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At least 109 records · Page 6Linked to original sources
PubMed2000

A two-step algorithm for predicting portal dose images in arbitrary detectors.

Recently, portal imaging systems have been successfully demonstrated in dosimetric treatment verification applications, where measured and predicted images are quantitatively compared. To advance this approach to dosimetric verification, a two-step model which predicts dose deposition in arbitrary portal image detectors is presented. The algorithm requires patient CT data, source-detector distance, and knowledge of the incident beam fluence. The first step predicts the fluence entering a portal imaging detector located behind the patient. Primary fluence is obtained through ray-tracing techniques, while scatter fluence prediction requires a library of Monte Carlo-generated scatter fluence kernels. These kernels allow prediction of basic radiation transport parameters characterizing the scattered photons, including fluence and mean energy. The second step of the algorithm involves a superposition of Monte Carlo-generated pencil beam kernels, describing dose deposition in a specific detector, with the predicted incident fluence. This process is performed separately for primary and scatter fluence, and yields a predicted dose image. A small but noticeable improvement in prediction is obtained by explicitly modeling the off-axis energy spectrum softening due to the flattening filter. The algorithm is tested on a slab phantom and a simple lung phantom (6 MV). Furthermore, an anthropomorphic phantom is utilized for a simulated lung treatment (6 MV), and simulated pelvis treatment (23 MV). Data were collected over a range of air gaps (10-80 cm). Detectors incorporating both low and high atomic number buildup are used to measure portal image profiles. Agreement between predicted and measured portal dose is better than 3% in areas of low dose gradient (<30%/cm) for all phantoms, air gaps, beam energies, and detector configurations tested here. It is concluded that this portal dose prediction algorithm is fast, accurate, allows separation of primary and scatter dose, and can model arbitrary detectors.

Air↗
PubMed2003

A soft docking algorithm for predicting the structures of protein-protein complexes.

An efficient soft docking algorithm is described to predict the mode of binding between two proteins based on the three-dimensional structures of molecules. The molecular model used in this work was grounded on the "simplified protein" model used in Janin's docking algorithm. The side chain flexibility of the amino acid residues Arg, Lys, Asp, Glu and Met at the protein surface was considered through softening the molecular surface. A double filtering technique was used to eliminate most of the unlike binding modes. The energy minimization was performed on the retained structures, and then these structures were evaluated with the scoring function which included electrostatic, desolvation and van der Waals energy terms. The 26 complexes were used to test this docking algorithm and good results were obtained. The native-like conformations of all the complexes were all found, of which 20 were ranked in the top 10.

Algorithms↗
PubMed2005

A statistical analysis of RNA folding algorithms through thermodynamic parameter perturbation.

Computational RNA secondary structure prediction is rather well established. However, such prediction algorithms always depend on a large number of experimentally measured parameters. Here, we study how sensitive structure prediction algorithms are to changes in these parameters. We found already that for changes corresponding to the actual experimental error to which these parameters have been determined, 30% of the structure are falsely predicted whereas the ground state structure is preserved under parameter perturbation in only 5% of all the cases. We establish that base-pairing probabilities calculated in a thermal ensemble are viable although not a perfect measure for the reliability of the prediction of individual structure elements. Here, a new measure of stability using parameter perturbation is proposed, and its limitations are discussed.

Algorithms↗
PubMed2006

Multiplatform genome-wide identification and modeling of functional human estrogen receptor binding sites.

BACKGROUND: Transcription factor binding sites (TFBS) impart specificity to cellular transcriptional responses and have largely been defined by consensus motifs derived from a handful of validated sites. The low specificity of the computational predictions of TFBSs has been attributed to ubiquity of the motifs and the relaxed sequence requirements for binding. We posited that the inadequacy is due to limited input of empirically verified sites, and demonstrated a multiplatform approach to constructing a robust model. RESULTS: Using the TFBS for the estrogen receptor (ER)alpha (estrogen response element [ERE]) as a model system, we extracted EREs from multiple molecular and genomic platforms whose binding to ERalpha has been experimentally confirmed or rejected. In silico analyses revealed significant sequence information flanking the standard binding consensus, discriminating ERE-like sequences that bind ERalpha from those that are nonbinders. We extended the ERE consensus by three bases, bearing a terminal G at the third position 3' and an initiator C at the third position 5', which were further validated using surface plasmon resonance spectroscopy. Our functional human ERE prediction algorithm (h-ERE) outperformed existing predictive algorithms and produced fewer than 5% false negatives upon experimental validation. CONCLUSION: Building upon a larger experimentally validated ERE set, the h-ERE algorithm is able to demarcate better the universe of ERE-like sequences that are potential ER binders. Only 14% of the predicted optimal binding sites were utilized under the experimental conditions employed, pointing to other selective criteria not related to EREs. Other factors, in addition to primary nucleotide sequence, will ultimately determine binding site selection.

Algorithms↗
PubMed2004

On the development and application of a continuous-discrete recursive prediction error algorithm.

Recursive state and parameter reconstruction is a well-established field in control theory. In the current paper we derive a continuous-discrete version of recursive prediction error algorithm and apply the filter in an environmental and biological setting as a possible alternative to the well-known extended Kalman filter. The framework from which the derivation is started is the so-called 'innovations-format' of the (continuous time) system model, including (discrete time) measurements. After the algorithm has been motivated and derived, it is subsequently applied to hypothetical and 'real-life' case studies including reconstruction of biokinetic parameters and parameters characterizing the dynamics of a river in the United Kingdom. Advantages and characteristics of the method are discussed.

Algorithms↗
PubMed2002

Risk group assessment and clinical outcome algorithm to predict the natural history of patients with surgically resected renal cell carcinoma.

PURPOSE: To create a comprehensive algorithm that can predict postoperative renal cell carcinoma (RCC) patient outcomes and response to therapy. PATIENTS AND METHODS: A prospective cohort study was performed with outcome assessment on the basis of chart review of 814 patients who underwent nephrectomy between 1989 and 2000. At diagnosis, M1 or N1/N2M0 metastatic disease (M) was present in 346 patients (43%), whereas 468 patients had no metastatic disease (NM) (N0M0). On the basis of UCLA Integrated Staging System category and the presence of metastases, patients were divided into low-risk (LR), intermediate-risk (IR), and high-risk (HR) groups. Decision boxes integrating tumor-node-metastasis staging, tumor grade, and performance status were compiled for determining a patient's risk group. RESULTS: NM-LR patients had 91% disease-specific survival at 5 years, lower recurrence rate, and better disease-free survival compared with NM-IR and HR patients. Disease progressed in 50% of NM-HR patients. Disease-specific survival of NM-HR patients who received immunotherapy (IMT) for recurrent disease was similar to that of M-LR patients treated with cytoreductive nephrectomy and adjuvant IMT. Time from recurrence to death for NM-HR patients was inferior to that for M-LR patients. After IMT, approximately 25% of M-LR and 12% of M-IR patients had long-term progression-free survival. M-HR patients did poorly despite IMT. CONCLUSION: Stratifying RCC patients into high-, intermediate-, and low-risk subgroups provides a clinically useful system for predicting outcome and provides a unique tool for risk assignment and outcome analysis. Subclassifying RCC into well-defined risk groups should allow better patient counseling and identification of both NM-HR subgroups that need adjuvant treatment and nonresponders who need alternative therapies.

Algorithms↗
PubMed2000

BiGGER: a new (soft) docking algorithm for predicting protein interactions.

A new computationally efficient and automated "soft docking" algorithm is described to assist the prediction of the mode of binding between two proteins, using the three-dimensional structures of the unbound molecules. The method is implemented in a software package called BiGGER (Bimolecular Complex Generation with Global Evaluation and Ranking) and works in two sequential steps: first, the complete 6-dimensional binding spaces of both molecules is systematically searched. A population of candidate protein-protein docked geometries is thus generated and selected on the basis of the geometric complementarity and amino acid pairwise affinities between the two molecular surfaces. Most of the conformational changes observed during protein association are treated in an implicit way and test results are equally satisfactory, regardless of starting from the bound or the unbound forms of known structures of the interacting proteins. In contrast to other methods, the entire molecular surfaces are searched during the simulation, using absolutely no additional information regarding the binding sites. In a second step, an interaction scoring function is used to rank the putative docked structures. The function incorporates interaction terms that are thought to be relevant to the stabilization of protein complexes. These include: geometric complementarity of the surfaces, explicit electrostatic interactions, desolvation energy, and pairwise propensities of the amino acid side chains to contact across the molecular interface. The relative functional contribution of each of these interaction terms to the global scoring function has been empirically adjusted through a neural network optimizer using a learning set of 25 protein-protein complexes of known crystallographic structures. In 22 out of 25 protein-protein complexes tested, near-native docked geometries were found with C(alpha) RMS deviations < or =4.0 A from the experimental structures, of which 14 were found within the 20 top ranking solutions. The program works on widely available personal computers and takes 2 to 8 hours of CPU time to run any of the docking tests herein presented. Finally, the value and limitations of the method for the study of macromolecular interactions, not yet revealed by experimental techniques, are discussed.

Algorithms↗
PubMed1989

Nightcap: a home-based sleep monitoring system.

In an attempt to offer a home-based adjunct to traditional sleep laboratory methods, we developed a system to monitor sleep, and to predict algorithmically non-rapid-eye-movement (NREM) and rapid-eye-movement (REM) sleep states, using eye and body motility as the only parameters. Eye movement was measured using a strain gauge transducer applied to the eyelid of subjects, while body movement was measured using a piezo-ceramic phono cartridge. Both transducers were mounted on a tennis headband, along with electronics that amplified, filtered, and digitized the signals. Digital pulse signals were input to a portable computer in minute-long epochs, and state-predicting algorithms were run based on this motility data. Four subjects were monitored in the sleep lab with both our headgear and standard polysomnography. Hand-scored sleep records were compared with those predicted by computer algorithms. Algorithm-predicted states agreed with hand-scored ones an average of 85.57% (SEM +/- 1.7%). Mean values for sleep onset and REM latency were within 1.6 and 10.8 min of polysomnographic records, respectively. These results are encouraging, and suggest that this system could provide a comfortable, subject operable, and inexpensive method for the evaluation of sleep at home.

Adult↗
PubMed2000

An algorithm for predicting proton nuclear magnetic resonance deshielding over a carbon-carbon double bond.

Hydrogen nuclei located over a carbon-carbon double bond in a strong magnetic field experience NMR shielding effects that result from the magnetic anisotropy of the nearby double bond and various other intramolecular shielding effects. We have used GIAO, a subroutine in Gaussian 98, to calculate isotropic shielding values and to predict the proton NMR shielding increment for a simple model system: methane held in various orientations, positions, and distances over ethene. The average proton NMR shielding increments of several orientations of methane have been plotted versus the Cartesian coordinates of the methane protons relative to the center of ethene. A single empirical equation for predicting the NMR shielding experienced by protons over a carbon-carbon double bond has been developed from these data. The predictive capability of this equation has been validated by comparing the shielding increments for several alkenes calculated using our equation to the experimentally observed shielding increments. This equation predicts the NMR shielding effects more accurately than previous models that were based on fewer geometries of methane over ethene. In fact, deshielding is predicted by this equation for protons over the center and within about 3 A of a carbon-carbon double bond. This result is in sharp contrast to predictions made by the long-held McConnell "shielding cone" model found in nearly every textbook on NMR, but is consistent with experimental observations. The algorithm for predicting the (de)shielding increment for a proton over an alkene can be used in a spreadsheet on a PC or incorporated into software that estimates chemical shifts using additive substituent constants or a database of structures. In either application its use can substantially improve the accuracy of the estimated chemical shift of a proton in the vicinity of a carbon-carbon double bond, and thus assist in spectral assignments and in correct structure determination.

Algorithms↗
PubMed1986

Probability of coding of a DNA sequence: an algorithm to predict translated reading frames from their thermodynamic characteristics.

An algorithm to determine the probability that a reading frame codifies for a protein is presented. It is based on the results of our previous studies on the thermodynamic characteristics of a translated reading frame. We also develop a prediction procedure to distinguish between coding and non-coding reading frames. The procedure is based on the characteristics of the putative product of the DNA sequence and not on periodicity characteristics of the sequence, so the prediction is not biased by the presence of overlapping translated reading frames or by the presence of translated reading frames on the complementary DNA strand.

Amino Acid Sequence↗
PubMed1985

A Bayesian feedback method of aminoglycoside dosing.

We assessed the accuracy of a Bayesian method in providing dosing regimens to achieve desired serum aminoglycoside concentrations. This method calculates individual kinetics based on serum drug concentration data. Performance was analyzed by determining accuracy, bias, correlations of observed to desired serum drug concentrations, and the ability to achieve a target serum drug concentration. We also compared results from the Bayesian method with those resulting from the use of the predictive algorithm portion of the computer program and with routine physician dosing. The Bayesian method resulted in a high correlation coefficient (r = 0.913) between observed and predicted serum concentrations. Analysis of peak aminoglycoside concentrations indicated that the Bayesian method was more accurate and less biased than the predictive algorithm portion of the program or routine physician dosing. A similar trend occurred for trough concentrations. Finally, there were no statistically significant differences between the predicted and observed peak (6.4 +/- 1.5 and 5.9 +/- micrograms/ml) and trough (1.2 +/- 0.9 and 1.4 +/- 0.8 micrograms/ml) serum aminoglycoside concentrations with the Bayesian dosing method. There were significant differences for peak concentrations with the predictive algorithm portion of the program and for peak and trough concentrations with physician dosing. These data demonstrate the accuracy of the Bayesian dosing method in attaining desired peak and trough serum aminoglycoside concentrations.

Bayes Theorem↗
PubMed2001

Long-stay patients in the pediatric intensive care unit.

OBJECTIVE: Length of stay in the pediatric intensive care unit (PICU) is a reflection of patient severity of illness and health status, as well as PICU quality and performance. We determined the clinical profiles and relative resource use of long-stay patients (LSPs) and developed a prediction model to identify LSPs for early quality and cost saving interventions. DESIGN: Nonconcurrent cohort study. SETTING: A total of 16 randomly selected PICUs and 16 volunteer PICUs. PATIENTS: A total of 11,165 consecutive admissions to the 32 PICUs. INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: LSPs were defined as patients having a length of stay greater than the 95th percentile (>12 days). Logistic regression analysis was used to determine which clinical characteristics, available within the first 24 hrs after admission, were associated with LSPs and to create a predictive algorithm. Overall, LSPs were 4.7% of the population but represented 36.1% of the days of care. Multivariate analysis indicated that the following factors are predictive of long stays: age <12 months, previous ICU admission, emergency admission, no CPR before admission, admission from another ICU or intermediate care unit, chronic care requirements (total parenteral nutrition and tracheostomy), specific diagnoses including acquired cardiac disease, pneumonia, and other respiratory disorders, having never been discharged from the hospital, need for ventilatory support or an intracranial catheter, and a Pediatric Risk of Mortality III score between 10 and 33. The performance of the prediction algorithm in both the training and validation samples for identifying LSPs was good for both discrimination (area under the receiver operating characteristics curve of 0.83 and 0.85, respectively), and calibration (goodness of fit, p = .33 and p = .16, respectively). LSPs comprised from 2.1% to 8.1% of individual ICU patients and occupied from 15.2% to 57.8% of individual ICU bed days. CONCLUSIONS: LSPs have less favorable outcomes and use more resources than non-LSPs. The clinical profile of LSPs includes those who are younger and those that require chronic care devices. A predictive algorithm could help identify patients at high risk of prolonged stays appropriate for specific interventions.

Age Distribution↗
PubMed2005

Selectivity of inferior temporal neurons for realistic pictures predicted by algorithms for image database navigation.

Primates have a remarkable ability to perceive, recognize, and discriminate among the plethora of people, places, and things that they see, and neural selectivity in the primate inferotemporal (IT) cortex is thought to underlie this ability. Here we investigated the relationship between neural response and perception by recording from IT neurons in monkeys while they viewed realistic images. We then compared the similarity of neural responses elicited by images to the quantitative similarity of the images. Image similarity was approximated using several algorithms, two of which were designed to search image databases for perceptually similar images. Some algorithms for image similarity correlated well with human perception, and these algorithms explained part of the stimulus selectivity of IT neurons. Images that elicited similar neural responses were ranked as more similar by these algorithms than images that elicited different neural responses, and images ranked as similar by the algorithms elicited similar responses from neurons. Neural selectivity was predicted more accurately when the reference images for algorithm similarity elicited either very strong or very weak responses from the neuron. The degree to which algorithms for image similarity were correlated with human perception was related to the degree to which algorithms explained the selectivity of IT neurons, providing support for the proposal that the selectivity of IT neurons is related to perceptual similarity of images.

Action Potentials↗
PubMed1990

Molecular modeling of residues 38-57 of the beta-subunit of human lutropin.

Several regions on both the alpha- and beta-subunits of human LH comprise the receptor-binding domain of the hormone. One of these, a disulfide loop peptide containing residues 38-57 on the beta-subunit, also stimulated steroidogenesis in rat Leydig cells. Circular dichroism analysis and a Schiffer-Edmundson helical wheel projection of beta-(38-57) revealed the possibility of an amphipathic alpha-helical structure through its N-terminal region. Secondary structure prediction algorithms do not predict alpha-helix in beta-(38-57), but, rather, suggest a sheet-coil-sheet topology. Homology searches between this peptide and proteins with known structure revealed that the two best matches are with prealbumin-(10-30) and melittin-(1-26). Based on hydrophobic moment calculations, we suggest that beta-(38-57) more closely resembles melittin, a known example of an amphipathic helix. Molecular models were constructed that included an alpha-helix between Pro-39 and Pro-50 producing a hydrophilic face involving Thr-40, Arg-43, and Gin-46. Loop closure was performed either visually or by an incremental minimization procedure, using distance constraints to patch in a disulfide bond. Molecular dynamics at 300, 360, and 1000 K were used to explore the local conformational space, and dynamic structures were minimized. The most reasonable structures were found with the 300 and 360 K simulations, with those at 360 K consistently producing structures with lower conformational energies. In each of these simulations, the N-terminus of the alpha-helix unraveled to form a reverse turn (predicted by the GOR algorithm) which include Cys-38, Pro-39, Thr-40, and Met-41. Simulations at 1000 K produced the most variation in structure, but these were deemed unreasonable. Although not all possible conformations were explored, several models were found that comply with the assumption of an amphipathic helix in the N-terminal half of the peptide.

Algorithms↗
PubMed1999

A potential smoothing algorithm accurately predicts transmembrane helix packing.

Potential smoothing, a deterministic analog of stochastic simulated annealing, is a powerful paradigm for the solution of conformational search problems that require extensive sampling, and should be a useful tool in computational approaches to structure prediction and refinement. A novel potential smoothing and search (PSS) algorithm has been developed and applied to predict the packing of transmembrane helices. The highlight of this method is the efficient manner in which it circumvents the combinatorial explosion associated with the large number of minima on multidimensional potential energy surfaces in order to converge to the global energy minimum. Here we show how our potential smoothing and search method succeeds in finding the global minimum energy structure for the glycophorin A (GpA) transmembrane helix dimer by optimizing interhelical van der Waals interactions over rigid and semi-rigid helices. Structures obtained from our ab initio predictions are in close agreement with recent experimental data.

Algorithms↗
PubMed2006

Revolutions in RNA secondary structure prediction.

RNA structure formation is hierarchical and, therefore, secondary structure, the sum of canonical base-pairs, can generally be predicted without knowledge of the three-dimensional structure. Secondary structure prediction algorithms evolved from predicting a single, lowest free energy structure to their current state where statistics can be determined from the thermodynamic ensemble. This article reviews the free energy minimization technique and the salient revolutions in the dynamic programming algorithm methods for secondary structure prediction. Emphasis is placed on highlighting the recently developed method, which statistically samples structures from the complete Boltzmann ensemble.

Algorithms↗