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PubMed2000

Generalized discriminant analysis using a kernel approach.

We present a new method that we call generalized discriminant analysis (GDA) to deal with nonlinear discriminant analysis using kernel function operator. The underlying theory is close to the support vector machines (SVM) insofar as the GDA method provides a mapping of the input vectors into high-dimensional feature space. In the transformed space, linear properties make it easy to extend and generalize the classical linear discriminant analysis (LDA) to nonlinear discriminant analysis. The formulation is expressed as an eigenvalue problem resolution. Using a different kernel, one can cover a wide class of nonlinearities. For both simulated data and alternate kernels, we give classification results, as well as the shape of the decision function. The results are confirmed using real data to perform seed classification.

Algorithms↗

PubMed2002

Constructing and testing a framework for dynamic risk assessment.

This paper describes the construction and testing of a framework for dynamic risk assessment. A review of previous studies identified 4 domains into which dynamic risk factors for sexual offending seem to fall. These were sexual interests, distorted attitudes, socioaffective functioning, and self-management. Psychometric indicators for 3 of the domains were identified, and 2 studies are reported using these indicators to test the framework. Study 1 divided men serving a prison sentence for a sexual offense against a child into 2 groups--those with a previous conviction of this kind (Repeaters) and those for whom this was the only time they had been sentenced for such an offense (Current Only). The Repeaters were found to show more distorted attitudes, worse socioaffective functioning, and poorer self-management than did the Current Only group. Study 2 used a simple algorithm to combine these psychometric indicators into an overall "Deviance" classification. Reconviction data was obtained for offenders classified as high, moderate, or low on Deviance. Sexual reconviction was found to be monotonically associated with the Deviance classification. Logistic regression analysis showed that both static variables (Static-99) and the Deviance classification made independent contributions to prediction. It is suggested that risk assessment procedures should combine these 2 approaches.

Algorithms↗

PubMed1993

Kakadu--a low power analogue neural network classifier.

An analogue neural network VLSI chip designed for low power operation is presented. This chip consists of 84 synapse elements arranged as arrays of size 10 x 6 and 6 x 4 and was fabricated using a standard 1.2 micron double metal single poly CMOS process. The synapses are digitally programmable and static weight storage is provided. The chip has a typical power consumption of tens of microwatts. It has been successfully trained and tested on a range of classification problems including 4-bit parity, character recognition and morphological-based classification of intracardiac electrogram signals.

Algorithms↗

PubMed1998

A review of methods for spike sorting: the detection and classification of neural action potentials.

The detection of neural spike activity is a technical challenge that is a prerequisite for studying many types of brain function. Measuring the activity of individual neurons accurately can be difficult due to large amounts of background noise and the difficulty in distinguishing the action potentials of one neuron from those of others in the local area. This article reviews algorithms and methods for detecting and classifying action potentials, a problem commonly referred to as spike sorting. The article first discusses the challenges of measuring neural activity and the basic issues of signal detection and classification. It reviews and illustrates algorithms and techniques that have been applied to many of the problems in spike sorting and discusses the advantages and limitations of each and the applicability of these methods for different types of experimental demands. The article is written both for the physiologist wanting to use simple methods that will improve experimental yield and minimize the selection biases of traditional techniques and for those who want to apply or extend more sophisticated algorithms to meet new experimental challenges.

Action Potentials↗

PubMed1997

Computer-assisted analysis of medulloblastoma. A cytologic study.

OBJECTIVE: To explore data from a set of cases of medulloblastoma to see whether quantitative image analysis might suggest evidence for the existence of lower and higher grade lesions. STUDY DESIGN: Fourteen consecutive cases of medulloblastoma were obtained. Smears were stained with toluidine blue. For each case, 50 nuclei were measured and a number of densitometric features extracted. RESULTS: The existence of two subgroups of cases, identified as lower and higher grade groups, was suggested by a plot of the total optical density versus nuclear area. Two nuclear texture features--the number of pixels with the same optical density value occurring consecutively in the nucleus and the proportion of pixels in the high optical density range--divided the cases into the same subgroups. The use of a clustering algorithm established two clusters that corresponded to that subgrouping except for one case. Discriminant analysis gave an identical classification, with the misplaced case having a borderline discriminant function score. An unsupervised learning algorithm based on an adaptive distance metric formed two clusters and assigned the borderline case to the low grade subgroup. The grouping obtained by quantitative analysis was only partly related to the grade of nuclear atypia subjectively evaluated. CONCLUSION: In our series of medulloblastomas, quantitative analysis provided a means of detecting differences in the nuclear size and texture that allowed the classification of cases into two subgroups.

Adult↗

PubMed1991

Continuous personnel scheduling algorithms: a literature review.

Hospitals frequently use personnel scheduling options as recruiting and retention instruments. The successful application of these personnel scheduling tools, whether developed in-house or purchased from vendors, requires appreciation of the interrelationships of three basic manpower decisions--staffing, personnel scheduling, and allocation. This article introduces these basic relationships and their influence on the development of satisfactory personnel schedules. Next, it reviews published personnel scheduling algorithms, applicable to hospital operations, within the context of the three manpower decisions. It is proposed that scheduling algorithms be classified by type of schedule produced (cyclic or noncyclic); and technique used (heuristic, mathematical programming, or self-scheduling). The characteristics of each classification are discussed. Considerations for the development of new personnel scheduling algorithms are also presented.

Algorithms↗

PubMed2001

Training nu-support vector classifiers: theory and algorithms.

The nu-support vector machine (nu-SVM) for classification proposed by Schölkopf, Smola, Williamson, and Bartlett (2000) has the advantage of using a parameter nu on controlling the number of support vectors. In this article, we investigate the relation between nu-SVM and C-SVM in detail. We show that in general they are two different problems with the same optimal solution set. Hence, we may expect that many numerical aspects of solving them are similar. However, compared to regular C-SVM, the formulation of nu-SVM is more complicated, so up to now there have been no effective methods for solving large-scale nu-SVM. We propose a decomposition method for nu-SVM that is competitive with existing methods for C-SVM. We also discuss the behavior of nu-SVM by some numerical experiments.

Journal Article↗

PubMedDate not supplied

Numerical and chemical classification of Streptosporangium and some related actinomycetes.

One hundred and seventeen streptosporangia from soil were compared with marker strains of the family Streptosporangiaceae for many phenotypic properties. The data were examined using the Jaccard, pattern and simple matching coefficients with clustering achieved using average, complete and single linkage algorithms. Particular confidence was placed in the product of the pattern, average linkage analysis given the sharp definition of aggregate groups and clusters and a combination of low test error and high cophenetic correlation values. The test strains were assigned to five aggregate groups that were equated with the genera Streptosporangium (group A), Microbispora (group B), Planobispora and Planomonospora (Group C), Kutzneria (neé Streptosporangium viridogriseum (group D), and Microtetraspora (group E). The streptosporangia, both isolates and marker strains, were assigned to 5 major, 7 minor and 18 single membered clusters. Representative streptosporangia examined for chemical markers were characterised by the presence of meso-diaminopimelic acid in whole-organism hydrolysates, complex mixtures of straight- and branched chain fatty acids, di- and tetrahydrogenated menaquinones as predominant isoprenologues, and complex polar lipid patterns containing diphosphatidylglycerol, phosphatidylethanolamine, phosphatidylglycerol, phosphatidylmethylethanolamine, phosphatidylinositol, phosphatidylinositol mannosides and uncharacterised components. The chemical and numerical data support the taxonomic integrity of the validly described species of Streptosporangium and suggest that the genus is markedly underspeciated.

Actinomycetales↗

PubMed2025

acmgscaler: an R package and Colab for standardized gene-level variant effect score calibration within the ACMG/AMP framework.

MOTIVATION: A genome-wide variant effect calibration method was recently developed under the guidelines of the American College of Medical Genetics and Genomics and the Association for Molecular Pathology (ACMG/AMP), following ClinGen recommendations for variant classification. While genome-wide approaches offer clinical utility, emerging evidence highlights the need for gene- and context-specific calibration to improve accuracy. Building on previous work, we have developed an algorithm tailored to converting functional scores from both multiplexed assays of variant effects (MAVEs) and computational variant effect predictors (VEPs) into ACMG/AMP evidence strengths. RESULTS: Our method is designed to deliver consistent performance across different genes and score distributions, with all variables adaptively determined from the input data, preventing selective adjustments or overfitting that could inflate evidence strengths beyond empirical support. To facilitate adoption, we introduce acmgscaler, a lightweight R package and a plug-and-play Google Colab notebook for the calibration of custom datasets. This algorithmic framework bridges the gap between MAVEs/VEPs and clinically actionable variant classification. AVAILABILITY AND IMPLEMENTATION: The R package and Colab notebook are available at https://github.com/badonyi/acmgscaler.

Software↗

PubMed1989

A one-layer model of laser-induced fluorescence for diagnosis of disease in human tissue: applications to atherosclerosis.

This paper describes a general model of tissue fluorescence which can be used both to: 1) determine chemical and physical properties of the tissue, and 2) design an optimal algorithm for clinical diagnosis of tissue composition. This model is based on a picture of tissue as a single, optically thick layer, in which fluorophores and absorbing species are homogeneously distributed. As a specific example, the model is applied to the laser induced fluorescence (LIF) of normal and atherosclerotic human aorta using 476 nm excitation. Methods for determining the relevant attenuation and fluorescence lineshapes are detailed, and these lineshapes are used to apply the model to data from 148 samples. The model parameters are related to the concentrations of the major arterial chromophores: structural proteins, hemoglobin and ceroid. In addition, the model parameters are used to derive diagnostic algorithms for the presence of atherosclerosis. Utilizing a binary classification scheme, the presence or absence of pathology was determined correctly in 88 percent of cases.

Algorithms↗

PubMed2001

Acoustic-phonetic features for the automatic classification of fricatives.

In this article, the acoustic-phonetic characteristics of the American English fricative consonants are investigated from the automatic classification standpoint. The features studied in the literature are evaluated and new features are proposed. To test the value of the extracted features, a statistically guided, knowledge-based, acoustic-phonetic system for the automatic classification of fricatives in speaker-independent continuous speech is proposed. The system uses an auditory-based front-end processing system and incorporates new algorithms for the extraction and manipulation of the acoustic-phonetic features that proved to be rich in their information content. Classification experiments are performed using hard-decision algorithms on fricatives extracted from the TIMIT database continuous speech of 60 speakers (not used in the design/training process) from seven different dialects of American English. An accuracy of 93% is obtained for voicing detection, 91% for place of articulation detection, and 87% for the overall classification of fricatives.

Algorithms↗

PubMed2001

A nonparametric scoring algorithm for identifying informative genes from microarray data.

Microarray data routinely contain gene expression levels of thousands of genes. In the context of medical diagnostics, an important problem is to find the genes that are correlated with given phenotypes. These genes may reveal insights to biological processes and may be used to predict the phenotypes of new samples. In most cases, while the gene expression levels are available for a large number of genes, only a small fraction of these genes may be informative in classification with statistical significance. We introduce a nonparametric scoring algorithm that assigns a score to each gene based on samples with known classes. Based on these scores, we can find a small set of genes which are informative of their class, and subsequent analysis can be carried out with this set. This procedure is robust to outliers and different normalization schemes, and immediately reduces the size of the data with little loss of information. We study the properties of this algorithm and apply it to the data set from cancer patients. We quantify the information in a given set of genes by comparing its distribution of the score statistics to a set of distributions generated by permutations that preserve the correlation structure among the genes.

Algorithms↗

PubMed2000

Evaluation of an algorithm for treatment of status epilepticus in adult patients undergoing video/EEG monitoring.

Convulsive or generalized tonic clonic status epilepticus (SE) is a neurological emergency that can lead to transient or permanent brain damage or even death. An algorithm was designed to aid nursing and medical staff members in decision making about the type of SE and pharmacological intervention needed to stop prolonged or repetitive seizures. Fifteen registered nurses at a northern New England medical center's epilepsy unit participated in educational sessions on classification of seizures and status epilepticus prior to use of the algorithm. A pretest-posttest design with an investigator-developed tool was used to measure SE knowledge before and after educational intervention. There was a significant improvement in scores on the posttest of the classification of status epilepticus (Z = -2.93, p = .003). Twenty-nine medical records of patients who had experienced SE between February 1992 and December 1997 were reviewed. Nineteen patients experienced SE before the algorithm was implemented, and 10 patients experienced SE after the algorithm was implemented. A total of 16 patients experienced generalized convulsive SE with 12 episodes occurring before and 4 episodes after algorithm implementation. The mean time taken to stop the episode of SE after pharmacologic treatment began was compared in both groups using a t-test. The mean difference between the groups was 235 minutes (t = 2.57, p = .026). The findings of this project demonstrate that combining a treatment algorithm with education of staff members on its use has benefits in the practice setting of an inpatient comprehensive epilepsy program. Episodes of SE are more accurately classified and successful treatment of the episodes occurs earlier.

Adolescent↗

PubMed1989

Algorithmic sequential decision-making in the frequency domain for life threatening ventricular arrhythmias and imitative artefacts: a diagnostic system.

A preliminary study to approach the problem of reliably detecting life threatening ventricular arrhythmias in real time is described. An algorithm (DIAGNOSIS) has been developed in order to classify ECG signal records on the basis of the computation of four simple parameters calculated from a representation in the frequency domain. This algorithm uses a set of rules constituting an operative classification scheme based on the comparison of the parameters with a set of pre-established thresholds. This allows us to differentiate four general categories: ventricular fibrillation-flutter, ventricular rhythms, imitative artefacts and predominant sinus rhythm.

Algorithms↗

PubMedDate not supplied

[Mathematical modelling and the prognosis of treatment efficacy in papillomavirus infection of the cervix uteri].

In this paper, consideration is given to the problem of mathematical modelling, diagnosis and effects of treatment options on the condition of a patient exposed to papillomavirus infection. The problem is tackled of identifying the most prominent signs of degree of severity of the disease course and of therapy efficiency on the basis of parameters characterizing the immunologic vigor with making use of the covariation matrix eigenvalues algorithm, namely the modelling manifolds algorithm. Such an approach allows the central problem of classification of indices for the immunologic vigor to be settled. A mathematical model as discrimination surface to be used for prediction of results of the treatments administered is constructed.

Algorithms↗

PubMed2001

A proposed taxonomy for nailfold capillaries based on their morphology.

Certain diseases cause permanent changes to the shapes and densities of nailfold capillaries and, therefore, nailfold capillaroscopy is important as a tool for diagnosing and monitoring these diseases. The first aim of the project is to resolve differences in terminology that have developed over the years in previous work. We propose a taxonomy for nailfold capillaries that cover six descriptive classes: cuticulis, open, tortuous, crossed, bushy, and bizarre. The first three are parametric in that they may be distinguished by the ratio of capillary length to width and by the curvature of the capillary limbs. The last three are characterized by their topology; a crossed capillary has a closed area that is not connected to the image background. Bushy and bizarre capillaries have atypical shapes that are characterized by the convex hull of their skeleton. These descriptive classes may be modified according to anomalies in width and length. The second aim is to automate the classification of capillaries by encapsulating the taxonomy in an algorithm; our computer program rivals the most experienced clinicians in classifying capillaries consistently with an overall agreement of 85% with the clinicians' majority view. This was particularly valuable in classifying borderline shapes objectively and consistently.

Capillaries↗

PubMed1994

Automated peak detection and cell cycle analysis of flow cytometric DNA histograms.

We describe an algorithm for fully automated flow cytometric DNA histogram classification and analysis that provides rapid, reproducible determination of DNA index and S-phase fraction (SPF). Automated classification agreed with subjective assessment of DNA ploidy in 96-98% of DNA histograms. Automated and conventional analyses of DNA index (r = 0.95) and SPF (r = 0.89) were also highly correlated with one another. In a series of 86 node-negative breast carcinomas, SPF calculated with the fully automated method was a significant predictor of 10 year survival (p = 0.009). Automation greatly increased the speed of DNA histogram analysis, allowing evaluation of the same set of histograms with different methods. In a preliminary study exploring the optimization of DNA histogram analysis, the best association between SPF and prognosis of breast cancer patients was achieved using sliced nuclei debris modeling, reporting only the aneuploid SPF (in aneuploid histograms), while excluding small aneuploid clones (< 15% of total cell count) from evaluation. In conclusion, automated DNA histogram analysis does not replace the need for close human supervision but provides a useful guideline for less experienced users, facilitates interlaboratory comparisons, and makes possible extensive reanalyses of large data sets.

Breast Neoplasms↗

PubMedDate not supplied

Quantitative self-organizing maps for clustering electron tomograms.

Tomography emerges as a powerful methodology for determining the complex architectures of biological specimens that are better regarded from the structural point of view as singular entities. However, once the structure of a sufficiently large number of singular specimens is solved, quite possibly structural patterns start to emerge. This latter situation is addressed here, where the clustering of a set of 3D reconstructions using a novel quantitative approach is presented. In general terms, we propose a new variant of a self-organizing neural network for the unsupervised classification of 3D reconstructions. The novelty of the algorithm lies in its rigorous mathematical formulation that, starting from a large set of noisy input data, finds a set of "representative" items, organized onto an ordered output map, such that the probability density of this set of representative items resembles at its possible best the probability density of the input data. In this study, we evaluate the feasibility of application of the proposed neural approach to the problem of identifying similar 3D motifs within tomograms of insect flight muscle. Our experimental results prove that this technique is suitable for this type of problem, providing the electron microscopy community with a new tool for exploring large sets of tomogram data to find complex patterns.

Algorithms↗