[Design of an algorithm for predicting successful performance of operators with little training selection].
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Algorithms↗
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Insight into the structure of amyloid fibrils from the analysis of globular proteins.
The conversion from soluble states into cross-beta fibrillar aggregates is a property shared by many different proteins and peptides and was hence conjectured to be a generic feature of polypeptide chains. Increasing evidence is now accumulating that such fibrillar assemblies are generally characterized by a parallel in-register alignment of beta-strands contributed by distinct protein molecules. Here we assume a universal mechanism is responsible for beta-structure formation and deduce sequence-specific interaction energies between pairs of protein fragments from a statistical analysis of the native folds of globular proteins. The derived fragment-fragment interaction was implemented within a novel algorithm, prediction of amyloid structure aggregation (PASTA), to investigate the role of sequence heterogeneity in driving specific aggregation into ordered self-propagating cross-beta structures. The algorithm predicts that the parallel in-register arrangement of sequence portions that participate in the fibril cross-beta core is favoured in most cases. However, the antiparallel arrangement is correctly discriminated when present in fibrils formed by short peptides. The predictions of the most aggregation-prone portions of initially unfolded polypeptide chains are also in excellent agreement with available experimental observations. These results corroborate the recent hypothesis that the amyloid structure is stabilised by the same physicochemical determinants as those operating in folded proteins. They also suggest that side chain-side chain interaction across neighbouring beta-strands is a key determinant of amyloid fibril formation and of their self-propagating ability.
Amino Acid Sequence↗
Prediction of siRNA knockdown efficiency using artificial neural network models.
Selective knockdown of gene expression by short interference RNAs (siRNAs) has allowed rapid validation of gene functions and made possible a high throughput, genome scale approach to interrogate gene function. However, randomly designed siRNAs display different knockdown efficiencies of target genes. Hence, various prediction algorithms based on siRNA functionality have recently been constructed to increase the likelihood of selecting effective siRNAs, thereby reducing the experimental cost. Toward this end, we have trained three Back-propagation and Bayesian neural network models, previously not used in this context, to predict the knockdown efficiencies of 180 experimentally verified siRNAs on their corresponding target genes. Using our input coding based primarily on RNA structure thermodynamic parameters and cross-validation method, we showed that our neural network models outperformed most other methods and are comparable to the best predicting algorithm thus far published. Furthermore, our neural network models correctly classified 74% of all siRNAs into different efficiency categories; with a correlation coefficient of 0.43 and receiver operating characteristic curve score of 0.78, thus highlighting the potential utility of this method to complement other existing siRNA classification and prediction schemes.
Algorithms↗
[Application of artificial neural network based on the genetic algorithm in predicting the root distribution of winter wheat].
In this study, a controlled experiment of winter wheat under water stress at the seedling stage was conducted in soil columns in greenhouse. Based on the data gotten from the experiment, a model to estimate root length density distribution was developed through optimizing the weights of neural network by genetic algorithm. The neural network model was constructed by using forward neural network framework, by applying the strategy of the roulette wheel selection and reserving the most optimizing series of weights, which were composed by real codes. This model was applied to predict the root length density distribution of winter wheat, and the predicted root length density had good agreement with experiment data. The way could save a lot of manpower and material resources for determining the root length density distribution of winter wheat.
Algorithms↗
An updated comparison of drug dosing methods. Part I: Phenytoin.
The relationship between a dose of phenytoin and the resultant serum concentration is difficult to predict, and numerous dosing methods have been developed to quantify the dose required to achieve a specific concentration. This review brings up to date the earlier article in the Journal regarding predictive algorithms, various pharmacokinetics-based dosing techniques and Bayesian feedback methods for phenytoin dosing. The latest data support the original conclusions that dosing methods for phenytoin which incorporate an individualised approach or Bayesian principles tend to offer results superior to those from predictive algorithms. Bayesian methods have the additional advantage of using only 1 serum concentration, obtained under either steady-state or non-steady-state conditions. There is still a need for future investigations that include prospective evaluations of predictive performance and cost-effectiveness data.
Aging↗
Reassessment of the algorithm for prediction of liver fibrosis in patients with features of the metabolic syndrome.
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Algorithms↗
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↗
An algorithm for prediction of structural elements in small proteins.
A method for predicting the location of surface loops/turns and assigning the intervening secondary structure of the transglobular linkers in small, single domain globular proteins has been developed. Application to a set of 10 proteins of known structure indicates a high level of accuracy. The secondary structure assignment in the center of transglobular connections is correct in more than 85% of the cases. A similar error rate is found for loops. Since more global information about the fold is provided, it is complementary to standard secondary structure prediction approaches. Consequently, it may be useful in early stages of tertiary structure prediction when establishment of the structural class and possible folding topologies is of interest.
Algorithms↗
Algorithm to predict assisted reproductive technology pregnancy outcome reveals minimal embryo synergy.
A mathematical model was developed to calculate the implantation probability for individual embryos based on the pregnancy outcome of in vitro fertilization/intracytoplasmic sperm injection (IVF/ICSI) cases with multiple embryos transferred. This model was used to calculate implantation probabilities of embryos of 31 morphological types using the outcome of 1,200 IVF/ICSI cases. The algorithm was validated by comparing the calculated pregnancy probability and multiple pregnancy probability with the actual outcome of 281 separate IVF/ICSI cases. Finally, an estimation of embryo synergy was calculated.
Algorithms↗
An immunological algorithm to predict risk of high-grade rejection in cardiac transplant recipients.
BACKGROUND: Transplant-related coronary-artery disease (TCAD) develops frequently in cardiac-allograft recipients, and limits long-term survival. We examined the relation between this disorder and cumulative frequency of high-grade rejection, and investigated whether concomitant use of three immunological factors at the time of a low-grade endomyocardial biopsy can predict progression to high-grade rejection. METHODS: We investigated the relation between the cumulative annual frequency of high-grade rejection and TCAD in 198 recipients of cardiac transplantation between 1992 and 1996 by means of Kaplan-Meier actuarial life-tables. Endomyocardial biopsy, lymphocyte-growth assays, and anti-HLA antibody measurements were compiled over 12 months in 102 patients during their first post-transplant year. We calculated predictive values for high-grade rejection within 90 days by chi2, Kaplan Meier survival curves, and by multivariable logistic regression analyses. FINDINGS: We found a direct correlation between cumulative annual frequency of rejection and TCAD onset with highest risk in those with more than 0.75 rejections per year (p=0.0002). After a low-grade endomyocardial biopsy (0 or 1A), one or more donor-recipient HLA-DR matches protected against high-grade rejections (p<0.001). Among individuals with one or two DR matches, the negative predictive value for progression from a low-grade biopsy to a high-grade rejection was 87% in the presence of a negative lymphocyte-growth assay. Among individuals with no DR matches, the presence of either a positive lymphocyte-growth assay or IgG anti-major-histocompatibility complex (MHC) class II antibodies was independently associated with high probability of progression to rejection (64% and 66%, respectively, p<0.0005). When both assays were positive, concomitantly with a low-grade endomyocardial biopsy, the positive predictive value for progression to a high-grade rejection was 86% (p<0.0001). For endomyocardial-biopsy grades 1B or 2, a positive lymphocyte-growth assay alone was associated with high-grade rejection in 100% of cases. INTERPRETATION: Use of an algorithm combining three immunological factors at the time of a low-grade endomyocardial biopsy enables prospective stratification of cardiac transplant recipients into risk categories for progression to high-grade rejection. Low-risk individuals require fewer biopsies, moderate-risk individuals require an ongoing schedule of surveillance biopsies, and high-risk individuals require rational organisation of interventional strategies aimed at preventing rejection. Additional predictive factors are needed to identify moderate-risk individuals who will progress to rejection. Ultimately, successful intervention may have an impact on the subsequent complication of TCAD.
Adult↗
Application of an algorithm to predict CD4 lymphocyte count below 200 cells/mm(3) in HIV-infected patients in South Africa.
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Algorithms↗
Adaptive epileptic seizure prediction system.
Current epileptic seizure "prediction" algorithms are generally based on the knowledge of seizure occurring time and analyze the electroencephalogram (EEG) recordings retrospectively. It is then obvious that, although these analyses provide evidence of brain activity changes prior to epileptic seizures, they cannot be applied to develop implantable devices for diagnostic and therapeutic purposes. In this paper, we describe an adaptive procedure to prospectively analyze continuous, long-term EEG recordings when only the occurring time of the first seizure is known. The algorithm is based on the convergence and divergence of short-term maximum Lyapunov exponents (STLmax) among critical electrode sites selected adaptively. A warning of an impending seizure is then issued. Global optimization techniques are applied for selecting the critical groups of electrode sites. The adaptive seizure prediction algorithm (ASPA) was tested in continuous 0.76 to 5.84 days intracranial EEG recordings from a group of five patients with refractory temporal lobe epilepsy. A fixed parameter setting applied to all cases predicted 82% of seizures with a false prediction rate of 0.16/h. Seizure warnings occurred an average of 71.7 min before ictal onset. Similar results were produced by dividing the available EEG recordings into half training and testing portions. Optimizing the parameters for individual patients improved sensitivity (84% overall) and reduced false prediction rate (0.12/h overall). These results indicate that ASPA can be applied to implantable devices for diagnostic and therapeutic purposes.
Algorithms↗
Algorithm to predict MiniCHEF electrophoresis patterns of Entamoeba histolytica DNA.
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Algorithms↗
Prediction of 3-yr cadaveric graft survival based on pre-transplant variables in a large national dataset.
Pre- and post-transplant predictive factors of graft survival for optimal and expanded criteria grafts have been studied in the past. The goal of our study was to evaluate the recent large set of United Network of Organ Sharing records (1990-1998) to generate a prediction algorithm of 3-yr graft survival based on pre-transplant variables alone. The dataset of patients with end-stage renal disease and cadaveric kidney or kidney-pancreas transplantation (1990-1998) used in the study consisted of 37,407 records. Logistic regression (LM) and a tree-based model (TBM) were used to identify predictors of 3-yr allograft survival and to generate prediction algorithm. Donor and recipient demographic characteristics (age, race, and gender) and body mass index showed non-linear, while human leukocyte antigen match showed strong linear relationships with 3-yr graft survival. Prediction of the probability of graft survival from the model, achieved a good match with the observed survival of the separate dataset, with a correlation of r = 0.998 for LM and r = 0.984 for TBM. The positive predictive value (PV) of allograft survival with LM and TBM was 76.0% and the negative PV was 63 and 53.8% for LM and TBM, respectively. Both LM and the TBM can potentially be used in clinical practice for long-term prediction of kidney allograft survival based on pre-transplant variables.
Algorithms↗
Predicting human immunodeficiency virus protease cleavage sites in proteins by a discriminant function method.
Based on the sequence-coupled (Markov chain) model and vector-projection principle, a discriminant function method is proposed to predict sites in protein substrates that should be susceptible to cleavage by the HIV-1 protease. The discriminant function is defined by delta = phi+ - phi-, where phi+ and phi- are the cleavable and noncleavable attributes for a given peptide, and they can be derived from two complementary sets of peptides, S+ and S-, known to be cleavable and noncleavable, respectively, by the enzyme. The rate of correct prediction by the method for the 62 cleavable peptides and 239 noncleavable peptides in the training set are 100 and 96.7%, respectively. Application of the method to the 55 sequences which are outside the training set and known to be cleaved by the HIV-1 protease accurately predicted 100% of the peptides as substrates of the enzyme. The method also predicted all but one of the sites hydrolyzed by the protease in native HIV-1 and HIV-2 reverse transcriptases, where the HIV-1 protease discriminates between nearly identical sequences in a very subtle fashion. Finally, the algorithm predicts correctly all of the HIV-1 protease processing sites in the native gag and gag/pol HIV-1 polyproteins, and all of the cleavage sites identified in denatured protease and reverse transcriptase. The new predictive algorithm provides a novel route toward understanding the specificity of this important therapeutic target.
Algorithms↗
An algorithm for predicting the NMR shielding of protons over substituted benzene rings.
In a strong magnetic field, hydrogen nuclei located over an aromatic ring experience a reduced magnetic field as a result of the induced magnetic field associated with circulating pi electrons. We used GIAO-SCF, an ab initio subroutine in Gaussian 94 to calculate isotropic shielding values and to determine the proton nuclear magnetic resonance (NMR) shielding increment for a simple model system: methane held at various positions over a substituted benzene ring. The NMR shielding increments experienced by the proximal protons of methane have been mapped as a function of their position X, Y, and Z relative to the center of aniline and, separately, nitrobenzene. A mathematical function of the same form has been fit to the three-dimensional shielding increment surface at each of five distances from the face of each aromatic ring. In addition, a single mathematical equation has been developed for predicting the shielding caused by either substituted aromatic ring. The chemical shifts predicted by using the results of this equation in conjunction with additive substituent increments are compared to observed values.
Algorithms↗
Evidence for the usefulness of in vitro dialyzability, Caco-2 cell models, animal models, and algorithms to predict zinc bioavailability in humans.
Low bioavailability of zinc in certain diet types may contribute to zinc deficiency and its consequences in populations. As a result, several experimental models including animal models, in vitro dialyzability models, and Caco-2 cells, have been used to study these factors and estimate their impact on human zinc absorption. For the most part, consistency has been observed between the latter models and human absorption studies to identify factors that enhance or inhibit zinc bioavailability. However, dialyzability methods are limited to modeling luminal interactions among the factors as they affect zinc availability while Caco-2 cells can model luminal effects and uptake by intestinal cells. Neither animal nor in vitro methods can predict the magnitude of zinc absorption at the level of the human organism. Caco-2 cells will be useful models for understanding the mechanisms of intestinal zinc absorption. The in vitro methods are also limited to modeling absorption and the interactions that occur. Algorithms to estimate zinc absorption, based on dietary content of zinc absorption modifiers, have been derived from human studies. An algorithm derived from studies of zinc retention from radioactive zinc-labeled test meals underestimates zinc absorption compared to that derived from measurement of true zinc absorption from total diets using isotopic tracer methods. Based on the latter, phytate appears to be the only major inhibitor of zinc absorption from typical diets. Ultimately, population-based studies are needed to determine the impact of dietary factors that modulate zinc absorption on the adequacy of zinc status.
Algorithms↗
Entrainment mapping for rapid distinction of left and right atrial tachycardias.
BACKGROUND: Distinguishing left from right atrial tachycardia is a critical step for guiding ablation. OBJECTIVES: The purpose of this study was to develop and validate a simple algorithm predicting the location of macroreentrant atrial tachycardia (AT) circuits from limited entrainment mapping in right atrium (RA) and coronary sinus (CS). METHODS: In 180 patients with organized reentrant AT, entrainment was performed at the high RA, proximal CS, and distal CS. The difference between the postpacing interval (PPI) and tachycardia cycle length (TCL) was calculated at each site. The location of the AT reentrant circuit was determined by mapping and ablation. An algorithm predicting AT regions was developed from 104 ATs in the first 90 patients (group I) and prospectively evaluated in a validation cohort of 106 ATs in the second 90 patients (group II). RESULTS: In group I, PPI-TCL difference <50 or >50 ms at the high RA distinguished RA from LA reentrant circuits. For RA tachycardias, PPI-TCL difference at the proximal CS distinguished common flutter from lateral RA circuits. For LA circuits, PPI-TCL difference at the proximal and distal CS distinguished perimitral reentry from reentry involving the right pulmonary veins and septum. In group II, an algorithm based on PPI-TCL difference >50 or <50 ms at the high RA, proximal CS, or distal CS had sensitivity of 94%, specificity of 88%, and predictive accuracy of 93% for predicting the successful ablation region. CONCLUSION: Limited entrainment from sites accessible from the RA can expeditiously suggest the AT location to guide more detailed mapping and potentially avoid unnecessary transseptal punctures in some patients.
Adult↗