Computational methods for prediction of T-cell epitopes - a framework for modelling, testing, and applications

Computational models complement laboratory experimentation for efficient identification of MHC-binding peptides and T-cell epitopes. Methods for prediction of MHC-binding peptides include binding motifs, quantitative matrices, artificial neural networks, hidden Markov models, and molecular modelling. Models derived by these methods have been successfully used for prediction of T-cell epitopes in cancer, autoimmunity, infectious disease, and allergy. For maximum benefit, the use of computer models must be treated as experiments analogous to standard laboratory procedures and performed according to strict standards. This requires careful selection of data for model building, and adequate testing and validation. A range of web-based databases and MHC-binding prediction programs are available. Although some available prediction programs for particular MHC alleles have reasonable accuracy, there is no guarantee that all models produce good quality predictions. In this article, we present and discuss a framework for modelling, testing, and applications of computational methods used in predictions of T-cell epitopes. (C) 2004 Elsevier Inc. All rights reserved.

ANNA: A new prediction method for bioassessment programs

1. Cluster analysis of reference sites with similar biota is the initial step in creating River Invertebrate Prediction and Classification System (RIVPACS) and similar river bioassessment models such as Australian River Assessment System (AUSRIVAS). This paper describes and tests an alternative prediction method, Assessment by Nearest Neighbour Analysis (ANNA), based on the same philosophy as RIVPACS and AUSRIVAS but without the grouping step that some people view as artificial. 2. The steps in creating ANNA models are: (i) weighting the predictor variables using a multivariate approach analogous to principal axis correlations, (ii) calculating the weighted Euclidian distance from a test site to the reference sites based on the environmental predictors, (iii) predicting the faunal composition based on the nearest reference sites and (iv) calculating an observed/expected (O/E) analogous to RIVPACS/AUSRIVAS. 3. The paper compares AUSRIVAS and ANNA models on 17 datasets representing a variety of habitats and seasons. First, it examines each model's regressions for Observed versus Expected number of taxa, including the r(2), intercept and slope. Second, the two models' assessments of 79 test sites in New Zealand are compared. Third, the models are compared on test and presumed reference sites along a known trace metal gradient. Fourth, ANNA models are evaluated for western Australia, a geographically distinct region of Australia. The comparisons demonstrate that ANNA and AUSRIVAS are generally equivalent in performance, although ANNA turns out to be potentially more robust for the O versus E regressions and is potentially more accurate on the trace metal gradient sites. 4. The ANNA method is recommended for use in bioassessment of rivers, at least for corroborating the results of the well established AUSRIVAS- and RIVPACS-type models, if not to replace them.

A comparison of lipid variables as predictors of cardiovascular disease in the Asia Pacific Region

PURPOSE: Many guidelines advocate measurement of total or low density lipoprotein cholesterol (LDL), high density lipoprotein cholesterol (HDL), and triglycerides (TG) to determine treatment recommendations for preventing coronary heart disease (CHD) and cardiovascular disease (CVD). This analysis is a comparison of lipid variables as predictors of cardiovascular disease. METHODS: Hazard ratios for coronary and cardiovascular deaths by fourths of total cholesterol (TC), LDL, HDL, TG, non-HDL, TC/HDL, and TG/HDL values, and for a one standard deviation change in these variables, were derived in an individual participant data meta-analysis of 32 cohort studies conducted in the Asia-Pacific region. The predictive value of each lipid variable was assessed using the likelihood ratio statistic. RESULTS: Adjusting for confounders and regression dilution, each lipid variable had a positive (negative for HDL) log-linear association with fatal CHD and CVD. Individuals in the highest fourth of each lipid variable had approximately twice the risk of CHD compared with those with lowest levels. TG and HDL were each better predictors of CHD and CVD risk compared with TC alone, with test statistics similar to TC/HDL and TG/HDL ratios. Calculated LDL was a relatively poor predictor. CONCLUSIONS: While LDL reduction remains the main target of intervention for lipid-lowering, these data support the potential use of TG or lipid ratios for CHD risk prediction. (c) 2005 Elsevier Inc. All rights reserved.

PREDBALB/c: a system for the prediction of peptide binding to H2(d) molecules, a haplotype of the BALB/c mouse

PREDBALB/c is a computational system that predicts peptides binding to the major histocompatibility complex-2 (H2(d)) of the BALB/c mouse, an important laboratory model organism. The predictions include the complete set of H2(d) class I ( H2-K-d, H2-L-d and H2-D-d) and class II (I-E-d and I-A(d)) molecules. The prediction system utilizes quantitative matrices, which were rigorously validated using experimentally determined binders and non-binders and also by in vivo studies using viral proteins. The prediction performance of PREDBALB/c is of very high accuracy. To our knowledge, this is the first online server for the prediction of peptides binding to a complete set of major histocompatibility complex molecules in a model organism (H2(d) haplotype). PREDBALB/c is available at http://antigen.i2r.a-star.edu.sg/predBalbc/.

Axial Young's modulus prediction of single-walled carbon nanotube arrays with diameters from nanometer to meter scales

Based on a self-similar array model, we systematically investigated the axial Young's modulus (Y-axis) of single-walled carbon nanotube (SWNT) arrays with diameters from nanometer to meter scales by an analytical approach. The results show that the Y-axis of SWNT arrays decreases dramatically with the increases of their hierarchy number (s) and is not sensitive to the specific size and constitution when s is the same, and the specific Young's modulus Y-axis(s) is independent of the packing configuration of SWNTs. Our calculations also show that the Y-axis of SWNT arrays with diameters of several micrometers is close to that of commercial high performance carbon fibers (CFs), but the Y-axis(s) of SWNT arrays is much better than that of high performance CFs. (C) 2005 American Institute of Physics.

Relation of Aortic Valve Calcium Detected by Cardiac Computed Tomography to All-Cause Mortality

Aortic valve calcium (AVC) can be quantified on the same computed tomographic scan as coronary artery calcium (CAC). Although CAC is an established predictor of cardiovascular events, limited evidence is available for an independent predictive value for AVC. We studied a cohort of 8,401 asymptomatic subjects (mean age 53 10 years, 69% men), who were free of known coronary heart disease and were undergoing electron beam computed tomography for assessment of subclinical atherosclerosis. The patients were followed for a median of 5 years (range 1 to 7) for the occurrence of mortality from any cause. Multivariate Cox regression models were developed to predict all-cause mortality according to the presence of AVC. A total of 517 patients (6%) had AVC on electron beam computed tomography. During follow-up, 124 patients died (1.5%), for an overall survival rate of 96.1% and 98.7% for those with and without AVC, respectively (hazard ratio 3.39, 95% confidence interval 2.09 to 5.49). After adjustment for age, gender, hypertension, dyslipidemia, diabetes mellitus, smoking, and a family history of premature coronary heart disease, AVC remained a significant predictor of mortality (hazard ratio 1.82, 95% confidence interval 1.11 to 2.98). Likelihood ratio chi-square statistics demonstrated that the addition of AVC contributed significantly to the prediction of mortality in a model adjusted for traditional risk factors (chi-square = 5.03, p = 0.03) as well as traditional risk factors plus the presence of CAC (chi-square = 3.58, p = 0.05). In conclusion, AVC was associated with increased all-cause mortality, independent of the traditional risk factors and the presence of CAC. (C) 2010 Published by Elsevier Inc. (Am J Cardiol 2010;106:1787-1791)

Prediction of impact forces in a vibratory ball mill using an inverse technique

To understand the dynamic mechanisms of the mechanical milling process in a vibratory mill, it is necessary to determine the characteristics of the impact forces associated with the collision events. However, it is difficult to directly measure the impact force in an operating mill. This paper describes an inverse technique for the prediction of impact forces from acceleration measurements on a vibratory ball mill. The characteristics of the vibratory mill have been investigated by the modal testing technique, and its system modes have been identified. In the modelling of the system vibration response to the impact forces, two modal equations have been used to describe the modal responses. The superposition of the modal responses gives rise to the total response of the system. A method based on an optimisation approach has been developed to predict the impact forces by minimising the difference between the measured acceleration of the vibratory ball mill and the predicted acceleration from the solution of the modal equations. The predicted and measured impact forces are in good agreement. Copyright (C) 1996 Elsevier Science Ltd.

Validation of a strategy to diagnose coronary artery disease and predict cardiac events in high-risk renal transplant candidates

Background We validated a strategy for diagnosis of coronary artery disease ( CAD) and prediction of cardiac events in high-risk renal transplant candidates ( at least one of the following: age >= 50 years, diabetes, cardiovascular disease). Methods A diagnosis and risk assessment strategy was used in 228 renal transplant candidates to validate an algorithm. Patients underwent dipyridamole myocardial stress testing and coronary angiography and were followed up until death, renal transplantation, or cardiac events. Results The prevalence of CAD was 47%. Stress testing did not detect significant CAD in 1/3 of patients. The sensitivity, specificity, and positive and negative predictive values of the stress test for detecting CAD were 70, 74, 69, and 71%, respectively. CAD, defined by angiography, was associated with increased probability of cardiac events [log-rank: 0.001; hazard ratio: 1.90, 95% confidence interval (CI): 1.29-2.92]. Diabetes (P=0.03; hazard ratio: 1.58, 95% CI: 1.06-2.45) and angiographically defined CAD (P=0.03; hazard ratio: 1.69, 95% CI: 1.08-2.78) were the independent predictors of events. Conclusion The results validate our observations in a smaller number of high-risk transplant candidates and indicate that stress testing is not appropriate for the diagnosis of CAD or prediction of cardiac events in this group of patients. Coronary angiography was correlated with events but, because less than 50% of patients had significant disease, it seems premature to recommend the test to all high-risk renal transplant candidates. The results suggest that angiography is necessary in many high-risk renal transplant candidates and that better noninvasive methods are still lacking to identify with precision patients who will benefit from invasive procedures. Coron Artery Dis 21: 164-167 (C) 2010 Wolters Kluwer Health vertical bar Lippincott Williams & Wilkins.

Derivation and external validation of a simple prediction model for the diagnosis of type 2 Diabetes Mellitus in the Brazilian urban population

A risk score model was developed based in a population of 1,224 individuals from the general population without known diabetes aging 35 years or more from an urban Brazilian population sample in order to select individuals who should be screened in subsequent testing and improve the efficacy of public health assurance. External validation was performed in a second, independent, population from a different city ascertained through a similar epidemiological protocol. The risk score was developed by multiple logistic regression and model performance and cutoff values were derived from a receiver operating characteristic curve. Model`s capacity of predicting fasting blood glucose levels was tested analyzing data from a 5-year follow-up protocol conducted in the general population. Items independently and significantly associated with diabetes were age, BMI and known hypertension. Sensitivity, specificity and proportion of further testing necessary for the best cutoff value were 75.9, 66.9 and 37.2%, respectively. External validation confirmed the model`s adequacy (AUC equal to 0.72). Finally, model score was also capable of predicting fasting blood glucose progression in non-diabetic individuals in a 5-year follow-up period. In conclusion, this simple diabetes risk score was able to identify individuals with an increased likelihood of having diabetes and it can be used to stratify subpopulations in which performing of subsequent tests is necessary and probably cost-effective.

Prediction of psychological adjustment to multiple sclerosis

This study examined the utility of self-efficacy as a predictor of social activity and mood control in multiple sclerosis (MS). Seventy-one subjects with MS were recruited from people attending an MS centre or from a mailing list and were examined on two occasions that were two months apart. Clinic patients were more disabled than patients who completed assessments by post, but they were of higher socioeconomic status and were less dysphoric; We attempted to predict self-reported performance of mood control and social activity at two months, from self-efficacy or performance on these tasks at pretest. Demographic variables, disorder status, disability, self-esteem and depression were also allowed to compete for entry into multiple regressions. Substantial stability in mood, performance and disability was observed over the two months. In both mood control and social activity, past performance was the strongest predictor of later performance, but self-efficacy also contributed significantly to the prediction. The disability level entered a prediction of social activity; but no other variables predicted either type of performance. A secondary analysis predicting self-esteem at two months also included self-efficacy for social activity, illustrating the contribution of perceived capability to later assessments of self-worth. The study provided support for self-efficacy as a predictor of later behavioural outcomes and self-esteem in multiple sclerosis. (C) 1997 Elsevier Science Ltd.

Evaluating SVM and MLDA in the extraction of discriminant regions for mental state prediction

Pattern recognition methods have been successfully applied in several functional neuroimaging studies. These methods can be used to infer cognitive states, so-called brain decoding. Using such approaches, it is possible to predict the mental state of a subject or a stimulus class by analyzing the spatial distribution of neural responses. In addition it is possible to identify the regions of the brain containing the information that underlies the classification. The Support Vector Machine (SVM) is one of the most popular methods used to carry out this type of analysis. The aim of the current study is the evaluation of SVM and Maximum uncertainty Linear Discrimination Analysis (MLDA) in extracting the voxels containing discriminative information for the prediction of mental states. The comparison has been carried out using fMRI data from 41 healthy control subjects who participated in two experiments, one involving visual-auditory stimulation and the other based on bimanual fingertapping sequences. The results suggest that MLDA uses significantly more voxels containing discriminative information (related to different experimental conditions) to classify the data. On the other hand, SVM is more parsimonious and uses less voxels to achieve similar classification accuracies. In conclusion, MLDA is mostly focused on extracting all discriminative information available, while SVM extracts the information which is sufficient for classification. (C) 2009 Elsevier Inc. All rights reserved.

New body fat prediction equations for severely obese patients

Background & aims: Severe obesity imposes physical limitations to body composition assessment. Our aim was to compare body fat (BF) estimations of severely obese patients obtained by bioelectrical impedance (BIA) and air displacement plethysmography (ADP) for development of new equations for BF prediction. Methods: Severely obese subjects (83 female/36 mate, mean age = 41.6 +/- 11.6 years) had BF estimated by BIA and ADP. The agreement of the data was evaluated using Bland-Altman`s graphic and concordance correlation coefficient (CCC). A multivariate regression analysis was performed to develop and validate new predictive equations. Results: BF estimations from BIA (64.8 +/- 15 kg) and ADP (65.6 +/- 16.4 kg) did not differ (p > 0.05, with good accuracy, precision, and CCC), but the Bland- Altman graphic showed a wide Limit of agreement (- 10.4; 8.8). The standard BIA equation overestimated BF in women (-1.3 kg) and underestimated BF in men (5.6 kg; p < 0.05). Two BF new predictive equations were generated after BIA measurement, which predicted BF with higher accuracy, precision, CCC, and limits of agreement than the standard BIA equation. Conclusions: Standard BIA equations were inadequate for estimating BF in severely obese patients. Equations developed especially for this population provide more accurate BF assessment. (C) 2008 Elsevier Ltd and European Society for Clinical Nutrition and Metabolism. All rights reserved.

Very early prediction of response to HCV treatment with PEG-IFN-alfa-2a and ribavirin in HIV/HCV-coinfected patients

The objective of this study was to find very early viral kinetic markers to predict nonresponse to hepatitis C virus (HCV) therapy in a group of human immunodeficiency virus (HIV)/HCV-coinfected patients. Twenty-six patients (15 HCV genotype-1 and 11 genotype-3) were treated with a 48-week regimen of peginterferon-alfa-2a (PEG-IFN) (180 mu g/week) and weight-based ribavirin (11 mg/kg/day). Samples were collected at baseline; 4, 8, 12, 18, 24, 30, 36 and 42 h; days 2, 3, 4, 7, 8, 15, 22, 29, 43 and 57 then weekly and monthly. Five patients discontinued treatment. Seven patients (27%) achieved a sustained virological response (SVR). Nadir HCV RNA levels were observed 1.6 +/- 0.3 days after initiation of therapy, followed by a 0.3- to 12.9-fold viral rebound until the administration of the second dose of PEG-IFN, which were not associated with SVR or HCV genotype. A viral decline < 1.19 log for genotype-1 and < 0.97 log for genotype-3, 2 days after starting therapy, had a negative predictive value (NPV) of 100% for SVR. The day 2 virological response had a similar positive predictive value for SVR as a rapid virological response at week 4. In addition, a second-phase viral decline slope (i.e., measured from day 2 to 29) < 0.3 log/week had a NPV = 100% for SVR. We conclude that first-phase viral decline at day 2 and second-phase viral decline slope (< 0.3 log/week) are excellent predictors of nonresponse. Further studies are needed to validate these viral kinetic parameters as early on-treatment prognosticators of nonresponse in patients with HCV and HIV.