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PubMed2026

Improving the reliability of polygenic risk score-based prediction for cardiovascular and renal complications across ancestries in type 2 diabetes using Mondrian Cross-Conformal Prediction.

Polygenic risk scores (PRS) developed in European populations often show reduced predictive performance in non-European populations, limiting their clinical utility. This lack of transferability across ancestries remains a major challenge in genomic medicine and raises concerns about health equity. We aimed to evaluate whether uncertainty-aware prediction, implemented through Mondrian Cross-Conformal Prediction, improves the performance and reliability of polygenic risk score-based predictions across ancestries for nephropathy, stroke, and myocardial infarction in individuals with type 2 diabetes in a multi-ethnic cohort. We leveraged Mondrian Cross-Conformal Prediction (MCCP), an uncertainty quantification framework, combined with logistic regression applied to a multi-polygenic risk score (multiPRS) to predict the risk of nephropathy, stroke, and myocardial infarction in individuals with type 2 diabetes. Two training frameworks were evaluated: one using 4,098 individuals with type 2 diabetes of European ancestry from the ADVANCE trial for training and 17,574 White British, 1,145 South Asian, and 749 African UK Biobank participants for testing; and another using the 17,574 White British UK Biobank participants for training and the South Asian and African participants for testing. Logistic regression provided robust baseline performance across populations. On top of this baseline, MCCP did not improve performance but added capabilities absent from probability-based stratification: for each individual, it issued a prediction together with an explicit confidence and credibility level; it allowed a tolerated error level to be set in advance and delivered prediction sets respecting it in the majority of settings; and it flagged individuals for whom no reliable prediction could be made. Applying MCCP to PRS-based prediction thus enables uncertainty-aware risk stratification and improves the reliability of risk prediction across ancestries, providing a more equitable framework for clinical use.

Female↗

PubMed2002

Comparison of the computer programs DEREK and TOPKAT to predict bacterial mutagenicity. Deductive Estimate of Risk from Existing Knowledge. Toxicity Prediction by Komputer Assisted Technology.

The performance of two computer programs, DEREK and TOPKAT, was examined with regard to predicting the outcome of the Ames bacterial mutagenicity assay. The results of over 400 Ames tests conducted at Glaxo Wellcome (now GlaxoSmithKline) during the last 15 years on a wide variety of chemical classes were compared with the mutagenicity predictions of both computer programs. DEREK was considered concordant with the Ames assay if (i) the Ames assay was negative (not mutagenic) and no structural alerts for mutagenicity were identified or (ii) the Ames assay was positive (mutagenic) and at least one structural alert was identified. Conversely, the DEREK output was considered discordant if (i) the Ames assay was negative and any structural alert was identified or (ii) the Ames assay was positive and no structural alert was identified. The overall concordance of the DEREK program with the Ames results was 65% and the overall discordance was 35%, based on over 400 compounds. About 23% of the test molecules were outside the permissible limits of the optimum prediction space of TOPKAT. Another 4% of the compounds were either not processable or had indeterminate mutagenicity predictions; these molecules were excluded from the TOPKAT analysis. If the TOPKAT probability was (i) > or =0.7 the molecule was predicted to be mutagenic, (ii) < or =0.3 the compound was predicted to be non-mutagenic and (iii) between 0.3 and 0.7 the prediction was considered indeterminate. From over 300 acceptable predictions, the overall TOPKAT concordance was 73% and the overall discordance was 27%. While the overall concordance of the TOPKAT program was higher than DEREK, TOPKAT fared more poorly than DEREK in the critical Ames-positive category, where 60% of the compounds were incorrectly predicted by TOPKAT as negative but were mutagenic in the Ames test. For DEREK, 54% of the Ames-positive molecules had no structural alerts and were predicted to be non-mutagenic. Alternative methods of analyzing the output of the programs to increase the accuracy with Ames-positive compounds are discussed.

Bacteria↗

PubMedDate not supplied

An algorithm for protein secondary structure prediction based on class prediction.

An algorithm has been developed to improve the success rate in the prediction of the secondary structure of proteins by taking into account the predicted class of the proteins. This method has been called the 'double prediction method' and consists of a first prediction of the secondary structure from a new algorithm which uses parameters of the type described by Chou and Fasman, and the prediction of the class of the proteins from their amino acid composition. These two independent predictions allow one to optimize the parameters calculated over the secondary structure database to provide the final prediction of secondary structure. This method has been tested on 59 proteins in the database (i.e. 10,322 residues) and yields 72% success in class prediction, 61.3% of residues correctly predicted for three states (helix, sheet and coil) and a good agreement between observed and predicted contents in secondary structure.

Algorithms↗

PubMed2005

Evaluation of methods for predicting the topology of beta-barrel outer membrane proteins and a consensus prediction method.

BACKGROUND: Prediction of the transmembrane strands and topology of beta-barrel outer membrane proteins is of interest in current bioinformatics research. Several methods have been applied so far for this task, utilizing different algorithmic techniques and a number of freely available predictors exist. The methods can be grossly divided to those based on Hidden Markov Models (HMMs), on Neural Networks (NNs) and on Support Vector Machines (SVMs). In this work, we compare the different available methods for topology prediction of beta-barrel outer membrane proteins. We evaluate their performance on a non-redundant dataset of 20 beta-barrel outer membrane proteins of gram-negative bacteria, with structures known at atomic resolution. Also, we describe, for the first time, an effective way to combine the individual predictors, at will, to a single consensus prediction method. RESULTS: We assess the statistical significance of the performance of each prediction scheme and conclude that Hidden Markov Model based methods, HMM-B2TMR, ProfTMB and PRED-TMBB, are currently the best predictors, according to either the per-residue accuracy, the segments overlap measure (SOV) or the total number of proteins with correctly predicted topologies in the test set. Furthermore, we show that the available predictors perform better when only transmembrane beta-barrel domains are used for prediction, rather than the precursor full-length sequences, even though the HMM-based predictors are not influenced significantly. The consensus prediction method performs significantly better than each individual available predictor, since it increases the accuracy up to 4% regarding SOV and up to 15% in correctly predicted topologies. CONCLUSIONS: The consensus prediction method described in this work, optimizes the predicted topology with a dynamic programming algorithm and is implemented in a web-based application freely available to non-commercial users at http://bioinformatics.biol.uoa.gr/ConBBPRED.

Algorithms↗

PubMed2004

Weather-based prediction of Plasmodium falciparum malaria in epidemic-prone regions of Ethiopia II. Weather-based prediction systems perform comparably to early detection systems in identifying times for interventions.

BACKGROUND: Timely and accurate information about the onset of malaria epidemics is essential for effective control activities in epidemic-prone regions. Early warning methods that provide earlier alerts (usually by the use of weather variables) may permit control measures to interrupt transmission earlier in the epidemic, perhaps at the expense of some level of accuracy. METHODS: Expected case numbers were modeled using a Poisson regression with lagged weather factors in a 4th-degree polynomial distributed lag model. For each week, the numbers of malaria cases were predicted using coefficients obtained using all years except that for which the prediction was being made. The effectiveness of alerts generated by the prediction system was compared against that of alerts based on observed cases. The usefulness of the prediction system was evaluated in cold and hot districts. RESULTS: The system predicts the overall pattern of cases well, yet underestimates the height of the largest peaks. Relative to alerts triggered by observed cases, the alerts triggered by the predicted number of cases performed slightly worse, within 5% of the detection system. The prediction-based alerts were able to prevent 10-25% more cases at a given sensitivity in cold districts than in hot ones. CONCLUSIONS: The prediction of malaria cases using lagged weather performed well in identifying periods of increased malaria cases. Weather-derived predictions identified epidemics with reasonable accuracy and better timeliness than early detection systems; therefore, the prediction of malarial epidemics using weather is a plausible alternative to early detection systems.

Algorithms↗

PubMed2004

Assessment of prediction confidence and domain extrapolation of two structure-activity relationship models for predicting estrogen receptor binding activity.

Quantitative structure-activity relationship (QSAR) methods have been widely applied in drug discovery, lead optimization, toxicity prediction, and regulatory decisions. Despite major advances in algorithms and software, QSAR models have inherent limitations associated with a size and chemical-structure diversity of the training set, experimental error, and many characteristics of structure representation and correlation algorithms. Whereas excellent fit to the training data may be readily attainable, often models fail to predict accurately chemicals that are outside their domain of applicability. A QSAR's utility and, in the case of regulatory decisions, justification for usage increasingly depend on the ability to quantify a model's potential for predicting unknown chemicals with some known degree of certainty. It is never possible to predict an unknown chemical with absolute certainty. Here we report on two QSAR models based on different data sets for classification of chemicals according to their ability to bind to the estrogen receptor. The models were developed by using a novel QSAR method, Decision Forest, which combines the results of multiple heterogeneous but comparable Decision Tree models to produce a consensus prediction. We used an extensive cross-validation process to define an applicability domain for model predictions based on two quantitative measures: prediction confidence and domain extrapolation. Together, these measures quantify the accuracy of each prediction within and outside of the training domain. Despite being based on large and diverse training sets, both QSAR models had poor accuracy for chemicals within the domain of low confidence, whereas good accuracy was obtained for those within the domain of high confidence. For prediction in the high confidence domain, accuracy was inversely proportional to the degree of domain extrapolation. The model with a larger training set of 1,092, compared with 232 for the other, was more accurate in predicting chemicals at larger domain extrapolation, and could be particularly useful for rapidly prioritizing potential endocrine disruptors from large chemical universe.

Animals↗

PubMed2006

A global examination of allometric scaling for predicting human drug clearance and the prediction of large vertical allometry.

Allometrically scaled data sets (138 compounds) used for predicting human clearance were obtained from the literature. Our analyses of these data have led to four observations. (1) The current data do not provide strong evidence that systemic clearance (CL(s); n = 102) is more predictable than apparent oral clearance (CL(po); n = 24), but caution needs to be applied because of potential CL(po) prediction error caused by differences in bioavailability across species. (2) CL(s) of proteins (n = 10) can be more accurately predicted than that of non-protein chemicals (n = 102). (3) CL(s) is more predictable for compounds eliminated by renal or biliary excretion (n = 33) than by metabolism (n = 57). (4) CL(s) predictability for hepatically eliminated compounds followed the order: high CL (n = 11) > intermediate CL (n = 17) > low CL (n = 29). All examples of large vertical allometry (% error of prediction greater than 1000%) occurred only when predicting human CL(s) of drugs having very low CL(s). A qualitative analysis revealed the application of two potential rules for predicting the occurrence of large vertical allometry: (1) ratio of unbound fraction of drug in plasma (f(u)) between rats and humans greater than 5; (2) C logP greater than 2. Metabolic elimination could also serve as an additional indicator for expecting large vertical allometry.

Biological Availability↗

PubMed2005

[Positive predictability and predictive factors of the third generation anti-hepatitis C virus (HCV) ELISA test for HCV infection].

BACKGROUND/AIMS: Anti-HCV positivity suggests past or present infection of HCV, or false positivity. The positive predictability of this test can differ according to the subjects. This study examines the positive predictability of the third generation anti-HCV ELISA and factors predicting HCV infection with special emphasis on the significance of the anti-HCV sample/cut-off (S/CO) ratio. METHODS: One hundred and ninety patients who were anti-HCV positive were enrolled, from November 1998 to January 2002 in Kyung Hee University Hospital. RT-PCR was performed to confirm HCV infection. RESULTS: One hundred and seven patients were RT-PCR positive (56.3% positive predictability). The positive predictability changed with the S/CO ratio: 17.9% in cases below 6, 58.3% between 6 and 50, 78.6% between 51 and 75, and 60% over 75. Those with the S/CO ratio more than 6 showed significantly higher predictability, but it did not increase further when the ratio got higher. Factors predicting HCV infection were the presence of liver cirrhosis (OR 5.5, p=0.000), hepatocellular carcinoma (OR 11.67, p=0.004), liver diseases (OR 2.99 p=0.001), and increase of AST (OR 2.49, p=0.002), ALT (OR 2.32, p=0.005), alpha-FP (OR 3.49, p=0.040), and the S/CO ratio of more than 6 (OR 7.82, p=0.000). However, liver cirrhosis was the sole factor in multivariate analysis (OR 8.32, p=0.02). CONCLUSIONS: The positive predictability of the third generation anti-HCV test was 56.3% with a significant difference between those with the S/CO ratio below 6 (18%) and above 6 (63%). In liver cirrhosis, positive predictability of anti-HCV test was relatively high as 85%.

Adult↗

PubMedDate not supplied

Assessing the perceived predictability of anxiety-related events: a report on the perceived predictability index.

Predictability, or lack thereof, is believed to play a critical role in the development and maintenance of anxiety, with unpredictability being associated with heightened levels of anxious and fearful responding. Despite the potential importance of predictability in theoretical accounts of emotional dysregulation, currently no standardized assessment instrument exists to assess predictability perceptions for anxiety-related events. The present series of four investigations report on an initial attempt to develop a self-report instrument (i.e., Perceived Predictability Index, PPI) that can measure predictability perceptions for the occurrence, duration, and termination of anxiety-related events. Initial item selection and factor structure of the instrument was based on a large sample of participants and yielded a two-factor solution: (1) prediction of anxiety-related environmental events and (2) prediction of internal events. Our subsequent studies show that the PPI possesses adequate levels of internal consistency and temporal stability over time. Additionally, the PPI demonstrated adequate divergent and convergent validity relative to other standard anxiety and fear measures. The internal dimension of the scale also demonstrated predictive validity for emotional responding during a biological challenge test. We discuss these findings in relation to the role of perceived predictability in the study of anxious and fearful responding, and offer directions for future research.

Adolescent↗

PubMed2003

Modulation of cerebellar activation by predictive and non-predictive sequential finger movements.

We investigated the modulation of cerebellar activation by predictive and non-predictive sequential finger movements. It is hypothesized that the prediction of desired movement sequences and adaptation to new movement parameters is mediated by the cerebellum. Using functional MRI at 1.5 T, seven normal subjects performed sequential finger to thumb opposition movements, either in predictive (repeatedly 2,3,4,5) or non-predictive (randomized) fashion at a constant frequency of 1 Hz. Performance and error rates were monitored by simultaneous recording of the finger movements. Predictive sequential finger opposition movements activated a cerebellar network including the lobuli IV-VI ipsilateral to the movements, the contralateral lobuli IV-VI, the vermis, and lobuli VIIB-VIII ipsilaterally. Non-predictive compared to predictive finger opposition movements activated a broader area within the ipsi- and contralateral anterior cerebellum, lobuli IV-VI, the vermis, and the ipsilateral lobuli VIIB-VIII. Additional activation foci were found in the contralateral lobuli VIIA and VIIB-VIII. Our study demonstrates a modulated information processing within the cerebellar network dependent on the predictability of movement sequences.

Brain Mapping↗

PubMed2005

HYPROSP II--a knowledge-based hybrid method for protein secondary structure prediction based on local prediction confidence.

MOTIVATION: In our previous approach, we proposed a hybrid method for protein secondary structure prediction called HYPROSP, which combined our proposed knowledge-based prediction algorithm PROSP and PSIPRED. The knowledge base constructed for PROSP contains small peptides together with their secondary structural information. The hybrid strategy of HYPROSP uses a global quantitative measure, match rate, to determine whether PROSP or PSIPRED is to be used for the prediction of a target protein. HYPROSP made slight improvement of Q(3) over PSIPRED because PROSP predicted well for proteins with match rate >80%. As the portion of proteins with match rate >80% is quite small and as the performance of PSIPRED also improves, the advantage of HYPROSP is diluted. To overcome this limitation and further improve the hybrid prediction method, we present in this paper a new hybrid strategy HYPROSP II that is based on a new quantitative measure called local match rate. RESULTS: Local match rate indicates the amount of structural information that each amino acid can extract from the knowledge base. With the local match rate, we are able to define a confidence level of the PROSP prediction results for each amino acid. Our new hybrid approach, HYPROSP II, is proposed as follows: for each amino acid in a target protein, we combine the prediction results of PROSP and PSIPRED using a hybrid function defined on their respective confidence levels. Two datasets in nrDSSP and EVA are used to perform a 10-fold cross validation. The average Q(3) of HYPROSP II is 81.8% and 80.7% on nrDSSP and EVA datasets, respectively, which is 2.0% and 1.1% better than that of PSIPRED. For local structures with match rate >80%, the average Q(3) improvement is 4.4% on the nrDSSP dataset. The use of local match rate improves the accuracy better than global match rate. There has been a long history of attempts to improve secondary structure prediction. We believe that HYPROSP II has greatly utilized the power of peptide knowledge base and raised the prediction accuracy to a new high. The method we developed in this paper could have a profound effect on the general use of knowledge base techniques for various predictionalgorithms. AVAILABILITY: The Linux executable file of HYPROSP II, as well as both nrDSSP and EVA datasets can be downloaded from http://bioinformatics.iis.sinica.edu.tw/HYPROSPII/.

Algorithms↗

PubMed1990

A comparison of multivariable mathematical methods for predicting survival--III. Accuracy of predictions in generating and challenge sets.

This paper concludes a study of "performance variability" when four methods of multivariable analysis--multiple linear regression, discriminant function analysis, multiple logistic regression, and two arrangements of Cox's proportional hazards regression--were applied to the same stratified random samples of "generating sets" containing seven different statistical distributions of cogent biologic attributes in a composite staging system for a large cohort of patients with lung cancer. Each model developed from the generating sets was also applied for predictions in a previously sequestered "challenge set". Across the different generating sets, the multivariable methods showed good agreement with one another in the stepwise choice of first two powerful predictor variables, but not in the sequence of subsequent choices or in the standardized coefficients assigned to the same collection of "forced" variables. In concordance of predictions for individual patients in the generating sets, the overall proportions of disagreement for pairs of methods ranged from 0 to 28%, and kappa values ranged from 0.49 to 1.00. The accuracy of individual predictions showed relatively similar results when the different methods were applied to the same generating set. Across the generating sets, the different methods showed similar total results but substantial variations in predictions for alive and dead patients. When the models from the generating sets were applied for predictions in the challenge set, the results showed an analogous pattern: similar accuracy within models for overall and live/dead predictions, but substantial variations in live/dead predictions across models derived from different generating sources. The results showed that the multivariable methods often had good agreement with one another in predictions for groups but not for individual persons; and that no single method was superior to the others or to the composite staging system. We conclude that multivariable analytic methods may be most effective and consistent if used to find the few most powerful predictor variables, omitting the many other variables that may be "statistically significant" but less cogent. The powerful predictors may sometimes be best constructed, before the analysis begins, as composite variables containing appropriate unions or ordinal arrangements of elemental candidate variables.

Cohort Studies↗

PubMed1993

How well do prediction equations predict? Using receiver operating characteristic curves and accuracy curves to compare validity and generalizability.

Although morbidity and mortality prediction equations are widely used in planning, clinical practice, and health risk appraisal, their validity and generalizability have been tested only in a limited way. Previous attempts lacked an absolute standard of performance and looked only at the equations' ability to predict who would become ill (sensitivity), not the equally important ability to predict who would remain healthy (specificity). We compared six all-cause mortality prediction equations using receiver operating characteristic curves and accuracy curves, which overcome the limitations of earlier methods and provide a concise visual representation of the results. We used equations from five prospective studies conducted in the United States (Tecumseh at 8 and 12 years of follow-up, Framingham, Chicago Gas, Chicago Western Electric, and Albany), each of which included cholesterol, smoking, and blood pressure as independent variables, to predict 12-year mortality in Tecumseh males age 40-54 years. Previous studies suggested that these equations predict equally well. Our analysis found that, although all predict better than chance, Albany, Chicago Western Electric, and Tecumseh at 8 years underestimate mortality. Receiver operating characteristic and accuracy curves are a promising technique for assessment of prediction equations.

Cholesterol↗

PubMed2006

Predicting mortality in spontaneous intracerebral hemorrhage: can modification to original score improve the prediction?

BACKGROUND AND PURPOSE: A clinical grading scale for intracerebral hemorrhage (ICH), formally ICH score, was recently developed showing to predict 30-day mortality in a simple and reliable manner. The aim of the present study was to validate the original ICH (oICH) score in an independent cohort of patients from a developing country assessing 30-day mortality and 6-month functional outcome and whether its modifications can improve prediction. METHODS: Consecutive patients admitted with acute ICH between January 1, 2003, and July 31, 2004, were prospectively included. oICH score was applied and 2 modified ICH (mICH) scores were created with the same variables, except localization, of the oICH score but with different cutoff values. Outcome was assessed as 30-day mortality and 6-month good outcome (Glasgow Outcome Scale [GOS] 4 to 5). RESULTS: A total of 153 patients were included during study period. Thirty-day mortality rate was 34.6% (n=53), and 59 patients (38.6%) had good functional outcome (GOS 4 to 5) at 6 months. The oICH and mICH scores predicted mortality equally well. According to Youden's index (J), the oICH score was a reliable predictor for mortality (J=0.59) but less reliable for predicting good outcome (J=0.54). The mICH scores were equal in predicting mortality but better for predicting good outcome than the oICH score (J=0.60). CONCLUSIONS: oICH score also confirms its validity in a socially and culturally different population. Modifications of oICH do not improve its 30-day mortality prediction but improve its ability to predict good functional outcome at 6 months.

Aged↗

PubMed2006

The stroke-thrombolytic predictive instrument: a predictive instrument for intravenous thrombolysis in acute ischemic stroke.

BACKGROUND AND PURPOSE: Many patients with ischemic stroke eligible for recombinant tissue plasminogen activator (rt-PA) are not treated in part because of the risks and benefits perceived by treating physicians. Therefore, we aimed to develop a Stroke-Thrombolytic Predictive Instrument (TPI) to aid physicians considering thrombolysis for stroke. METHODS: Using data from 5 major randomized clinical trials (n=2184) testing rt-PA in the 0- to 6-hour window, we developed logistic regression equations using clinical variables as potential predictors of a good outcome (modified Rankin Scale score < or =1) and of a catastrophic outcome (modified Rankin Scale score > or =5), with and without rt-PA. The models were internally validated using bootstrap re-sampling. RESULTS: To predict good outcome, in addition to rt-PA treatment, 7 variables significantly affected prognosis and/or the treatment-effect of rt-PA: age, diabetes, stroke severity, sex, previous stroke, systolic blood pressure, and time from symptom onset. To predict catastrophic outcome, only age, stroke severity, and serum glucose were significant; rt-PA treatment was not. For patients treated within 3 hours, the median predicted probability of a good outcome with rt-PA was 42.9% (interquartile range [IQR]=18.6% to 64.7%) versus 25.3% (IQR=9.8% to 46.2%) without rt-PA; the median predicted absolute benefit was 12.5% (IQR=5.1% to 21.0%). The median probability for a catastrophic outcome, with or without, rt-PA was 15.2% (IQR=8.0% to 31.2%). The area under the receiver-operator characteristic curve was 0.788 for the model predicting good outcome and 0.775 for the model predicting bad outcome. CONCLUSIONS: The Stroke-TPI predicts good and bad functional outcomes with and without thrombolysis. Incorporated into a usable tool, it may assist in decision-making.

Aged↗

PubMed2004

Sensitivity, specificity, positive predictive value, and negative predictive value of the dipyridamole sestamibi stress test comparing arterial to vein conduits.

STUDY OBJECTIVES: To determine the sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) of the dipyridamole stress test (DSST) in predicting > or = 50% obstruction of an internal mammary artery or new native coronary artery disease (CAD) compared with saphenous vein graft obstruction > or = 50% in patients with prior coronary artery surgery and symptoms. DESIGN: In 144 patients with prior coronary artery surgery who underwent a DSST within 8 +/- 7 days of coronary angiography performed because of cardiac symptoms, we investigated the sensitivity, specificity, PPV, and NPV of the DSST in predicting > or = 50% obstruction of an internal mammary artery or new native CAD (201 total arterial conduits) vs > or = 50% obstruction of saphenous vein grafts (total saphenous grafts = 246). SETTING: A university hospital. PATIENTS: The 144 patients included 88 men and 56 women, mean age 68 +/- 9 years (+/- SD). RESULTS: The DSST had a sensitivity of 81%, a specificity of 87%, a PPV of 84%, and a NPV of 84% in predicting > or = 50% obstruction of an internal mammary artery or new native CAD. The DSST had a sensitivity of 88%, a specificity of 82%, a PPV of 86%, and a NPV of 85% in predicting > or = 50% obstruction of saphenous vein grafts. CONCLUSION: There was no significant difference in sensitivity, specificity, PPV, or NPV of the DSST in predicting > or = 50% obstruction of an internal mammary artery or new native CAD vs predicting > or = 50% obstruction of saphenous vein grafts in patients with prior coronary artery surgery and cardiac symptoms.

Adult↗

PubMed2006

Improved performance in protein secondary structure prediction by combining multiple predictions.

In this paper(1) we present a novel framework for protein secondary structure prediction. In this prediction framework, firstly we propose a novel parameterized semi-probability profile, which combines single sequence with evolutionary information effectively. Secondly, different semi-probability profiles are respectively applied as network input to predict protein secondary structure. Then a comparison among these different predictions is discussed in this article. Finally, naïve Bayes approaches are used to combine these predictions in order to obtain a better prediction performance than individual prediction. The experimental results show that our proposed framework can indeed improve the prediction accuracy.

Algorithms↗

PubMed2005

Predicting the development of diabetes in older adults: the derivation and validation of a prediction rule.

OBJECTIVE: To create a simple prediction rule that could perform as well as the 2-h postchallenge plasma glucose (PCPG) test to predict those at risk for diabetes. We created a prediction rule in one sample and prospectively validated it for incident diabetes in a separate cohort. RESEARCH DESIGN AND METHODS: A cross-sectional analysis with data from the Rancho Bernardo Study (age 67 +/- 11 years) to derive a rule predicting abnormal PCPG >/=140 mg/dl, using demographic, clinical, and laboratory data of nondiabetic participants with fasting plasma glucose (FPG) <126 mg/dl. Data from the Health, Aging and Body Composition study (age 74 +/- 3 years) were used to prospectively validate this rule for incident diabetes and compare it with the predictive ability of the PCPG test. RESULTS: Of 1,549 RBS participants, 514 (33%) had PCPG >/=140 mg/dl. Female sex, age, triglycerides, and FPG were most significantly associated with abnormal PCPG. Based on standardized beta-coefficients, we allotted 1 point for female sex, triglycerides >/=150 mg/dl, or FPG 95-104 mg/dl. Age >/=70 years or FPG 105-115 mg/dl were given 2 points, and FPG 116-125 mg/dl received 3 points. In the validation cohort, this simple prediction rule was as good as the 2-h PCPG test for predicting incident diabetes (C-statistic: 0.71 for both). CONCLUSIONS: Advanced age, female sex, FPG, and triglycerides were able to predict adults at risk for diabetes equally well as the 2-h PCPG test. Using this rule, clinicians may better identify older persons who should receive intensive lifestyle intervention to prevent type 2 diabetes.

Aged↗