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At least 631 records · Page 35Linked to original sources
PubMed2001

Improved prediction of intraocular lens power using partial coherence interferometry.

PURPOSE: To evaluate the feasibility of using a new optical biometry technique, dual-beam partial coherence interferometry (PCI), to improve intraocular lens (IOL) power prediction in cataract surgery. SETTING: Department of Ophthalmology, Vienna General Hospital, and Institute of Medical Physics, University of Vienna, Vienna, Austria. METHODS: Preoperative axial length (AL) data obtained with PCI biometry and applanation ultrasound (US) biometry in 77 eyes of 51 patients was applied to 4 commonly used IOL power formulas. The refractive outcome and the mean absolute error (MAE) were calculated for each formula using both biometry methods. A linear multiple-regression model based on preoperative PCI biometry data was derived to predict the postoperative anterior chamber depth (ACD). The predictive power of this regression model was assessed by adding the predicted ACD to the SRK/T formula. Predicted residuals were calculated to evaluate the feasibility and stability of this modified IOL power formula. RESULTS: Using PCI instead of US biometry significantly improved the refractive outcome with all 4 IOL power formulas. The Holladay I and SRK/T formulas yielded an MAE of 0.44 diopter (D) using PCI AL data and 0.56 D and 0.57 D, respectively, using US biometry data. The SRK/T formula combined with the PCI regression model for postoperative ACD prediction performed slightly better (MAE 0.42 D) than the conventional SRK/T formula alone. Predicted residuals revealed an MAE of 0.46 D, proving the predictive performance of the new formula. CONCLUSIONS: Partial coherence interferometry biometry applied to several widely used IOL power formulas yielded significantly better IOL power prediction and therefore refractive outcome in cataract surgery than US biometry. Further improvement can be achieved by applying PCI to a modified SRK/T formula that predicts the postoperative ACD using PCI biometry data.

Biometry↗
PubMed2002

Single-pass attenuated total reflection Fourier transform infrared spectroscopy for the prediction of protein secondary structure.

Principal component regression (PCR) was applied to a spectral library of proteins in H2O solution acquired by single-pass attenuated total reflectance (ATR) Fourier transform infrared (FT-IR) spectroscopy. PCR was used to predict the secondary structure content, principally alpha-helical and the beta-sheet content, of proteins within a spectral library. Quantitation of protein secondary structure content was performed as a proof of principle that use of single-pass ATR-FT-IR is an appropriate method for protein secondary structure analysis. The ATR-FT-IR method permits acquisition of the entire spectral range from 700 to 3900 cm(-1) without significant interference from water bands. An "inside model space" bootstrap and a genetic algorithm (GA) were used to improve prediction results. Specifically, the bootstrap was utilized to increase the number of replicates for adequate training and validation of the PCR model. The GA was used to optimize PCR parameters, particularly wavenumber selection. The use of the bootstrap allowed for adequate representation of variability in the amide A, amide B, and C-H stretching regions due to differing levels of sample hydration. Implementation of the bootstrap improved the robustness of the PCR models significantly; however, the use of a GA only slightly improved prediction results. Two spectral libraries are presented where one was better suited for beta-sheet content prediction and the other for alpha-helix content prediction. The GA-optimized PCR method for alpha-helix content prediction utilized 120 wavenumbers within the amide I, II, A, B, and IV and the C-H stretching regions and 18 factors. For beta-sheet content predictions, 580 wavenumbers within the amide I, II, A, and B and the C-H stretching regions and 18 factors were used. The validation results using these two methods yielded an average absolute error of 1.7% for alpha-helix content prediction and an average absolute error of 2.3% for beta-sheet content prediction. After the PCR models were developed and validated, they were used to predict the alpha-helix and beta-sheet content of two unknowns, casein and immunoglobulin G.

Multivariate Analysis↗
PubMed1998

Dopamine neurons report an error in the temporal prediction of reward during learning.

Many behaviors are affected by rewards, undergoing long-term changes when rewards are different than predicted but remaining unchanged when rewards occur exactly as predicted. The discrepancy between reward occurrence and reward prediction is termed an 'error in reward prediction'. Dopamine neurons in the substantia nigra and the ventral tegmental area are believed to be involved in reward-dependent behaviors. Consistent with this role, they are activated by rewards, and because they are activated more strongly by unpredicted than by predicted rewards they may play a role in learning. The present study investigated whether monkey dopamine neurons code an error in reward prediction during the course of learning. Dopamine neuron responses reflected the changes in reward prediction during individual learning episodes; dopamine neurons were activated by rewards during early trials, when errors were frequent and rewards unpredictable, but activation was progressively reduced as performance was consolidated and rewards became more predictable. These neurons were also activated when rewards occurred at unpredicted times and were depressed when rewards were omitted at the predicted times. Thus, dopamine neurons code errors in the prediction of both the occurrence and the time of rewards. In this respect, their responses resemble the teaching signals that have been employed in particularly efficient computational learning models.

Animals↗
PubMed2000

Accuracy of metamemory after traumatic brain injury: predictions during verbal learning.

The primary intent of this study was to investigate the metamemory monitoring abilities of adult survivors of at least moderate traumatic brain injury (TBI) during a verbal-learning activity. Eighteen survivors and 18 non-injured control participants made judgment-of-learning (JOL) predictions of their recall ability immediately after studying 3 lists of noun-pairs or after a slight delay. A secondary intent of this study was to determine if verbal retrieval attempts would enhance predictive accuracy. One half of participants made retrieval attempts during the second and third list-learning task, and the other half made retrieval attempts during the third list-learning task only. Measures of the correlation between JOL predictions and recall accuracy revealed that survivors were as accurate as controls when making delayed predictions and were less accurate when making immediate predictions. This occurred regardless of retrieval attempts. Absolute measures that compared mean JOL ratings to overall recall revealed that the survivor group was well-calibrated when making delayed JOL predictions but overestimated when making immediate JOL predictions. The non-injured control group underestimated when making both types of predictions. However, within-group variability was high. These findings are compared to those from studies that investigated metamemory beliefs in which survivors' ratings were compared to family-member ratings. Clinical implications for basing executive decisions about compensatory strategies on delayed and immediate predictions of future recall are discussed. Additionally, a rationale is provided for the use of both relative and absolute measures of predictive accuracy in metamemory studies involving neurological clinical populations.

Adult↗
PubMed1987

An evaluation of a computer program to predict the outcome of hyaline membrane disease.

A computerized model was developed to predict the severity of hyaline membrane disease. This study compared the program's predictive ability with that of three neonatologists. Blood gas and respiratory support data of 33 infants were entered into the computer which then predicted the severity of disease. The computer predicted 18 outcomes correctly. The neonatologists, provided with the same data, predicted 24 outcomes correctly. Both sets of predictions--computer and physician--found that patients predicted to have mild outcomes had the shortest oxygen requirements, duration of mechanical ventilation, and length of hospitalization, and infants predicted to have severe outcomes had the greatest oxygen requirements, duration of mechanical ventilation, and length of hospitalization. While actual severity was linked to birth weight, the model did not utilize birth weight in its predictive algorithm. Consequently, although the computer program predicted the outcomes with moderate success, it was less accurate than the neonatologists.

Blood Gas Analysis↗
PubMed2005

Prediction equation for head volume of Japanese young adults.

Accurate measurement of head volume is indispensable for precise assessments of body composition determined by hydrostatic weighing without head submersion. The purpose of this study was to establish a prediction equation for head volume measured by the immersion method from multiple regression analysis using head parameters (head circumference, head length, head breadth, neck girth and head thickness) as independent variables. The participants were 106 Japanese young adults (55 males and 51 females) aged 17-27 years. Intra-class correlation coefficients (ICCs) for each head parameter and head volume in males and females were very high (ICC = 0.993-0.999, 0.992-0.998). Head circumference was closely related to head volume measured by the immersion method (r = 0.719, 0.861, P < 0.05), and was the most important parameter for the prediction equation in both sexes. Head breadth was related poorly (r = 0.475, 0.500, P < 0.05) and showed a small individual difference. It was, therefore, excluded from the independent variables. The prediction equation for males was predicted head volume = 122.10X1 + 106.19X3 + 37.16X4 - 89.46X5 - 4754.93, R = 0.909, SEE = 121.75 ml, and that for females was predicted head volume = 213.83X1 + 45.24X3 + 36.85X4 - 74.34X5 - 8912.43, R = 0.913, SEE = 136.26 ml (where X1 = head circumference, X3 = head length, X4 = neck girth, X5 = head thickness, and SEE = standard error of the estimate). The limits of agreement for predicted and measured head volume were -234.5 to 234.1 ml for males, and -261.0 to 261.0 ml for females. In cross-validation groups of both sexes, there were no significant differences between measured head volume and predicted head volume. The correlation coefficients between measured head volume and predicted head volume in males and females were 0.894 and 0.908, respectively. The predicted head volume from prediction equations was considered to have high reliability and validity.

Adolescent↗
PubMed2006

Transactional skew and assured fitness return models fail to predict patterns of cooperation in wasps.

Cooperative breeders often exhibit reproductive skew, where dominant individuals reproduce more than subordinates. Two approaches derived from Hamilton's inclusive fitness model predict when subordinate behavior is favored over living solitarily. The assured fitness return (AFR) model predicts that subordinates help when they are highly likely to gain immediate indirect fitness. Transactional skew models predict dominants and subordinates "agree" on a level of reproductive skew that induces subordinates to join groups. We show the AFR model to be a special case of transactional skew models that assumes no direct reproduction by subordinates. We use data from 11 populations of four wasp species (Polistes, Liostenogaster) as a test of whether transactional frameworks suffice to predict when subordinate behavior should be observed in general and the specific level of skew observed in cooperative groups. The general prediction is supported; in 10 of 11 cases, transactional models correctly predict presence or absence of cooperation. In contrast, the specific prediction is not consistent with the data. Where cooperation occurs, the model accurately predicts highly biased reproductive skew between full sisters. However, the model also predicts that distantly related or unrelated females should cooperate with low skew. This prediction fails: cooperation with high skew is the observed norm. Neither the generalized transactional model nor the special-case AFR model can explain this significant feature of wasp sociobiology. Alternative, nontransactional hypotheses such as parental manipulation and kin recognition errors are discussed.

Animals↗
PubMed2004

Prediction of respiratory tumour motion for real-time image-guided radiotherapy.

Image guidance in radiotherapy and extracranial radiosurgery offers the potential for precise radiation dose delivery to a moving tumour. Recent work has demonstrated how to locate and track the position of a tumour in real-time using diagnostic x-ray imaging to find implanted radio-opaque markers. However, the delivery of a treatment plan through gating or beam tracking requires adequate consideration of treatment system latencies, including image acquisition, image processing, communication delays, control system processing, inductance within the motor, mechanical damping, etc. Furthermore, the imaging dose given over long radiosurgery procedures or multiple radiotherapy fractions may not be insignificant, which means that we must reduce the sampling rate of the imaging system. This study evaluates various predictive models for reducing tumour localization errors when a real-time tumour-tracking system targets a moving tumour at a slow imaging rate and with large system latencies. We consider 14 lung tumour cases where the peak-to-peak motion is greater than 8 mm, and compare the localization error using linear prediction, neural network prediction and Kalman filtering, against a system which uses no prediction. To evaluate prediction accuracy for use in beam tracking, we compute the root mean squared error between predicted and actual 3D motion. We found that by using prediction, root mean squared error is improved for all latencies and all imaging rates evaluated. To evaluate prediction accuracy for use in gated treatment, we present a new metric that compares a gating control signal based on predicted motion against the best possible gating control signal. We found that using prediction improves gated treatment accuracy for systems that have latencies of 200 ms or greater, and for systems that have imaging rates of 10 Hz or slower.

Algorithms↗
PubMed1994

Predicting alpha-helix and beta-strand segments of globular proteins.

All current methods of protein secondary structure prediction are based on evaluation of a single residue state. Although the accuracy of the best of them is approximately 60-70%, for reliable prediction of tertiary structure it is more useful to predict an approximate location of alpha-helix and beta-strand segments, especially prolonged ones. We have developed a simple method for protein secondary structure prediction which is oriented on the location of secondary structure segments. The method uses linear discriminant analysis to assign segments of a given amino acid sequence a particular type of secondary structure, by taking into account the amino acid composition of internal parts of segments as well as their terminal and adjacent regions. Four linear discriminant functions were constructed for recognition of short and long alpha-helix and beta-strand segments respectively. These functions combine three characteristics: hydrophobic moment, segment singlet, and pair preferences to an alpha-helix or beta-strand. The last two characteristics are calculated by summing the preference parameters of single residues and pairs of residues located in a segment and its adjacent regions. The final program SSP predicts all possible potential alpha-helices and beta-strands and resolves some possible overlap between them. Overall three-state (alpha, beta, c) prediction gives approximately 65.1% correctly predicted residues on 126 non-homologous proteins using the jackknife test procedure. Analysis of the prediction results shows a high prediction accuracy of long secondary structure segments (approximately 89% of alpha-helices of length > 8 and approximately 71% of beta-strands of length > 6 are correctly located with probability of correct prediction 0.82 and 0.78 respectively.(ABSTRACT TRUNCATED AT 250 WORDS)

Algorithms↗
PubMed1995

Protein secondary structure prediction by the analysis of variation and conservation in multiple alignments.

A number of methods exist for the prediction of protein secondary structure from primary sequence. One method identifies variable charged and conserved hydrophobic residues within large multiple alignments as a means of indicating outside and inside sites respectively in the protein structure. These sites are then manually fitted to secondary structure templates to generate a secondary structure prediction. Using the existing theoretical bases of this method, we present an algorithm (STAMA) which automatically carries out the initial variation/conservation analysis of the alignment. We also test the accuracy of complete predictions carried out by manual fitting of the STAMA-derived assignments to structure templates, using five large multiple alignments each including a protein of known structure. The method was found on average to predict only 57% of residues in the correct secondary structure, and was only as accurate as predictions carried out using the established and automated method of Garnier, Osguthorpe and Robson (1978) applied to a single sequence. When used in conjunction with other secondary structure prediction methods, however, the resulting consensus predictions were found to be very accurate, with 78% of the elements (alpha helices or beta strands) for which a consensus could be obtained being predicted correctly. The algorithm presented here, plus the assessment of the accuracy of prediction generated by this method, should enable this predictive approach to receive informed general use.

Algorithms↗
PubMed2002

NETASA: neural network based prediction of solvent accessibility.

MOTIVATION: Prediction of the tertiary structure of a protein from its amino acid sequence is one of the most important problems in molecular biology. The successful prediction of solvent accessibility will be very helpful to achieve this goal. In the present work, we have implemented a server, NETASA for predicting solvent accessibility of amino acids using our newly optimized neural network algorithm. Several new features in the neural network architecture and training method have been introduced, and the network learns faster to provide accuracy values, which are comparable or better than other methods of ASA prediction. RESULTS: Prediction in two and three state classification systems with several thresholds are provided. Our prediction method achieved the accuracy level upto 90% for training and 88% for test data sets. Three state prediction results provide a maximum 65% accuracy for training and 63% for the test data. Applicability of neural networks for ASA prediction has been confirmed with a larger data set and wider range of state thresholds. Salient differences between a linear and exponential network for ASA prediction have been analysed. AVAILABILITY: Online predictions are freely available at: http://www.netasa.org. Linux ix86 binaries of the program written for this work may be obtained by email from the corresponding author.

Algorithms↗
PubMed2003

Local structure prediction with local structure-based sequence profiles.

MOTIVATION: A large body of experimental and theoretical evidence suggests that local structural determinants are frequently encoded in short segments of protein sequence. Although the local structural information, once recognized, is particularly useful in protein structural and functional analyses, it remains a difficult problem to identify embedded local structural codes based solely on sequence information. RESULTS: In this paper, we describe a local structure prediction method aiming at predicting the backbone structures of nine-residue sequence segments. Two elements are the keys for this local structure prediction procedure. The first key element is the LSBSP1 database, which contains a large number of non-redundant local structure-based sequence profiles for nine-residue structure segments. The second key element is the consensus approach, which identifies a consensus structure from a set of hit structures. The local structure prediction procedure starts by matching a query sequence segment of nine consecutive amino acid residues to all the sequence profiles in the local structure-based sequence profile database (LSBSP1). The consensus structure, which is at the center of the largest structural cluster of the hit structures, is predicted to be the native state structure adopted by the query sequence segment. This local structure prediction method is assessed with a large set of random test protein structures that have not been used in constructing the LSBSP1 database. The benchmark results indicate that the prediction capacities of the novel local structure prediction procedure exceed the prediction capacities of the local backbone structure prediction methods based on the I-sites library by a significant margin. AVAILABILITY: All the computational and assessment procedures have been implemented in the integrated computational system PrISM.1 (Protein Informatics System for Modeling). The system and associated databases for LINUX systems can be downloaded from the website: http://www.columbia.edu/~ay1/.

Amino Acid Sequence↗
PubMed2004

Using functional and organizational information to improve genome-wide computational prediction of transcription units on pathway-genome databases.

MOTIVATION: The prediction of transcription units (TUs, which are similar to operons) is an important problem that has been tackled using many different approaches. The availability of complete microbial genomes has made genome-wide TU predictions possible. Pathway-genome databases (PGDBs) add metabolic and other organizational (i.e. protein complexes) information to the annotated genome, and are able to capture TU organization information. These characteristics of PGDBs make them a suitable framework for the development and implementation of TU predictors. RESULTS: We implemented a TU predictor that uses only intergenic distance and functional classification of genes to predict TU boundaries, and applied it to EcoCyc, our PGDB of Escherichia coli. To this original predictor, we added information on metabolic pathways, protein complexes and transporters, all readily available in EcoCyc, in order to generate an enhanced predictor. The enhanced predictor correctly predicted 80% of the known E.coli TUs (69% of the known operons), a moderate improvement over the original predictor's performance (75% of TUs and 65% of operons correctly predicted), demonstrating that the extra information available in the PGDB does indeed improve prediction performance. Performance of this E.coli-based predictor on a genome other than that of E.coli was tested on BsubCyc, our computationally generated PGDB for Bacillus subtilis, for which a set of 100 known operons is available. Prediction accuracy decreased substantially (46% of the known operons correctly predicted). This was due in part to missing information in BsubCyc, which prevented full use of the predictor's features. The augmented predictor has been implemented as part of our Pathway Tools software suite, and can be used to populate a PGDB with predicted TUs. AVAILABILITY: The TU predictor is included in version 7.0 of the Pathway Tools software suite. Pathway Tools 7.0 is available free of charge to academic institutions and for a fee to commercial enterprises. It runs on Sun Solaris 8, Linux and Windows. TUs predicted on the Caulobacter crescentus and Mycobacterium tuberculosis (H37Rv) genomes are available in our CauloCyc and MtbrvCyc databases, available at the BioCyc web site (http://biocyc.org). To obtain version 7.0 of Pathway Tools, follow the directions in our web site, http://biocyc.org/download.shtml.

Algorithms↗
PubMed2004

Predicting genetic regulatory response using classification.

MOTIVATION: Studying gene regulatory mechanisms in simple model organisms through analysis of high-throughput genomic data has emerged as a central problem in computational biology. Most approaches in the literature have focused either on finding a few strong regulatory patterns or on learning descriptive models from training data. However, these approaches are not yet adequate for making accurate predictions about which genes will be up- or down-regulated in new or held-out experiments. By introducing a predictive methodology for this problem, we can use powerful tools from machine learning and assess the statistical significance of our predictions. RESULTS: We present a novel classification-based method for learning to predict gene regulatory response. Our approach is motivated by the hypothesis that in simple organisms such as Saccharomyces cerevisiae, we can learn a decision rule for predicting whether a gene is up- or down-regulated in a particular experiment based on (1) the presence of binding site subsequences ('motifs') in the gene's regulatory region and (2) the expression levels of regulators such as transcription factors in the experiment ('parents'). Thus, our learning task integrates two qualitatively different data sources: genome-wide cDNA microarray data across multiple perturbation and mutant experiments along with motif profile data from regulatory sequences. We convert the regression task of predicting real-valued gene expression measurements to a classification task of predicting +1 and -1 labels, corresponding to up- and down-regulation beyond the levels of biological and measurement noise in microarray measurements. The learning algorithm employed is boosting with a margin-based generalization of decision trees, alternating decision trees. This large-margin classifier is sufficiently flexible to allow complex logical functions, yet sufficiently simple to give insight into the combinatorial mechanisms of gene regulation. We observe encouraging prediction accuracy on experiments based on the Gasch S.cerevisiae dataset, and we show that we can accurately predict up- and down-regulation on held-out experiments. We also show how to extract significant regulators, motifs and motif-regulator pairs from the learned models for various stress responses. Our method thus provides predictive hypotheses, suggests biological experiments, and provides interpretable insight into the structure of genetic regulatory networks. AVAILABILITY: The MLJava package is available upon request to the authors. Supplementary: Additional results are available from http://www.cs.columbia.edu/compbio/geneclass

Binding Sites↗
PubMed2005

pTARGET [corrected] a new method for predicting protein subcellular localization in eukaryotes.

MOTIVATION: There is a scarcity of efficient computational methods for predicting protein subcellular localization in eukaryotes. Currently available methods are inadequate for genome-scale predictions with several limitations. Here, we present a new prediction method, pTARGET that can predict proteins targeted to nine different subcellular locations in the eukaryotic animal species. RESULTS: The nine subcellular locations predicted by pTARGET include cytoplasm, endoplasmic reticulum, extracellular/secretory, golgi, lysosomes, mitochondria, nucleus, plasma membrane and peroxisomes. Predictions are based on the location-specific protein functional domains and the amino acid compositional differences across different subcellular locations. Overall, this method can predict 68-87% of the true positives at accuracy rates of 96-99%. Comparison of the prediction performance against PSORT showed that pTARGET prediction rates are higher by 11-60% in 6 of the 8 locations tested. Besides, the pTARGET method is robust enough for genome-scale prediction of protein subcellular localizations since, it does not rely on the presence of signal or target peptides. AVAILABILITY: A public web server based on the pTARGET method is accessible at the URL http://bioinformatics.albany.edu/~ptarget. Datasets used for developing pTARGET can be downloaded from this web server. Source code will be available on request from the corresponding author.

Algorithms↗
PubMed2004

Doctors' prediction of certified sickness absence.

BACKGROUND: Doctors' ability to predict the duration of their patients' certified sickness absence is important for follow-up efforts aimed at patients with increased probability of long-term absence. OBJECTIVES: The aim of this study was to examine the accuracy of doctors' predictions of their patients' sickness absence status 4 weeks ahead, and which factors were associated with it. METHODS: A questionnaire survey was carried out in primary health care concerning 796 patients certified sick within 140 days after the start of absence. The episodes of absence were labelled short-standing (up to 2 weeks) and long-standing (from 3 to 20 weeks), at the time of consultation. The doctors' prediction of the patients' absence status 4 weeks ahead, diagnoses, work ability, clinical information sources used and the presence of non-medical factors that could have influenced the doctors' work ability assessments were collected. The predictions were compared with the patients' absence status 4 weeks later by positive predictive values (PPVs) for the statements 'returned to work' and 'still certified sick'. Factors associated with the accuracy of the predictions were analysed by multiple logistic regression analyses. RESULTS: The doctors accurately predicted return to work in 84% [95% confidence interval (CI) 79-87] of the cases in short-standing episodes, and in 53% (43-62) in long-standing episodes. The corresponding PPVs for still certified sick were 72% (62-80) and 91% (85-94). In short-standing episodes, the doctors' probability of making accurate predictions was higher for respiratory disorders [odds ratio (OR) 2.84; 95% CI 1.36-5.90], than for the reference category 'all other disorders', and lower for mental disorders (0.46; 0.24-0.89). In long-standing episodes, the probability was lower for musculoskeletal disorders (0.33; 0.12-0.86) and injuries (0.12; 0.03-0.48). Neither the age nor gender of patients or doctors, nor the degree of work ability reduction, nor other factors were associated with the accuracy of the predictions. CONCLUSIONS: The doctors' predictions were highly accurate for return to work in short-standing episodes, and for still certified sick in long-standing episodes. Diagnoses were associated with the accuracy; other factors, including the doctors' work ability assessments, were not.

Adolescent↗
PubMed1995

"Well-determined" regions in RNA secondary structure prediction: analysis of small subunit ribosomal RNA.

Recent structural analyses of genomic RNAs from RNA coliphages suggest that both well-determined base paired helices and well-determined structural domains that are identified by "energy dot plot" analysis using the RNA folding package mfold, are likely to be predicted correctly. To test these observations with another group of large RNAs, we have analyzed 15 ribosomal RNAs. Published secondary structure models that were derived by comparative sequence analysis were used to evaluate the predicted structures. Both the optimal predicted fold and the predicted "energy dot plot" of each sequence were examined. Each prediction was obtained from a single computer run on an entire ribosomal RNA sequence. All predicted base pairs in optimal foldings were examined for agreement with proven base pairs in the comparative models. Our analyses show that the overall correspondence between the predicted and comparative models varied for different RNAs and ranges from a low of 27% to high of 70%, with a mean value of 49%. The correspondence improves to a mean value of 81% when the analysis is limited to well-determined helices. In addition to well-determined helices, large well-determined structural domains can be observed in "energy dot plots" of some 16S ribosomal RNAs. The predicted domains correspond closely with structural domains that are found by the comparative method in the same RNAs. Our analyses also show that measuring the agreement between predicted and comparative secondary structure models underestimates the reliability of structural prediction by mfold.

Base Sequence↗
PubMed2003

EVA: Evaluation of protein structure prediction servers.

EVA (http://cubic.bioc.columbia.edu/eva/) is a web server for evaluation of the accuracy of automated protein structure prediction methods. The evaluation is updated automatically each week, to cope with the large number of existing prediction servers and the constant changes in the prediction methods. EVA currently assesses servers for secondary structure prediction, contact prediction, comparative protein structure modelling and threading/fold recognition. Every day, sequences of newly available protein structures in the Protein Data Bank (PDB) are sent to the servers and their predictions are collected. The predictions are then compared to the experimental structures once a week; the results are published on the EVA web pages. Over time, EVA has accumulated prediction results for a large number of proteins, ranging from hundreds to thousands, depending on the prediction method. This large sample assures that methods are compared reliably. As a result, EVA provides useful information to developers as well as users of prediction methods.

Automation↗