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Exhaustive enumeration of protein domain families.

Domains are considered as the basic units of protein folding, evolution, and function. Decomposing each protein into modular domains is thus a basic prerequisite for accurate functional classification of biological molecules. Here, we present ADDA, an automatic algorithm for domain decomposition and clustering of all protein domain families. We use alignments derived from an all-on-all sequence comparison to define domains within protein sequences based on a global maximum likelihood model. In all, 90% of domain boundaries are predicted within 10% of domain size when compared with the manual domain definitions given in the SCOP database. A representative database of 249,264 protein sequences were decomposed into 450,462 domains. These domains were clustered on the basis of sequence similarities into 33,879 domain families containing at least two members with less than 40% sequence identity. Validation against family definitions in the manually curated databases SCOP and PFAM indicates almost perfect unification of various large domain families while contamination by unrelated sequences remains at a low level. The global survey of protein-domain space by ADDA confirms that most large and universal domain families are already described in PFAM and/or SMART. However, a survey of the complete set of mobile modules leads to the identification of 1479 new interesting domain families which shuffle around in multi-domain proteins. The data are publicly available at ftp://ftp.ebi.ac.uk/pub/contrib/heger/adda.

Algorithms↗

A hybrid approach to EMG pattern analysis for classification of arm movements using statistical and fuzzy techniques.

In this paper, a hybrid approach is presented for discriminating a few upper limb movements by processing the electromyographic (EMG) signals from selected shoulder muscles. Statistical techniques, such as the Generalized Likelihood Ratio test, the Principal Component Analysis, autoregressive parametric modeling techniques and cepstral analysis techniques, combined with a fuzzy logic based classifier (the Abe-Lan network) are used to construct low-dimensional feature spaces with high classification rates. The experimental results show the ability of the algorithm to correctly classify all the EMG patterns related to the selected planar arm pointing movements. Moreover, the structure presented offers promise for real-time applications because of the low computation costs of the overall algorithm.

Adult↗

Methylation inhibitor therapy in the treatment of myelodysplastic syndrome.

The class of DNA methyltransferase inhibitors is represented by azacitidine and decitabine. Azacitidine is approved for the treatment of patients in both low- and high-risk subtypes of myelodysplastic syndrome (MDS), and decitabine is currently under review by the FDA. Azacitidine phase III trial data, based upon the Cancer and Leukemia Group B (CALGB) study 9221, showed durable clinical and symptomatic improvement in bone marrow function, a reduction in the risk of leukemic transformation, and significant improvements in the quality of life of patients treated with azacitidine compared with supportive care alone. This study also provided data suggestive of improvement in survival in MDS patients. The experience with decitabine comprises a number of phase I/II studies and a phase III trial yet to be published. While there is a strong base of experience supporting the efficacy of DNA methyltransferase inhibitors in the treatment of MDS, a number of practical issues need to be explored further. These include the optimization of the timing and duration of treatment, and the prediction of response to therapy. Along with current experience, future studies will lead to the development of treatment algorithms, strategies for selecting patients (e.g. according to age, risk, classification, and cytogenetic profile), and the combination strategies, particularly with histone deacetylase inhibitors, in the management of MDS.

Antimetabolites, Antineoplastic↗

Supervised machine learning techniques for the classification of metabolic disorders in newborns.

MOTIVATION: During the Bavarian newborn screening programme all newborns have been tested for about 20 inherited metabolic disorders. Owing to the amount and complexity of the generated experimental data, machine learning techniques provide a promising approach to investigate novel patterns in high-dimensional metabolic data which form the source for constructing classification rules with high discriminatory power. RESULTS: Six machine learning techniques have been investigated for their classification accuracy focusing on two metabolic disorders, phenylketo nuria (PKU) and medium-chain acyl-CoA dehydrogenase deficiency (MCADD). Logistic regression analysis led to superior classification rules (sensitivity >96.8%, specificity >99.98%) compared to all investigated algorithms. Including novel constellations of metabolites into the models, the positive predictive value could be strongly increased (PKU 71.9% versus 16.2%, MCADD 88.4% versus 54.6% compared to the established diagnostic markers). Our results clearly prove that the mined data confirm the known and indicate some novel metabolic patterns which may contribute to a better understanding of newborn metabolism.

Algorithms↗

Regularized linear discriminant analysis and its application in microarrays.

In this paper, we introduce a modified version of linear discriminant analysis, called the "shrunken centroids regularized discriminant analysis" (SCRDA). This method generalizes the idea of the "nearest shrunken centroids" (NSC) (Tibshirani and others, 2003) into the classical discriminant analysis. The SCRDA method is specially designed for classification problems in high dimension low sample size situations, for example, microarray data. Through both simulated data and real life data, it is shown that this method performs very well in multivariate classification problems, often outperforms the PAM method (using the NSC algorithm) and can be as competitive as the support vector machines classifiers. It is also suitable for feature elimination purpose and can be used as gene selection method. The open source R package for this method (named "rda") is available on CRAN (http://www.r-project.org) for download and testing.

Computer Simulation↗

Lack of resolution in the animal phylogeny: closely spaced cladogeneses or undetected systematic errors?

A recent phylogenomic study reported that the animal phylogeny was unresolved despite the use of 50 genes. This lack of resolution was interpreted as "a positive signature of closely spaced cladogenetic events." Here, we propose that this lack of resolution is rather due to the mutual cancellation of the phylogenetic signal (historical) and the nonphylogenetic signal (due to systematic errors) that results from inadequate taxon sampling and/or model of sequence evolution. Starting with a data set of comparable size, we use 3 different strategies to reduce the nonphylogenetic signal: 1) increasing the number of species; 2) replacing a fast-evolving species by a slowly evolving one; and 3) using a better model of sequence evolution. In all cases, the phylogenetic resolution is markedly improved, in agreement with our hypothesis that the originally reported lack of resolution was artifactual.

Algorithms↗

Mutual information-based analysis of JPEG2000 contexts.

Context-based arithmetic coding has been widely adopted in image and video compression and is a key component of the new JPEG2000 image compression standard. In this paper, the contexts used in JPEG2000 are analyzed using the mutual information, which is closely related to the compression performance. We first show that, when combining the contexts, the mutual information between the contexts and the encoded data will decrease unless the conditional probability distributions of the combined contexts are the same. Given I, the initial number of contexts, and F, the final desired number of contexts, there are S(I, F) possible context classification schemes where S(I, F) is called the Stirling number of the second kind. The optimal classification scheme is the one that gives the maximum mutual information. Instead of using an exhaustive search, the optimal classification scheme can be obtained through a modified generalized Lloyd algorithm with the relative entropy as the distortion metric. For binary arithmetic coding, the search complexity can be reduced by using dynamic programming. Our experimental results show that the JPEG2000 contexts capture the correlations among the wavelet coefficients very well. At the same time, the number of contexts used as part of the standard can be reduced without loss in the coding performance.

Algorithms↗

A computational approach for peptidomic analysis in taxonomic study of amphibian species.

Peptides in the skin secretion of frogs have been studied for some time now because they frequently possess important biological activity such as antibiotic, antimicrobial, or anticancer properties. In this paper, we present a computational approach for measuring the degree of similarity between the entire peptide complement of the skin secretion of specimens from the same or different species. The first step in the analysis is the generation of a mass spectral profile from an experimental high-performance liquid chromatography/electrosparay ionization analysis of the sample. An "overlap" between the mass spectral profiles of different specimens is then proposed as a measure of their similarity. Analysis of specimens from three species of the genus Litoria, viz., L. Aurea, L. Caerulea, and L. Infrafrenata, and Rana Capito of genus Rana shows that the degree of similarity is highest between specimens from the same species, lower for specimens from different species of the same genus, and lowest between specimens from different genera. This indicates that comparison of skin peptide profiles (i.e., mass spectral profiles of skin secretion) is potentially a useful aid in the taxonomic study of amphibian species.

Algorithms↗

Comparison of linear, nonlinear, and feature selection methods for EEG signal classification.

The reliable operation of brain-computer interfaces (BCIs) based on spontaneous electroencephalogram (EEG) signals requires accurate classification of multichannel EEG. The design of EEG representations and classifiers for BCI are open research questions whose difficulty stems from the need to extract complex spatial and temporal patterns from noisy multidimensional time series obtained from EEG measurements. The high-dimensional and noisy nature of EEG may limit the advantage of nonlinear classification methods over linear ones. This paper reports the results of a linear (linear discriminant analysis) and two nonlinear classifiers (neural networks and support vector machines) applied to the classification of spontaneous EEG during five mental tasks, showing that nonlinear classifiers produce only slightly better classification results. An approach to feature selection based on genetic algorithms is also presented with preliminary results of application to EEG during finger movement.

Algorithms↗

An empirical risk functional to improve learning in a neuro-fuzzy classifier.

The paper proposes a new Empirical Risk Functional as cost function for training neuro-fuzzy classifiers. This cost function, called Approximate Differentiable Empirical Risk Functional (ADERF), provides a differentiable approximation of the misclassification rate so that the Empirical Risk Minimization Principle formulated in Vapnik's Statistical Learning Theory can be applied. Also, based on the proposed ADERF, a learning algorithm is formulated. Experimental results on a number of benchmark classification tasks are provided and comparison to alternative approaches given.

Journal Article↗

Bayesian model averaging of naive Bayes for clustering.

This paper considers a Bayesian model-averaging (MA) approach to learn an unsupervised naive Bayes classification model. By using the expectation model-averaging (EMA) algorithm, which is proposed in this paper, a unique naive Bayes model that approximates an MA over selective naive Bayes structures is obtained. This algorithm allows to obtain the parameters for the approximate MA clustering model in the same time complexity needed to learn the maximum-likelihood model with the expectation-maximization algorithm. On the other hand, the proposed method can also be regarded as an approach to an unsupervised feature subset selection due to the fact that the model obtained by the EMA algorithm incorporates information on how dependent every predictive variable is on the cluster variable.

Algorithms↗

Reflectance spectroscopy for in vivo detection of cervical precancer.

Optical technologies, in particular fluorescence spectroscopy, have shown the potential to provide improved detection methods for cervical neoplasia that are sensitive and cost effective through accurate, objective, instantaneous point-of-care diagnostic tools. The specific goals of this study were to analyze reflectance spectra of normal and neoplastic cervical tissue in vivo and to evaluate the data for use in diagnostic algorithm development. Spectroscopic measurements were obtained at four distinct source-detector separations from 324 sites in 161 patients. As the source-detector separation increases, greater tissue depth is probed. The average spectra of each diagnostic class differed at all source-detector separations, with the greatest differences occurring at the smallest source-detector separations. Algorithms, based on principal-component analysis and Mahalanobis distance classification, were developed and evaluated for all combinations of source-detector separations relative to the gold standard of colposcopically directed biopsy. The diagnostic combination of squamous normal versus high-grade squamous intraepithelial lesions gave good discrimination with a sensitivity of 72% and a specificity of 81%; discrimination of columnar normal versus high-grade squamous intraepithelial lesions also was good, with sensitivity of 72% and specificity of 83%. Thus, reflectance spectroscopy appears promising for in vivo detection of cervical precancer. Strategies that combine fluorescence and reflectance spectroscopy may enhance the discrimination capabilities.

Female↗

The distribution of the paternity index as a basis for evaluation of sequential testing in paternity analysis.

Several procedures for evaluation of paternity testing data have been suggested in the literature, the majority of them being based on the paternity index statistic (L) or some transform of it. A major problem has been that the true distribution of the paternity index has not been known, making it difficult to perform quantitative evaluations of different procedures. We present an algorithm for computation of the distribution of the paternity index within the limits of a completely controlled amount of approximation. Using this algorithm we evaluate the power and the rate of erroneous classifications of a standard routine test based on a fixed number of genetic marker systems. The efficiency of this standard test procedure is compared to a stepwise (sequential) procedure where in each step one or several marker systems are scored for the mother-child-putative father trio. We suggest that a sequential strategy for testing may be more efficient than one that is based on a fixed number of systems. A sequential procedure can provide information about the accused man's state of paternity in a considerably larger fraction of cases without a substantial increase of the frequency of incorrect classifications. In addition, the cost measured as the average number of marker systems that has to be tested for each trio may be lower in the case of sequential testing than with a fixed number of systems.

Blood Group Antigens↗

High-sensitivity C-reactive protein: potential adjunct for global risk assessment in the primary prevention of cardiovascular disease.

Inflammation plays a major role in atherothrombosis, and measurement of inflammatory markers such as high-sensitivity C-reactive protein (HSCRP) may provide a novel method for detecting individuals at high risk of plaque rupture. Several large-scale prospective studies demonstrate that HSCRP is a strong independent predictor of future myocardial infarction and stroke among apparently healthy men and women and that the addition of HSCRP to standard lipid screening may improve global risk prediction among those with high as well as low cholesterol levels. Because agents such as aspirin and statins seem to attenuate inflammatory risk, HSCRP may also have utility in targeting proven therapies for primary prevention. Inexpensive commercial assays for HSCRP are now available; they have shown variability and classification accuracy similar to that of cholesterol screening. Risk prediction algorithms using a simple quintile approach to HSCRP evaluation have been developed for outpatient use. Thus, although limitations inherent to inflammatory screening remain, available data suggest that HSCRP has the potential to play an important role as an adjunct for global risk assessment in the primary prevention of cardiovascular disease.

Algorithms↗

Automated global structure extraction for effective local building block processing in XCS.

Learning Classifier Systems (LCSs), such as the accuracy-based XCS, evolve distributed problem solutions represented by a population of rules. During evolution, features are specialized, propagated, and recombined to provide increasingly accurate subsolutions. Recently, it was shown that, as in conventional genetic algorithms (GAs), some problems require efficient processing of subsets of features to find problem solutions efficiently. In such problems, standard variation operators of genetic and evolutionary algorithms used in LCSs suffer from potential disruption of groups of interacting features, resulting in poor performance. This paper introduces efficient crossover operators to XCS by incorporating techniques derived from competent GAs: the extended compact GA (ECGA) and the Bayesian optimization algorithm (BOA). Instead of simple crossover operators such as uniform crossover or one-point crossover, ECGA or BOA-derived mechanisms are used to build a probabilistic model of the global population and to generate offspring classifiers locally using the model. Several offspring generation variations are introduced and evaluated. The results show that it is possible to achieve performance similar to runs with an informed crossover operator that is specifically designed to yield ideal problem-dependent exploration, exploiting provided problem structure information. Thus, we create the first competent LCSs, XCS/ECGA and XCS/BOA, that detect dependency structures online and propagate corresponding lower-level dependency structures effectively without any information about these structures given in advance.

Algorithms↗

Echographic tissue characterization in diffuse parenchymal liver disease: correlation of image structure with histology.

Seventy livers were examined in vitro using a computerized ultrasound B-mode data acquisition and analysis system. For tissue characterization, statistical parameters from pattern recognition algorithms describing image brightness and image structure were used. Reference classification based on histopathology as well as on chemical/morphometrical analysis led to the diagnostic classes of normal, fatty liver, fibrosis/cirrhosis and fatty fibrosis/cirrhosis. Comparing the two reference methods for ultrasound tissue characterization, reclassification based on chemical/morphometrical analysis resulted in a significant increase in diagnostic accuracy. The strong correlations between statistical ultrasound image parameters and morphometrical features reflect the relevance of our statistical approach to ultrasound tissue characterization.

Fatty Liver↗

Maximum-likelihood estimation of coalescence times in genealogical trees.

We develop a method for maximum-likelihood estimation of coalescence times in genealogical trees, based on population genetics data. For this purpose, a Viterbi-type algorithm is constructed to maximize the joint likelihood of the coalescence times. Marginal confidence intervals for the coalescence times based on the profile likelihoods are also computed. Our method of finding MLEs and calculating C.I.'s appears to be more accurate than alternative numerical maximization methods, and maximum-likelihood inference appears to be more accurate than other existing model-free approaches to estimating coalescent times. We demonstrate the method on two different data sets: human Y chromosome DNA data and fungus DNA data.

Algorithms↗