SEARCH · PubMed Health

Results for “Classification Algorithms”

Explore indexed PubMed citations for clinical trials, systematic reviews and public health research. Read source abstracts and follow each citation to its original PubMed record.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 1,099 records · Page 61Linked to original sources

PubMedDate not supplied

Using particle swarms for the development of QSAR models based on K-nearest neighbor and kernel regression.

We describe the application of particle swarms for the development of quantitative structure-activity relationship (QSAR) models based on k-nearest neighbor and kernel regression. Particle swarms is a population-based stochastic search method based on the principles of social interaction. Each individual explores the feature space guided by its previous success and that of its neighbors. Success is measured using leave-one-out (LOO) cross validation on the resulting model as determined by k-nearest neighbor kernel regression. The technique is shown to compare favorably to simulated annealing using three classical data sets from the QSAR literature.

Algorithms↗

PubMed2000

Sjögren's syndrome: autoimmune epithelitis.

Sjögren's syndrome (SS) is a chronic autoimmune disorder of the exocrine glands of unknown aetiology, which is typically associated with focal lymphocytic infiltrates of glandular tissues and autoantibody responses against the Ro(SSA) and La(SSB) ribonucleoproteins. In almost one-third of patients disease involves various extraglandular sites, whereas approximately 5% of patients may also develop malignant B-cell lymphoma. In addition, features of SS are frequently encountered (5-20%) in patients with several other autoimmune rheumatic diseases, and in several respects these 'secondary' forms may be distinct from SS found alone (primary-SS), as well as from each other. The correct diagnosis and management of SS may require consideration from various specialists. Differential diagnosis includes adverse effects of drugs, sarcoidosis, lipoproteinaemias, age-related atrophy, chronic graft-versus-host disease, lymphomas, amyloidosis and infection by human immunodeficiency virus or hepatitis C virus. Based on the sequential application of the validated European classification criteria for SS, a practical algorithm for diagnosis is presented. Despite progress in the understanding of the broad clinicopathological spectrum of the entity, its treatment remains largely empirical and symptomatic. To date, the decision for systemic therapeutic intervention is primarily based on the severity of extraglandular manifestations.

Diagnosis, Differential↗

PubMed1989

[Quantitative studies on the influence of connective tissue and fatty structures on the ultrasonic image of the liver].

Using a computerized ultrasound system, 63 autopsied livers were examined. A quantitative description of the ultrasound image was made by using statistical parameters from pattern recognition algorithms. As a reference a chemical/morphometrical classification into normal, diffuse and regional fatty infiltration and fibrosis/cirrhosis was performed. Diagnostic accuracy varied from 77% to 92%. Ultrasound tissue characterisation showed an influence of fatty and connective tissue structures on image parameters. This supplies evidence for the fact that computerised ultrasound examination yields more information from the ultrasound image than the normal observer. This may be the cause of an increased diagnostic accuracy when using computerized ultrasound systems, especially in the case of fibrosis/cirrhosis.

Diagnosis, Computer-Assisted↗

PubMed2004

Column sorting: rapid calculation of the phylogenetic likelihood function.

Likelihood applications have become a central approach for molecular evolutionary analyses since the first computationally tractable treatment two decades ago. Although Felsenstein's original pruning algorithm makes likelihood calculations feasible, it is usually possible to take advantage of repetitive structure present in the data to arrive at even greater computational reductions. In particular, alignment columns with certain similarities have components of the likelihood calculation that are identical and need not be recomputed if columns are evaluated in an optimal order. We develop an algorithm for exploiting this speed improvement via an application of graph theory. The reductions provided by the method depend on both the tree and the data, but typical savings range between 15%and 50%. Real-data examples with time reductions of 80%have been identified. The overhead costs associated with implementing the algorithm are minimal, and they are recovered in all but the smallest data sets. The modifications will provide faster likelihood algorithms, which will allow likelihood methods to be applied to larger sets of taxa and to include more thorough searches of the tree topology space.

Algorithms↗

PubMed2005

Evaluating intraspecific "network" construction methods using simulated sequence data: do existing algorithms outperform the global maximum parsimony approach?

In intraspecific studies, reticulated graphs are valuable tools for visualization, within a single figure, of alternative genealogical pathways among haplotypes. As available software packages implementing the global maximum parsimony (MP) approach only give the possibility to merge resulting topologies into less-resolved consensus trees, MP has often been neglected as an alternative approach to purely algorithmic (i.e., methods defined solely on the basis of an algorithm) "network" construction methods. Here, we propose to search tree space using the MP criterion and present a new algorithm for uniting all equally most parsimonious trees into a single (possibly reticulated) graph. Using simulated sequence data, we compare our method with three purely algorithmic and widely used graph construction approaches (minimum-spanning network, statistical parsimony, and median-joining network). We demonstrate that the combination of MP trees into a single graph provides a good estimate of the true genealogy. Moreover, our analyses indicate that, when internal node haplotypes are not sampled, the median-joining and MP methods provide the best estimate of the true genealogy whereas the minimum-spanning algorithm shows very poor performances.

Algorithms↗

PubMed2006

Efficient likelihood computations with nonreversible models of evolution.

Recent advances in heuristics have made maximum likelihood phylogenetic tree estimation tractable for hundreds of sequences. Noticeably, these algorithms are currently limited to reversible models of evolution, in which Felsenstein's pulley principle applies. In this paper we show that by reorganizing the way likelihood is computed, one can efficiently compute the likelihood of a tree from any of its nodes with a nonreversible model of DNA sequence evolution, and hence benefit from cutting-edge heuristics. This computational trick can be used with reversible models of evolution without any extra cost. We then introduce nhPhyML, the adaptation of the nonhomogeneous nonstationary model of Galtier and Gouy (1998; Mol. Biol. Evol. 15:871-879) to the structure of PhyML, as well as an approximation of the model in which the set of equilibrium frequencies is limited. This new version shows good results both in terms of exploration of the space of tree topologies and ancestral G+C content estimation. We eventually apply it to rRNA sequences slowly evolving sites and conclude that the model and a wider taxonomic sampling still do not plead for a hyperthermophilic last universal common ancestor.

Algorithms↗

PubMed2003

Applications of image analysis to anatomic pathology: realities and promises.

Image Analysis in Pathology is viewed as an ancillary method meant to provide objective support in the resolution of difficult problems. Its Achilles heel is the process of nuclear segmentation (delimitation of the nuclear membrane) which is extremely difficult in pathology materials. Although interactive segmentation procedures are available no reliable fully automatic method has been described. The only application of image analysis that has truly succeeded in Pathology is DNA ploidy measurement. A very desirable application is the quantitation of immunohistochemical markers, which is technically challenging, has been resolved only in certain cases and is unlikely to have a general solution. Nuclear quantitation has repeatedly proven to be helpful in reaching differential diagnoses, in particular when based on size distributions of nuclear profiles rather than its average, but is hampered by the segmentation problem discussed above. Texture analysis of chromatin is an exciting, mathematically complex application likely to succeed, for which many approaches have been described. Finally a diagnosis (classification) can be obtained based on algorithms applied to multiple descriptors of tumor cells (for instance nuclear sizes, chromatin texture, shape, etc). The best classificatory approaches are neural networks (a form of artificial intelligencee), multivariate analysis, and logistic regression (statistical).

Algorithms↗

PubMed2001

TreeGeneBrowser: phylogenetic data mining of gene sequences from public databases.

MOTIVATION: Sequence databases represent an enormous resource of phylogenetic information, but there is a lack of tools for accessing that information in order to assess the amount of evolutionary information in these databases that may be suitable for phylogenetic reconstruction and for identifying areas of the taxonomy that are under-represented for specific gene sequences. RESULTS: We have developed TreeGeneBrowser which allows inspection and evaluation of gene sequence data for phylogenetic reconstruction. This program improves the efficiency of identification of genes that may be useful for particular phylogenetic studies and identifies taxa and taxonomic branches that are under-represented in sequence databases.

Algorithms↗

PubMed2006

Neural network prediction of peptide separation in strong anion exchange chromatography.

MOTIVATION: The still emerging combination of technologies that enable description and characterization of all expressed proteins in a biological system is known as proteomics. Although many separation and analysis technologies have been employed in proteomics, it remains a challenge to predict peptide behavior during separation processes. New informatics tools are needed to model the experimental analysis method that will allow scientists to predict peptide separation and assist with required data mining steps, such as protein identification. RESULTS: We developed a software package to predict the separation of peptides in strong anion exchange (SAX) chromatography using artificial neural network based pattern classification techniques. A multi-layer perceptron is used as a pattern classifier and it is designed with feature vectors extracted from the peptides so that the classification error is minimized. A genetic algorithm is employed to train the neural network. The developed system was tested using 14 protein digests, and the sensitivity analysis was carried out to investigate the significance of each feature. AVAILABILITY: The software and testing results can be downloaded from ftp://ftp.bbc.purdue.edu.

Algorithms↗

PubMed2005

How stable are stages of change for nutrition behaviors in the Netherlands?

This paper describes the stability of the stages of change concept of the Transtheoretical Model for three different nutrition behaviors (fat, fruit and vegetable intake) among adult individuals who are unexposed to planned interventions. Secondary analyses were conducted on data collected in control groups (n = 386 and n = 739) of two intervention studies in the Netherlands. Data on dietary intakes and stages of change was collected at baseline and follow up, either 1 year (study 1) or 3 months (study 2) post-baseline. Higher levels of agreement between baseline and follow-up measures of stage of change were found for pre-contemplation and maintenance than for the other stages. However, many forward as well as backward stage transitions occurred between baseline and follow-up also among respondents in pre-contemplation and maintenance at baseline. The results indicate that many stage transitions may occur in individuals, also when they are not exposed to planned interventions. An additional explanation for the stage instability may be low reliability of the staging algorithm. The results imply that if classification into stages of change is used to tailor interventions, these interventions may be tailored to the wrong stage, at least with longer time intervals between stage assessment and intervention. Further research is needed to assess 'spontaneous' stage-transitions in shorter time intervals.

Adult↗

PubMed2004

Bayesian phylogenetic model selection using reversible jump Markov chain Monte Carlo.

A common problem in molecular phylogenetics is choosing a model of DNA substitution that does a good job of explaining the DNA sequence alignment without introducing superfluous parameters. A number of methods have been used to choose among a small set of candidate substitution models, such as the likelihood ratio test, the Akaike Information Criterion (AIC), the Bayesian Information Criterion (BIC), and Bayes factors. Current implementations of any of these criteria suffer from the limitation that only a small set of models are examined, or that the test does not allow easy comparison of non-nested models. In this article, we expand the pool of candidate substitution models to include all possible time-reversible models. This set includes seven models that have already been described. We show how Bayes factors can be calculated for these models using reversible jump Markov chain Monte Carlo, and apply the method to 16 DNA sequence alignments. For each data set, we compare the model with the best Bayes factor to the best models chosen using AIC and BIC. We find that the best model under any of these criteria is not necessarily the most complicated one; models with an intermediate number of substitution types typically do best. Moreover, almost all of the models that are chosen as best do not constrain a transition rate to be the same as a transversion rate, suggesting that it is the transition/transversion rate bias that plays the largest role in determining which models are selected. Importantly, the reversible jump Markov chain Monte Carlo algorithm described here allows estimation of phylogeny (and other phylogenetic model parameters) to be performed while accounting for uncertainty in the model of DNA substitution.

Algorithms↗

PubMed2006

Refining multiple sequence alignments with conserved core regions.

Accurate multiple sequence alignments of proteins are very important to several areas of computational biology and provide an understanding of phylogenetic history of domain families, their identification and classification. This article presents a new algorithm, REFINER, that refines a multiple sequence alignment by iterative realignment of its individual sequences with the predetermined conserved core (block) model of a protein family. Realignment of each sequence can correct misalignments between a given sequence and the rest of the profile and at the same time preserves the family's overall block model. Large-scale benchmarking studies showed a noticeable improvement of alignment after refinement. This can be inferred from the increased alignment score and enhanced sensitivity for database searching using the sequence profiles derived from refined alignments compared with the original alignments. A standalone version of the program is available by ftp distribution (ftp://ftp.ncbi.nih.gov/pub/REFINER) and will be incorporated into the next release of the Cn3D structure/alignment viewer.

Algorithms↗

PubMed2004

Evaluation of pulmonary arterial hypertension.

PURPOSE OF REVIEW: Pulmonary arterial hypertension (PAH) is defined as a group of diseases characterized by a progressive increase of pulmonary vascular resistance leading to right ventricular failure and premature death. The purpose of this review is to analyze the current knowledge of the evaluation of PAH patients. RECENT FINDINGS: Recently, the diagnostic approach has been more clearly defined according to the new clinical classification and with consensus reached on algorithms of various investigative tests and procedures that exclude other causes and ensure an accurate diagnosis of PAH. The diagnostic procedures include clinical history and physical examination, ECG, chest radiography, transthoracic Doppler echocardiography, pulmonary function tests, arterial blood gases, ventilation and perfusion lung scan, high-resolution CT of the lung, contrast-enhanced spiral CT of the lung and pulmonary angiography, blood tests and immunology, abdominal ultrasound scan, exercise capacity assessment, and hemodynamic evaluation. SUMMARY: Invasive and noninvasive markers of disease severity, either biomarkers or physiologic parameters and tests that can be widely applied, have been proposed to reliably diagnose PAH and monitor the clinical course.

Electrocardiography↗

PubMed2003

Allergic and nonallergic drug reactions.

Allergic drug reactions may be difficult to distinguish from nonallergic reactions. In this article, we review a pragmatic approach to the management of adverse drug reactions on the basis of knowledge of the classification and patterns of these reactions. Algorithms for management of patients with a previous adverse drug reaction who require treatment for the same indication, and the approach to a patient who experiences a drug reaction while on multiple drugs, are presented.

Anti-Bacterial Agents↗

PubMed2001

Molecular strategy for 'serotyping' of human enteroviruses.

To explore further the phylogenetic relationships between human enteroviruses and to develop new diagnostic approaches, we designed a pair of generic primers in order to study a 1452 bp genomic fragment (relative to the poliovirus Mahoney genome), including the 3' end of the VP1-coding region, the 2A- and 2B-coding regions, and the 5' moiety of the 2C-coding region. Fifty-nine of the 64 prototype strains and 45 field isolates of various origins, involving 21 serotypes and 6 strains untypable by standard immunological techniques, were successfully amplified with these primers. By determining the nucleotide sequence of the genomic fragment encoding the C-terminal third of the VP1 capsid protein we developed a molecular typing method based on RT-PCR and sequencing. If field isolate sequences were compared to human enterovirus VP1 sequences available in databases, nucleotide identity score was, in each case, highest with the homotypic prototype (74.8 to 89.4%). Phylogenetic trees were generated from alignments of partial VP1 sequences with several phylogeny algorithms. In all cases, the new classification of enteroviruses into five identified species was confirmed and strains of the same serotype were always monophyletic. Analysis of the results confirmed that the 3' third of the VP1-coding sequence contains serotype-specific information and can be used as the basis of an effective and rapid molecular typing method. Furthermore, the amplification of such a long genomic fragment, including non-structural regions, is straightforward and could be used to investigate genome variability and to identify recombination breakpoints or specific attributes of pathogenicity.

3' Untranslated Regions↗

PubMed2005

Video content classification based on 3-D eigen analysis.

To achieve video understanding, it is of utmost practical importance to classify videos according to its spatial and temporal features in an efficient and effective manner. It still remains, however, largely an elusive task. In still-image analysis, thanks to the great efforts made by many researchers, a broad spectrum of methods have been developed with great success, especially the ones based on eigen analysis due to its efficacy. In this paper, inspired by the impressive performance achieved by this framework, we will develop a content-based video classification method based on three-dimensional (3-D) eigen analysis. Unlike most other video understanding schemes where the spatial and temporal contents play different roles in the processing, this new method treats a video as a solid within a 3-D Euclidean space and can, thus, naturally take advantage of the spatial and temporal contents existing in videos. After computing the eigen values and corresponding eigen vectors of the autocorrelation matrix for each small 3-D macroblock, different labels are assigned regarding its spatial/temporal natures based on the behavioral properties of the eigen values and eigen vectors. Extensive empirical studies have suggested encouraging performance for the use of this eigen analysis-based video classification method.

Algorithms↗

PubMed2005

An adaptive tissue characterization network for model-free visualization of dynamic contrast-enhanced magnetic resonance image data.

Dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) has become an important source of information to aid cancer diagnosis. Nevertheless, due to the multi-temporal nature of the three-dimensional volume data obtained from DCE-MRI, evaluation of the image data is a challenging task and tools are required to support the human expert. We investigate an approach for automatic localization and characterization of suspicious lesions in DCE-MRI data. It applies an artificial neural network (ANN) architecture which combines unsupervised and supervised techniques for voxel-by-voxel classification of temporal kinetic signals. The algorithm is easy to implement, allows for fast training and application even for huge data sets and can be directly used to augment the display of DCE-MRI data. To demonstrate that the system provides a reasonable assessment of kinetic signals, the outcome is compared with the results obtained from the model-based three-time-points (3TP) technique which represents a clinical standard protocol for analysing breast cancer lesions. The evaluation based on the DCE-MRI data of 12 cases indicates that, although the ANN is trained with imprecisely labeled data, the approach leads to an outcome conforming with 3TP without presupposing an explicit model of the underlying physiological process.

Algorithms↗

PubMed2003

Penalized discriminant methods for the classification of tumors from gene expression data.

Due to the advent of high-throughput microarray technology, it has become possible to develop molecular classification systems for various types of cancer. In this article, we propose a methodology using regularized regression models for the classification of tumors in microarray experiments. The performances of principal components, partial least squares, and ridge regression models are studied; these regression procedures are adapted to the classification setting using the optimal scoring algorithm. We also develop a procedure for ranking genes based on the fitted regression models. The proposed methodologies are applied to two microarray studies in cancer.

Algorithms↗