardized training set. Given a new candidate sequence, the best parse is deduced from the model using a dynamic programming algorithm to identify the path through the model with maximum probability. The GHMM is flexible and modular, so new sensors and additional states can be inserted easily. In addition, it provides simple solutions for integrating cardinality constraints, reading frame constraints, "indels", and homology searching. The description and results of an implementation of such a gene-finding model, called Genie, is presented. The exon sensor is a codon frequency model conditioned on windowed nucleotide frequency and the preceding codon. Two neural networks are used, as in (Brunak, Engelbrecht, & Knudsen 1991), for splice site prediction. We show that this simple model performs quite well. For a cross-validated standard test set of 304 genes [ftp:@www-hgc.lbl.gov/pub/genesets] in human DNA, our gene-finding system identified up to 85% of protein-coding bases correctly with a specificity of 80%. 58% of exons were exactly identified with a specificity of 51%. Genie is shown to perform favorably compared with several other gene-finding systems.

Chromosomes, Human↗

PubMed2026

Biallelic loss of RB1 in hepatocellular carcinoma as synthetic lethal target for artificial intelligence-guided therapy.

The retinoblastoma (RB1) gene is a critical tumor suppressor that regulates cell cycle progression and genomic stability. Although RB1 alterations have been reported in hepatocellular carcinoma (HCC), the biological and clinical consequences of biallelic RB1 inactivation (RB1-Bi) remain poorly defined. We performed a comprehensive allele-specific genomic analysis of HCC patients from the TCGA-LIHC (n = 355) and in-house AMC (n = 206) cohorts, collectively comprising the AMC-TCGA discovery cohort. In this combined cohort, RB1-Bi was identified in 14.6% of tumors, was enriched in poorly differentiated HCCs and was independently associated with significantly reduced overall survival (adjusted hazard ratio 3.32, 95% CI 1.93-5.72, p < 0.001). Additionally, a deep learning-based histopathology model using hematoxylin and eosin-stained slides (i.e., FR-MIL model) accurately predicted RB1-Bi status (F1 score 84.39% [95% CI, ±0.02]), making it readily identifiable in routine clinical practice. The prevalence and prognostic impact of RB1-Bi, as well as FR-MIL model performance, were consistent across independent validation cohorts, including advanced-stage tumors and external institutions. High-throughput drug screening in isogenic HCC models revealed that RB1-Bi HCC cells were particularly sensitive to inhibitors targeting mitotic regulators (e.g., AURKA, PLK1, KSP) and DNA damage response pathways (e.g., PARP inhibitors). Synthetic lethal interactions between RB1-Bi and these compounds were demonstrated in vitro and in vivo, and combination treatment with mitotic and PARP inhibitors had synergistic effects with acceptable tolerability. We conclude that RB1-Bi represents a clinically actionable biomarker that identifies a high-risk HCC subtype with specific therapeutic vulnerabilities, offering new opportunities for precision medicine.

Humans↗

PubMed2002

Diagnosis of breast tumors with sonographic texture analysis using wavelet transform and neural networks.

To increase the ability of ultrasonographic technology for the differential diagnosis of solid breast tumors, we describe a novel computer-aided diagnosis (CADx) system using neural networks for classification of breast tumors. Tumor regions and surrounding tissues are segmented from the physician-located region-of-interest (ROI) images by applying our proposed segmentation algorithm. Cooperating with the segmentation algorithm, three feasible features, including variance contrast, autocorrelation contrast and distribution distortion of wavelet coefficients, were extracted from the ROI images for further classification. A multilayered perceptron (MLP) neural network trained using error back-propagation algorithm with momentum was then used for the differential diagnosis of breast tumors on sonograms. In the experiment, 242 cases (including benign breast tumors from 161 patients and carcinomas from 82 patients) were sampled with k-fold cross-validation (k = 10) to evaluate the performance. The receiver operating characteristic (ROC) area index for the proposed CADx system is 0.9396 +/- 0.0183, the sensitivity is 98.77%, the specificity is 81.37%, the positive predictive value is 72.73% and the negative predictive value is 99.24%. Experimental results showed that our diagnosis model performed very well for breast tumor diagnosis.

Breast Diseases↗