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At least 19 recordsLinked to original sources
PubMed2000

A cell-kinetic model of CD34+ cell mobilization and harvest: development of a predictive algorithm for CD34+ cell yield in PBPC collections.

BACKGROUND: Mobilization and homing of PBPCs are still poorly understood. Thus, a sufficient algorithm for the prediction of PBPC yield in apheresis procedures does not yet exist. STUDY DESIGN AND METHODS: The decline of CD34+ cells in the peripheral blood during apheresis and their simultaneous increase in the collection bag were determined in a prospective study of 18 consecutive apheresis procedures. A cell-kinetic, four-compartment model describing these changes was developed. Retrospective data from 136 apheresis procedures served to further improve this model. A predictive algorithm for the yield was developed that considered the sex, weight, and height of the patient, the number of CD34+ cells in peripheral blood before apheresis, the inlet flow, and the duration of the apheresis. The accuracy of this algorithm was evaluated by comparison of the predicted and the observed yields of CD34+ cells in 105 prospective autologous and 148 retrospective allogeneic apheresis procedures. RESULTS: The correlation between predicted and observed yields was good for the autologous and allogeneic groups with a correlation coefficient (r) of 0.8979 and 0.8311 (p<0.0001), respectively. The regression is described by the equations log (measured value [m]) = 1.0118 + 0.8595 x log (predicted value [p]) for the autologous and log (m) = 2.226 + 0.7559 x log (p) for the allogeneic group. The respective equations for the zero-point regression are log (m) = 1.014 x log (p) and log (m) = 1.026 x log (p). The probability that the measured value was 90 percent or more of the predicted value was 83.8 percent for the autologous and 90.5 percent for the allogeneic apheresis procedures. CONCLUSION: The predictive accuracy of the algorithm and the slope of the zero-point regression curve were higher for allogeneic than autologous PBPC collections. The predictive algorithm may be a useful tool in PBPC harvest, enabling the adaptation of the size of the apheresis to the needs of each patient.

Algorithms↗
PubMed2005

A simple prediction algorithm for bacteraemia in patients with acute febrile illness.

BACKGROUND: Existing prediction models for the risk of bacteraemia are complex and difficult to use. Physicians are likely to use a model only if it is simple and sensitive. AIM: To develop a simple classification algorithm predicting the risk of bacteraemia. DESIGN: Hospital-based study. METHODS: We enrolled 526 adult consecutive patients with acute febrile illness (40 with bacteraemia) presenting to the emergency department at a community hospital in Okinawa, Japan. Recursive partitioning analysis was used to build the classification algorithm with V-fold cross-validation. We used two clinical scenarios: in the first, laboratory tests were not available; in the second, they were. RESULTS: The two prediction algorithms generated three different risk groups for bacteraemia. In the first scenario, the important variables were chills, pulse, and physician diagnosis of a low-risk site. The low-risk group from this first algorithm included 68% of the total patients; sensitivity was 87.5% and the misclassification rate was 1.4% (5/358). In the second scenario, the important variables were chills, C-reactive protein, and physician diagnosis of a low-risk site. The low-risk group for the second algorithm included 62% of the total patients; sensitivity was 92.5% and misclassification rate 0.9% (3/328). The algorithms had negative predictive values of 98.6% (first scenario) and 99.1% (second). DISCUSSION: This simple and sensitive prediction algorithm may be useful for identifying patients at low risk of bacteraemia. Prospective validation is needed in other settings.

Acute Disease↗
PubMed1995

A predictive algorithm for skin permeability: the effects of molecular size and hydrogen bond activity.

PURPOSE: To develop a predictive algorithm of nonelectrolyte transport through skin based upon a partitioning-diffusion model. METHODS: Drug permeability is described by a partitioning-diffusion equation. Through free-energy relationships, partitioning is related to the drug's molecular volume (MV), and hydrogen bond donor (Hd) and acceptor (Ha) activity. Diffusion is related to the drug's MV using a theory of diffusion through lipid lamellae based on free-volume fluctuations within the lipid domain. These two explicit descriptions are combined to give an equation describing permeability in terms of the permeant's physical properties. The aqueous permeability coefficients of 37 nonelectrolytes through human epidermis were evaluated as a function of these physical properties using a multiple regression analysis. RESULT: The results of the regression analysis show that 94% of the variability in the data can be explained by a model which includes only the permeant's MV, Hd and Ha. These results further provide an algorithm to predict skin permeability based upon the values of these parameters. In addition, the relative contribution of various chemical functional groups (e.g., -COOH) is derived, and can be used to predict skin transport from drug structure alone. CONCLUSIONS: A biophysically relevant model of drug transport through human skin is derived based solely on the physical properties of the drug. The model provides an algorithm to predict permeability from the drug's structure and/or physical properties. Moreover, the model is applicable to a number of lipid barrier membranes, suggesting a common transport mechanism in all.

Algorithms↗
PubMed1991

New joint prediction algorithm (Q7-JASEP) improves the prediction of protein secondary structure.

The classical problem of secondary structure prediction is approached by a new joint algorithm (Q7-JASEP) that combines the best aspects of six different methods. The algorithm includes the statistical methods of Chou-Fasman, Nagano, and Burgess-Ponnuswamy-Scheraga, the homology method of Nishikawa, the information theory method of Garnier-Osgurthope-Robson, and the artificial neural network approach of Qian-Sejnowski. Steps in the algorithm are (i) optimizing each individual method with respect to its correlation coefficient (Q7) for assigning a structural type from the predictive score of the method, (ii) weighting each method, (iii) combining the scores from different methods, and (iv) comparing the scores for alpha-helix, beta-strand, and coil conformational states to assign the secondary structure at each residue position. The present application to 45 globular proteins demonstrates good predictive power in cross-validation testing (with average correlation coefficients per test protein of Q7, alpha = 0.41, Q7, beta = 0.47, Q7,c = 0.41 for alpha-helix, beta-strand, and coil conformations). By the criterion of correlation coefficient (Q7) for each type of secondary structure, Q7-JASEP performs better than any of the component methods. When all protein classes are included for training and testing (by cross-validation), the results here equal the best in the literature, by the Q7 criterion. More generally, the basic algorithm can be applied to any protein class and to any type of structure/sequence or function/sequence correlation for which multiple predictive methods exist.

Algorithms↗
PubMed2004

Differentiation between septic arthritis and transient synovitis of the hip in children with clinical prediction algorithms.

BACKGROUND: Differentiation between septic arthritis and transient synovitis of the hip in children can be difficult. Kocher et al. recently developed a clinical prediction algorithm for septic arthritis based on four clinical variables: history of fever, non-weight-bearing, an erythrocyte sedimentation rate of >or=40 mm/hr, and a serum white blood-cell count of >12000/mm(3) (>12.0 x 10(9)/L). The purpose of this study was to apply this clinical algorithm retrospectively to determine its predictive value in our patient population. METHODS: A retrospective review was performed to identify all children who had undergone a hip arthrocentesis for the evaluation of an irritable hip at our institution between 1992 and 2000. One hundred and sixty-three patients with 165 involved hips satisfied the criteria for inclusion in the study and were classified as having true septic arthritis (twenty hips), presumed septic arthritis (twenty-seven hips), or transient synovitis (118 hips). RESULTS: Patients with septic arthritis (true and presumed; forty-seven hips) differed significantly (p < 0.05) from patients with transient synovitis (118 hips) with regard to the erythrocyte sedimentation rate, differential of serum white blood-cell count, total white blood-cell count and differential in the synovial fluid, gender, previous health-care visits, and history of fever. If the four independent multivariate predictors of septic arthritis proposed by Kocher et al. were present, the predicted probability of the patient having septic arthritis was 59% in our study, in contrast to the 99.6% predicted probability in the patient population described by Kocher et al. Statistical analyses demonstrated that the best model to describe our patient population was based on three variables: a history of fever, a serum total white blood-cell count of >12000/mm(3) (>12.0 x 10(9)/L), and a previous health-care visit. When all three variables were present, the predicted probability of the patient having septic arthritis was 71%. CONCLUSIONS: Although the use of a clinical prediction algorithm to differentiate between septic arthritis and transient synovitis may have improved the utility of existing technology and medical care to facilitate the diagnosis at the institution at which the algorithm originated, application of the algorithm proposed by Kocher et al. or of our three-variable model does not appear to be valid at other institutions.

Algorithms↗
PubMed2005

Weighted sequence motifs as an improved seeding step in microRNA target prediction algorithms.

We present a new microRNA target prediction algorithm called TargetBoost, and show that the algorithm is stable and identifies more true targets than do existing algorithms. TargetBoost uses machine learning on a set of validated microRNA targets in lower organisms to create weighted sequence motifs that capture the binding characteristics between microRNAs and their targets. Existing algorithms require candidates to have (1) near-perfect complementarity between microRNAs' 5' end and their targets; (2) relatively high thermodynamic duplex stability; (3) multiple target sites in the target's 3' UTR; and (4) evolutionary conservation of the target between species. Most algorithms use one of the two first requirements in a seeding step, and use the three others as filters to improve the method's specificity. The initial seeding step determines an algorithm's sensitivity and also influences its specificity. As all algorithms may add filters to increase the specificity, we propose that methods should be compared before such filtering. We show that TargetBoost's weighted sequence motif approach is favorable to using both the duplex stability and the sequence complementarity steps. (TargetBoost is available as a Web tool from http://www.interagon.com/demo/.).

5' Untranslated Regions↗
PubMed2003

An analysis of the Candida albicans genome database for soluble secreted proteins using computer-based prediction algorithms.

We sought to identify all genes in the Candida albicans genome database whose deduced proteins would likely be soluble secreted proteins (the secretome). While certain C. albicans secretory proteins have been studied in detail, more data on the entire secretome is needed. One approach to rapidly predict the functions of an entire proteome is to utilize genomic database information and prediction algorithms. Thus, we used a set of prediction algorithms to computationally define a potential C. albicans secretome. We first assembled a validation set of 47 C. albicans proteins that are known to be secreted and 47 that are known not to be secreted. The presence or absence of an N-terminal signal peptide was correctly predicted by SignalP version 2.0 in 47 of 47 known secreted proteins and in 47 of 47 known non-secreted proteins. When all 6165 C. albicans ORFs from CandidaDB were analysed with SignalP, 495 ORFs were predicted to encode proteins with N-terminal signal peptides. In the set of 495 deduced proteins with N-terminal signal peptides, 350 were predicted to have no transmembrane domains (or a single transmembrane domain at the extreme N-terminus) and 300 of these were predicted not to be GPI-anchored. TargetP was used to eliminate proteins with mitochondrial targeting signals, and the final computationally-predicted C. albicans secretome was estimated to consist of up to 283 ORFs. The C. albicans secretome database is available at http://info.med.yale.edu/intmed/infdis/candida/

Algorithms↗
PubMed2005

An epileptic seizure prediction algorithm based on second-order complexity measure.

The quality of life of many epilepsy patients may be improved significantly if the occurrence of epileptic seizures can be successfully forecasted and clinical intervention, such as electrical stimulation or drug delivery, can then be used to suppress their emergence, or warn the patient of the forthcoming events. In this paper, a prediction algorithm based on the second-order complexity measure was proposed to predict the impending seizures. Through the analysis of long-term intracranial EEG recordings from two frontal lobe epilepsy patients, the results indicated that the sensitivity of prediction was 77.8% (14/18) and 66.7% (4/6) and the number of false warnings was 3 and 2 for the two patients, respectively. Because only the information of past seizures was utilized to predict the current seizure and the computation load was low, the prediction algorithm could possibly be applied to clinical practice.

Adolescent↗
PubMed1995

The Riyadh Intensive Care Program mortality prediction algorithm assessed in 617 intensive care patients in Glasgow.

The hospital mortality prediction algorithm from the Riyadh Intensive Care Program was assessed in 617 general intensive care unit patients and the results were compared with APACHE II admission scoring. Of the 119 patients predicted to die by the Riyadh Intensive Care Program, 24 (20.2%) recovered sufficiently to be discharged home. The overall false-positive rate was 5.2%, the false predictions mostly occurring in the trended component, the admission component having similar performance to admission APACHE II. For equivalent specificity the Riyadh Intensive Care Program was more sensitive than admission APACHE II risk of death figures, but the very high false-positive rate in those predicted to die precludes the algorithm's use in patient management decisions. In our view, the Riyadh Intensive Care Program does not justify the considerable extra work involved in data collection and processing over current admission scoring systems.

APACHE↗
PubMed2006

Predictability analysis for an automated seizure prediction algorithm.

Epileptic seizures of mesial temporal origin are preceded by changes in signal properties detectable in the intracranial EEG. A series of computer algorithms designed to detect the changes in spatiotemporal dynamics of the EEG signals and to warn of impending seizures have been developed. In this study, we evaluated the performance of a novel adaptive threshold seizure warning algorithm (ATSWA), which detects the convergence in Short-Term Maximum Lyapunov Exponent (STLmax) values among critical intracranial EEG electrode sites, as a function of different seizure warning horizons (SWHs). The ATSWA algorithm was compared to two statistical based naïve prediction algorithms (periodic and random) that do not employ EEG information. For comparison purposes, three performance indices "area above ROC curve" (AAC), "predictability power" (PP) and "fraction of time under false warnings" (FTF) were defined and the effect of SWHs on these indices was evaluated. The results demonstrate that this EEG based seizure warning method performed significantly better (P < 0.05) than both naïve prediction schemes. Our results also show that the performance indexes are dependent on the length of the SWH. These results suggest that the EEG based analysis has the potential to be a useful tool for seizure warning.

Adult↗
PubMed2006

Analysis of algorithms predicting blood:air and tissue:blood partition coefficients from solvent partition coefficients for prevalent components of JP-8 jet fuel.

Algorithms predicting tissue and blood partition coefficients (PCs) from solvent properties were compared to assess their usefulness in a petroleum mixture physiologically based pharmacokinetic/pharmacodynamic model. Measured blood:air and tissue:blood PCs for rat and human tissues were sought from literature resources for 14 prevalent jet fuel (JP-8) components. Average experimental PCs were compared with predicted PCs calculated using algorithms from 9 published sources. Algorithms chosen used solvent PCs (octanol:water, saline or water:air, oil:air coefficients) due to the relative accessibility of these parameters. Tissue:blood PCs were calculated from ratios of predicted tissue:air and experimental blood:air values (PCEB). Of the 231 calculated values, 27% performed within +/- 20% of the experimental PC values. Physiologically based equations (based on water and lipid components of a tissue type) did not perform as well as empirical equations (derived from linear regression of experimental PC data) and hybrid equations (physiological parameters and empirical factors combined) for the jet fuel components. The major limitation encountered in this analysis was the lack of experimental data for the selected JP-8 constituents. PCEB values were compared with tissue:blood PCs calculated from ratios of predicted tissue:air and predicted blood:air values (PCPB). Overall, 68% of PCEB values had smaller absolute % errors than PCPB values. If calculated PC values must be used in models, a comparison of experimental and predicted PCs for chemically similar compounds would estimate the expected error level in calculated values.

Adipose Tissue↗
PubMed1995

Two novel T cell epitope prediction algorithms based on MHC-binding motifs; comparison of predicted and published epitopes from Mycobacterium tuberculosis and HIV protein sequences.

We have designed two computer-based algorithms for T cell epitope prediction, OptiMer and EpiMer, which incorporate current knowledge of MHC-binding motifs. OptiMer locates amphipathic segments of protein antigens with a high density of MHC-binding motifs. EpiMer identifies peptides with a high density of MHC-binding motifs alone. These algorithms exploit the striking tendency for MHC-binding motifs to cluster within short segments of each protein. Putative epitopes predicted by these algorithms contain motifs corresponding to many different MHC alleles, and may contain both class I and class II motifs, features thought to be ideal for the peptide components of synthetic subunit vaccines. In this study, we describe the use of OptiMer and EpiMer for the prediction of putative T cell epitopes from Mycobacterium tuberculosis and human immunodeficiency virus protein antigens, and demonstrate that these two algorithms may provide sensitive and efficient means for the prediction of promiscuous T cell epitopes that may be critical to the development of vaccines against these and other pathogens.

Algorithms↗
PubMed1994

Mapping of linear antigenic sites on the S glycoprotein of a neurotropic murine coronavirus with synthetic peptides: a combination of nine prediction algorithms fails to identify relevant epitopes and peptide immunogenicity is drastically influenced by the nature of the protein carrier.

The elucidation of the antigenic structure of the S glycoprotein of murine coronaviruses will provide further understanding of the complex pathogenicity of these viruses. In order to identify linear antigenic determinants, the primary structure of the S glycoprotein of murine hepatitis virus strain A59 was analyzed with a combination of nine epitope prediction algorithms. Fifteen potential epitopes were synthesized chemically and injected into BALB/c mice to study their biological relevance. This approach failed to identify novel important epitopes. Furthermore, the algorithms were unable to identify as antigenic the previously mapped immunodominant epitope A [C. Daniel, R. Anderson, M. J. Buchmeier, J. O. Fleming, W. J. M. Spaan, H. Wege, and Talbot, P. J. (1993). J. Virol. 67, 1185-1194]. Interestingly, peptide A coupled to KLH induced an immune response that simulated the immune response induced by the corresponding region of the protein much more accurately than when the same peptide was coupled to BSA. This included drastically enhanced competition with monoclonal antibodies and protection from virus challenge. These findings emphasize the shortcomings of amino acid sequence-based epitope prediction algorithms and demonstrate the critical importance of the carrier when synthetic peptides are considered as potential vaccines.

Amino Acid Sequence↗
PubMed2001

PAProC: a prediction algorithm for proteasomal cleavages available on the WWW.

The first version of PAProC (Prediction Algorithm for Proteasomal Cleavages) is now available to the general public. PAProC is a prediction tool for cleavages by human and yeast proteasomes, based on experimental cleavage data. It will be particularly useful for immunologists working on antigen processing and the prediction of major histocompatibility complex class I molecule (MHC I) ligands and cytotoxic T-lymphocyte (CTL) epitopes. Likewise, in cases in which proteasomal protein degradation has been indicated in disease, PAProC can be used to assess the general cleavability of disease-linked proteins. On its web site (http://www.paproc.de), background information and hyperlinks are provided for the user (e.g., to SYFPEITHI, the database for the prediction of MHC I ligands).

Algorithms↗
PubMed2000

Assessing the accuracy of prediction algorithms for classification: an overview.

We provide a unified overview of methods that currently are widely used to assess the accuracy of prediction algorithms, from raw percentages, quadratic error measures and other distances, and correlation coefficients, and to information theoretic measures such as relative entropy and mutual information. We briefly discuss the advantages and disadvantages of each approach. For classification tasks, we derive new learning algorithms for the design of prediction systems by directly optimising the correlation coefficient. We observe and prove several results relating sensitivity and specificity of optimal systems. While the principles are general, we illustrate the applicability on specific problems such as protein secondary structure and signal peptide prediction.

Algorithms↗
PubMed2005

Automatic intraaortic balloon pump timing using an intrabeat dicrotic notch prediction algorithm.

PURPOSE: The efficacy of intraaortic balloon counterpulsation (IABP) during arrhythmic episodes is questionable. A novel algorithm for intrabeat prediction of the dicrotic notch was used for real time IABP inflation timing control. DESCRIPTION: A windkessel model algorithm was used to calculate real-time aortic flow from aortic pressure. The dicrotic notch was predicted using a percentage of calculated peak flow. Automatic inflation timing was set at intrabeat predicted dicrotic notch and was combined with automatic IAB deflation. EVALUATION: Prophylactic IABP was applied in 27 patients with low ejection fraction (< 35%) undergoing cardiac surgery. Analysis of IABP at a 1:4 ratio revealed that IAB inflation occurred at a mean of 0.6 +/- 5 ms from the dicrotic notch. In all patients accurate automatic timing at a 1:1 assist ratio was performed. Seventeen patients had episodes of severe arrhythmia, the novel IABP inflation algorithm accurately assisted 318 of 320 arrhythmic beats at a 1:1 ratio. CONCLUSIONS: The novel real-time intrabeat IABP inflation timing algorithm performed accurately in all patients during both regular rhythms and severe arrhythmia, allowing fully automatic intrabeat IABP timing.

Aged↗
PubMed2004

Measure profile surrogates: a method to validate the performance of epileptic seizure prediction algorithms.

In a growing number of publications it is claimed that epileptic seizures can be predicted by analyzing the electroencephalogram (EEG) with different characterizing measures. However, many of these studies suffer from a severe lack of statistical validation. Only rarely are results passed to a statistical test and verified against some null hypothesis H0 in order to quantify their significance. In this paper we propose a method to statistically validate the performance of measures used to predict epileptic seizures. From measure profiles rendered by applying a moving-window technique to the electroencephalogram we first generate an ensemble of surrogates by a constrained randomization using simulated annealing. Subsequently the seizure prediction algorithm is applied to the original measure profile and to the surrogates. If detectable changes before seizure onset exist, highest performance values should be obtained for the original measure profiles and the null hypothesis. "The measure is not suited for seizure prediction" can be rejected. We demonstrate our method by applying two measures of synchronization to a quasicontinuous EEG recording and by evaluating their predictive performance using a straightforward seizure prediction statistics. We would like to stress that the proposed method is rather universal and can be applied to many other prediction and detection problems.

Algorithms↗
PubMed2004

An efficient prediction algorithm for image vector quantization.

In this paper, we propose a novel efficient fuzzy prediction algorithm for image vector quantization by using the fact that the pixel values of a block is highly related to those of its adjacent blocks in the same image frame because of its spatial correlation. The experimental results show that our method performs better than other VQ algorithms, such as SOC-VQ, LCIC-VQ, STC-VQ, and DST-VQ, in terms of computational complexity and coding efficiency.

Journal Article↗