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PubMed1989

The P-A-I-N MMPI classification system: a critical review.

The Costello et al. (Pain, 30 (1987) 199-209) literature-based MMPI clustering algorithm was compared to a standard clustering procedure of chronic pain patients' MMPI profiles. Results indicated that the Costello algorithm was too restrictive, failing to classify 69% of the MMPI profiles of the local sample. It was suggested that it may be premature to adopt a literature based clustering method until the validity of empirically derived clusters has been more thoroughly determined. The utility of a given set of clusters may best be determined by using locally derived clusters to predict treatment response. Once the predictive validity of the local clusters has been demonstrated, the Costello approach may prove more useful.

Adolescent↗

PubMed2004

Karyometry detects subvisual differences in chromatin organisation state between non-recurrent and recurrent papillary urothelial neoplasms of low malignant potential.

AIM: To analyse nuclear chromatin texture in non-recurrent and recurrent papillary urothelial neoplasms of low malignant potential (PUNLMPs). MATERIALS: Ninety three karyometric features were analysed on haematoxylin and eosin stained sections from 20 PUNLMP cases: 10 from patients with a solitary PUNLMP lesion, who were disease free during at least eight years' follow up, and 10 from patients with unifocal PUNLMP, one or more recurrences being seen during follow up. RESULTS: Kruskal-Wallis analysis was used to search for features showing significant differences between recurrent and non-recurrent cases. Significance was better than p<0.005 for more than 20 features. Based on significance, six texture features were selected for discriminant analysis. Stepwise linear discriminant analysis reduced Wilk's lambda to 0.87, indicating a highly significant difference between the two multivariate data sets, but only modest ability to discriminate (70% correct case classification). A box sequential classifier was used based on data derived from discriminant analysis. The classifier took three classification steps and classified 19 of the 20 cases correctly (95% correct case classification). To determine whether significant case grouping could also be obtained based on an objective criterion, the merged data sets of non-recurrent and recurrent cases were submitted to the unsupervised learning algorithm P-index. Two clusters were formed with significant differences. The subsequent application of a Cooley/Lohnes classifier resulted in an overall correct case classification rate of 85%. CONCLUSIONS: Karyometry and multivariate analyses detect subvisual differences in chromatin organisation state between non-recurrent and recurrent PUNLMPs, thus allowing identification of lesions that do or do not recur.

Algorithms↗

PubMed2005

Oblique decision trees for spatial pattern detection: optimal algorithm and application to malaria risk.

BACKGROUND: In order to detect potential disease clusters where a putative source cannot be specified, classical procedures scan the geographical area with circular windows through a specified grid imposed to the map. However, the choice of the windows' shapes, sizes and centers is critical and different choices may not provide exactly the same results. The aim of our work was to use an Oblique Decision Tree model (ODT) which provides potential clusters without pre-specifying shapes, sizes or centers. For this purpose, we have developed an ODT-algorithm to find an oblique partition of the space defined by the geographic coordinates. METHODS: ODT is based on the classification and regression tree (CART). As CART finds out rectangular partitions of the covariate space, ODT provides oblique partitions maximizing the interclass variance of the independent variable. Since it is a NP-Hard problem in RN, classical ODT-algorithms use evolutionary procedures or heuristics. We have developed an optimal ODT-algorithm in R2, based on the directions defined by each couple of point locations. This partition provided potential clusters which can be tested with Monte-Carlo inference. We applied the ODT-model to a dataset in order to identify potential high risk clusters of malaria in a village in Western Africa during the dry season. The ODT results were compared with those of the Kulldorff' s SaTScan. RESULTS: The ODT procedure provided four classes of risk of infection. In the first high risk class 60%, 95% confidence interval (CI95%) [52.22-67.55], of the children was infected. Monte-Carlo inference showed that the spatial pattern issued from the ODT-model was significant (p < 0.0001). Satscan results yielded one significant cluster where the risk of disease was high with an infectious rate of 54.21%, CI95% [47.51-60.75]. Obviously, his center was located within the first high risk ODT class. Both procedures provided similar results identifying a high risk cluster in the western part of the village where a mosquito breeding point was located. CONCLUSION: ODT-models improve the classical scanning procedures by detecting potential disease clusters independently of any specification of the shapes, sizes or centers of the clusters.

Africa, Western↗

PubMed2003

Algorithms for large-scale genotyping microarrays.

MOTIVATION: Analysis of many thousands of single nucleotide polymorphisms (SNPs) across whole genome is crucial to efficiently map disease genes and understanding susceptibility to diseases, drug efficacy and side effects for different populations and individuals. High density oligonucleotide microarrays provide the possibility for such analysis with reasonable cost. Such analysis requires accurate, reliable methods for feature extraction, classification, statistical modeling and filtering. RESULTS: We propose the modified partitioning around medoids as a classification method for relative allele signals. We use the average silhouette width, separation and other quantities as quality measures for genotyping classification. We form robust statistical models based on the classification results and use these models to make genotype calls and calculate quality measures of calls. We apply our algorithms to several different genotyping microarrays. We use reference types, informative Mendelian relationship in families, and leave-one-out cross validation to verify our results. The concordance rates with the single base extension reference types are 99.36% for the SNPs on autosomes and 99.64% for the SNPs on sex chromosomes. The concordance of the leave-one-out test is over 99.5% and is 99.9% higher for AA, AB and BB cells. We also provide a method to determine the gender of a sample based on the heterozygous call rate of SNPs on the X chromosome. See http://www.affymetrix.com for further information. The microarray data will also be available from the Affymetrix web site. AVAILABILITY: The algorithms will be available commercially in the Affymetrix software package.

Algorithms↗

PubMed2004

Align-m--a new algorithm for multiple alignment of highly divergent sequences.

MOTIVATION: Multiple alignment of highly divergent sequences is a challenging problem for which available programs tend to show poor performance. Generally, this is due to a scoring function that does not describe biological reality accurately enough or a heuristic that cannot explore solution space efficiently enough. In this respect, we present a new program, Align-m, that uses a non-progressive local approach to guide a global alignment. RESULTS: Two large test sets were used that represent the entire SCOP classification and cover sequence similarities between 0 and 50% identity. Performance was compared with the publicly available algorithms ClustalW, T-Coffee and DiAlign. In general, Align-m has comparable or slightly higher accuracy in terms of correctly aligned residues, especially for distantly related sequences. Importantly, it aligns much fewer residues incorrectly, with average differences of over 15% compared with some of the other algorithms. AVAILABILITY: Align-m and the test sets are available at http://bioinformatics.vub.ac.be

Algorithms↗

PubMed1997

Applying local statistics to detect subtle G-bands in chromosomes.

Genetic disorders manifested in chromosome aberrations can be evaluated by analysis of the banding pattern of human metaphase chromosomes. We describe the resolution of chromosomal bands depending on the degree of chromosomal condensation and introduce a pattern enhancement technique using local statistics to improve band pattern features of a past condensation stage. The algorithm was first tested on synthesized profiles to determine its characteristics as the parameters of the algorithm are varied. It is shown that the method is capable of enhancing subtle staining patterns hidden in artificial profiles as well as in measured profiles. The example of a human chromosome No. 11 demonstrates the importance of the described technique for chromosome classification in ambiguous cases.

Algorithms↗

PubMed2000

Median neuropathy at the wrist: diagnostic utility of clinical findings and an automated electrodiagnostic device.

Clinical findings have limited value in predicting electrophysiologically confirmed median neuropathy at the wrist (MNW). To determine the value of clinical findings and an automated electrophysiologic neurodiagnostic device (AEND) in diagnosing MNW, we studied two groups of 75 consecutive patients (an initial group and a validation group, 150 total) referred to an academic electrophysiology laboratory for upper extremity complaints. The definitive standard for MNW was the neurologist's diagnosis after formal clinical and electrodiagnostic evaluation. The neurologist was blinded to the results of the AEND (NC-Stat, NeuroMetrix, Inc). In the validation group, the AEND yielded a distal motor latency (DML) in 97% of hands with a conventional motor response, and the correlation of the AEND DML with the conventional DML was 0.94 (P < 0.001). Of 248 symptomatic hands, the neurologist diagnosed 117 (47%) with MNW. At 90% specificity, the AEND DML had a sensitivity of 86% for MNW. Age, body mass index, sensory symptoms in digits 1 to 3, and nocturnal awakening were independent clinical predictors of MNW. Each 1-msec increase in the adjusted AEND DML was independently associated with an OR of 298 (95% confidence interval, 40 to 2233) for MNW. Each 1-msec increase in the F-wave latency was independently associated with an OR of 2.6 (95% confidence interval, 1.3 to 4.9) for MNW. Compared with a model based solely on clinical variables, an algorithm including symptom variables plus the AEND DML had an odds ratio for correct diagnostic classification of 6.3 (95% confidence interval, 3.8 to 12.3). The sensitivity at 90% specificity improved from 40% for the clinical model to 86% for the model with DML. A practical method for integrating clinical and electrophysiologic findings to assess the risk of MNW was proposed. This method correctly stratified 79% of control and MNW patients into very low- and high-risk groups, respectively. We concluded that MNW diagnosis is significantly improved with an AEND.

Adult↗

PubMed2005

Normalization of multicolor fluorescence in situ hybridization (M-FISH) images for improving color karyotyping.

BACKGROUND: Multiplex or multicolor fluorescence in situ hybridization (M-FISH) is a recently developed cytogenetic technique for cancer diagnosis and research on genetic disorders. By simultaneously viewing the multiply labeled specimens in different color channels, M-FISH facilitates the detection of subtle chromosomal aberrations. The success of this technique largely depends on the accuracy of pixel classification (color karyotyping). Improvements in classifier performance would allow the elucidation of more complex and more subtle chromosomal rearrangements. Normalization of M-FISH images has a significant effect on the accuracy of classification. In particular, misalignment or misregistration across multiple channels seriously affects classification accuracy. Image normalization, including automated registration, must be done before pixel classification. METHODS AND RESULTS: We studied several image normalization approaches that affect image classification. In particular, we developed an automated registration technique to correct misalignment across the different fluor images (caused by chromatic aberration and other factors). This new registration algorithm is based on wavelets and spline approximations that have computational advantages and improved accuracy. To evaluate the performance improvement brought about by these data normalization approaches, we used the downstream pixel classification accuracy as a measurement. A Bayesian classifier assumed that each of 24 chromosome classes had a normal probability distribution. The effects that this registration and other normalization steps have on subsequent classification accuracy were evaluated on a comprehensive M-FISH database established by Advanced Digital Imaging Research (http://www.adires.com/05/Project/MFISH_DB/MFISH_DB.shtml). CONCLUSIONS: Pixel misclassification errors result from different factors. These include uneven hybridization, spectral overlap among fluors, and image misregistration. Effective preprocessing of M-FISH images can decrease the effects of those factors and thereby increase pixel classification accuracy. The data normalization steps described in this report, such as image registration and background flattening, can significantly improve subsequent classification accuracy. An improved classifier in turn would allow subtle DNA rearrangements to be identified in genetic diagnosis and cancer research.

Algorithms↗

PubMed2005

An evidence-based causative classification system for acute ischemic stroke.

Regular, evidence-based assignment of patients to etiologic stroke categories is essential to enable valid comparison among studies. We designed an algorithm (SSS-TOAST) that incorporated recent advances in stroke imaging and epidemiology to identify the most probable TOAST category in the presence of evidence for multiple mechanisms. Based on the weight of evidence, each TOAST subtype was subdivided into 3 subcategories as "evident", "probable", or "possible". Classification into the subcategories was determined via predefined specific clinical and imaging criteria. These criteria included published risks of ischemic stroke from various mechanisms and published reports of the strength of associations between clinical and imaging features and particular stroke mechanisms. Two neurologists independently assessed 50 consecutively admitted patients with acute ischemic stroke through reviews of abstracted data from medical records. The number of patients classified as "undetermined-unclassified" per the original TOAST system decreased from 38-40% to 4% using the SSS-TOAST system. The kappa value for inter-examiner reliability was 0.78 and 0.90 for the original TOAST and SSS-TOAST respectively. The SSS-TOAST system successfully classifies patients with acute ischemic stroke into determined etiologic categories without sacrificing reliability. The SSS-TOAST is a dynamic algorithm that can accommodate modifications as new epidemiological data accumulate and diagnostic techniques advance.

Adult↗

PubMed1998

In vivo apparent affinity and efficacy estimates for mu opiates in a rat tail-withdrawal assay.

Experiments in a rat tail-withdrawal assay tested the hypothesis that the magnitude and pattern of antagonism of mu opiate agonists by the insurmountable mu opioid antagonist clocinnamox are inversely related to agonist efficacy. In addition, these experiments examined whether this antagonism could be quantified to yield apparent affinity and efficacy estimates for the pharmacological characterization of five opiate agonists. Etonitazene, etorphine, morphine, buprenorphine, and GPA 1657 produced dose-dependent increases in tail-withdrawal latency until 100% maximum possible effect (%MPE) was obtained. Morphine required a higher dose of clocinnamox for a 50% reduction in maximal antinociceptive effect than did buprenorphine or GPA 1657. In contrast, no dose of clocinnamox tested decreased the %MPE for etonitazene or etorphine. These data suggest a rank order of relative efficacy of etonitazene > or = etorphine > morphine > or = GPA 1657 > or = buprenorphine. Similarly, numerical analysis of these data yielded the following apparent affinity and efficacy estimates: etonitazene (0.38 mg/kg, 128); etorphine (0.68 mg/kg, 125); morphine (50 mg/kg, 38), GPA 1657 (6.6, 39); and buprenorphine (0.042 mg/kg, 2.2). These data illustrate that in vivo affinity and efficacy estimates for a number of agonists are remarkably similar across different methods of analysis and are useful for drug classification.

Algorithms↗

PubMed1998

A structural tree for proteins containing S-like beta-sheets.

A structural tree for proteins and domains containing S-like beta-sheets has been constructed. An S-like beta-sheet is taken as a starting structure in modelling or as a root structure of the tree. Larger structures are obtained by a stepwise addition of beta-strands and/or alpha-helices to the root S-like beta-sheet in accordance with a restricted set of rules inferred from known principles of protein structure. Applications of the structural tree to structure comparison, protein classification and protein folding are described.

Algorithms↗

PubMedDate not supplied

Characterizing bitterness: identification of key structural features and development of a classification model.

This work describes the first approach in the development of a comprehensive classification method for bitterness of small molecules. The data set comprises 649 bitter and 13 530 randomly selected molecules from the MDL Drug Data Repository (MDDR) which are analyzed by circular fingerprints (MOLPRINT 2D) and information-gain feature selection. The feature selection proposes substructural features which are statistically correlated to bitterness. Classification is performed on the selected features via a naïve Bayes classifier. The substructural features upon which the classification is based are able to discriminate between bitter and random compounds, and thus we propose they are also functionally responsible for causing the bitter taste. Such substructures include various sugar moieties as well as highly branched carbon scaffolds. Cynaropicrine contains a number of the substructural features found to be statistically associated with bitterness and thus was correctly predicted to be bitter by our model. Alternatively, both promethazine and saccharin contain fewer of these substructural features, and thus the bitterness in these compounds was not identified. Two different classes of bitter compounds were identified, namely those which are larger and contain mainly oxygen and carbon and often sugar moieties, and those which are rather smaller and contain additional nitrogen and/or sulfur fragments. The classifier is able to predict 72.1% of the bitter compounds. Feature selection reduces the number of false-positives while also increasing the number of false negatives to 69.5% of bitter compounds correctly predicted. Overall, the method presented here presents both one of the largest databases of bitter compounds presently available as well as a relatively reliable classification method.

Algorithms↗

PubMed1998

Unreliability of the Chou-Fasman parameters in predicting protein secondary structure.

The Chou-Fasman method has been widely used for predicting protein secondary structure. It is based on knowledge of the potential of amino acid residues to form alpha-helical or beta-sheet regions in proteins. Our main interest in this study was to examine the reliability of these Chou-Fasman parameters. We calculated the Chou-Fasman parameters, with 95% confidence limits, of 144 non-homologous proteins consisting of 155 chains, and a total of 33 118 amino acid residues. All of the protein chains used were X-ray structures known at a resolution of at least 2.5 A. We compared the results of our calculations with those previously done by Chou and Fasman. Our results show that Chou and Fasman classified four amino acid residues wrongly in alpha-helical regions and one in a beta-sheet region. This is so, because the confidence limits we calculated did not include the values determined by Chou and Fasman. Moreover, the confidence limit calculations contradict most of the Chou-Fasman classification of amino acid residues.

Algorithms↗

PubMedDate not supplied

Dimension reduction-based penalized logistic regression for cancer classification using microarray data.

The use of penalized logistic regression for cancer classification using microarray expression data is presented. Two dimension reduction methods are respectively combined with the penalized logistic regression so that both the classification accuracy and computational speed are enhanced. Two other machine-learning methods, support vector machines and least-squares regression, have been chosen for comparison. It is shown that our methods have achieved at least equal or better results. They also have the advantage that the output probability can be explicitly given and the regression coefficients are easier to interpret. Several other aspects, such as the selection of penalty parameters and components, pertinent to the application of our methods for cancer classification are also discussed.

Algorithms↗

PubMed2006

Lidar detection of underwater objects using a neuro-SVM-based architecture.

This paper presents a neural network architecture using a support vector machine (SVM) as an inference engine (IE) for classification of light detection and ranging (Lidar) data. Lidar data gives a sequence of laser backscatter intensities obtained from laser shots generated from an airborne object at various altitudes above the earth surface. Lidar data is pre-filtered to remove high frequency noise. As the Lidar shots are taken from above the earth surface, it has some air backscatter information, which is of no importance for detecting underwater objects. Because of these, the air backscatter information is eliminated from the data and a segment of this data is subsequently selected to extract features for classification. This is then encoded using linear predictive coding (LPC) and polynomial approximation. The coefficients thus generated are used as inputs to the two branches of a parallel neural architecture. The decisions obtained from the two branches are vector multiplied and the result is fed to an SVM-based IE that presents the final inference. Two parallel neural architectures using multilayer perception (MLP) and hybrid radial basis function (HRBF) are considered in this paper. The proposed structure fits the Lidar data classification task well due to the inherent classification efficiency of neural networks and accurate decision-making capability of SVM. A Bayesian classifier and a quadratic classifier were considered for the Lidar data classification task but they failed to offer high prediction accuracy. Furthermore, a single-layered artificial neural network (ANN) classifier was also considered and it failed to offer good accuracy. The parallel ANN architecture proposed in this paper offers high prediction accuracy (98.9%) and is found to be the most suitable architecture for the proposed task of Lidar data classification.

Algorithms↗

PubMed2005

On utilizing search methods to select subspace dimensions for kernel-based nonlinear subspace classifiers.

In Kernel-based Nonlinear Subspace (KNS) methods, the subspace dimensions have a strong influence on the performance of the subspace classifier. In order to get a high classification accuracy, a large dimension is generally required. However, if the chosen subspace dimension is too large, it leads to a low performance due to the overlapping of the resultant subspaces and, if it is too small, it increases the classification error due to the poor resulting approximation. The most common approach is of an ad hoc nature, which selects the dimensions based on the so-called cumulative proportion computed from the kernel matrix for each class. In this paper, we propose a new method of systematically and efficiently selecting optimal or near-optimal subspace dimensions for KNS classifiers using a search strategy and a heuristic function termed the Overlapping criterion. The rationale for this function has been motivated in the body of the paper. The task of selecting optimal subspace dimensions is reduced to finding the best ones from a given problem-domain solution space using this criterion as a heuristic function. Thus, the search space can be pruned to very efficiently find the best solution. Our experimental results demonstrate that the proposed mechanism selects the dimensions efficiently without sacrificing the classification accuracy.

Algorithms↗

PubMed1997

Opening the black box: the relationship between neural networks and linear discriminant functions.

Over the last ten years feed-forward neural networks have become a popular tool for statistical decision making. During this time, they have been applied in many fields, including cytological classification. Neural networks are often treated as a black box, whose inner workings are concealed from the researcher. This is unfortunate, since the inner workings of a neural network can be understood in a manner similar to that of a linear discriminant function, which is the standard tool that researchers use for decision making. This paper discusses feed-forward neural networks and some methods to improve their performance for classification problems. Their relationship to discriminant functions will be examined for a simple two-dimensional classification problem.

Algorithms↗

PubMed2001

Automated detection of connective tissue by tissue counter analysis and classification and regression trees.

OBJECTIVE: To evaluate the feasibility of the CART (Classification and Regression Tree) procedure for the recognition of microscopic structures in tissue counter analysis. METHODS: Digital microscopic images of H & E; stained slides of normal human skin and of primary malignant melanoma were overlayed with regularly distributed square measuring masks (elements) and grey value, texture and colour features within each mask were recorded. In the learning set, elements were interactively labeled as representing either connective tissue of the reticular dermis, other tissue components or background. Subsequently, CART models were based on these data sets. RESULTS: Implementation of the CART classification rules into the image analysis program showed that in an independent test set 94.1% of elements classified as connective tissue of the reticular dermis were correctly labeled. Automated measurements of the total amount of tissue and of the amount of connective tissue within a slide showed high reproducibility (r=0.97 and r=0.94, respectively; p<0.001). CONCLUSIONS: CART procedure in tissue counter analysis yields simple and reproducible classification rules for tissue elements.

Algorithms↗

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