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PubMed2006

Characterization of surface EMG signals using improved approximate entropy.

An improved approximate entropy (ApEn) is presented and applied to characterize surface electromyography (sEMG) signals. In most previous experiments using nonlinear dynamic analysis, this certain processing was often confronted with the problem of insufficient data points and noisy circumstances, which led to unsatisfactory results. Compared with fractal dimension as well as the standard ApEn, the improved ApEn can extract information underlying sEMG signals more efficiently and accurately. The method introduced here can also be applied to other medium-sized and noisy physiological signals.

Algorithms↗
PubMed1998

Knowledgebased segmentation.

The segmentation of medical images like CT or MRI scans represents a great challenge to researchers in computer vision, due to the variability of the individual anatomy and the different characteristics of the scanning systems. As an anatomical knowledge base improves the recognition of structures in CT or MRI scans, we chose a knowledge based segmentation in our approach, which will be explained in the following.

Algorithms↗
PubMed1999

[Critical review of the categorical structures of the core Conflictual Relationship Theme Method (CCRT)].

The CCRT-method developed by Lester Luborsky is the most widespread and best established method to assess relationship structures in the field of psychodynamic psychotherapy research. Although its categorical structures are criticised in many different ways there have not been any attempts to modify them. This article points out the inconsistency of the current categorical system and demonstrates first approaches to alternative solutions. For this purpose genetic algorithms are used. Their application in psychotherapy research is demonstrated here for the first time.

Algorithms↗
PubMed2002

[Chemical QSAR recognition by using fuzzy min-max neural-network].

By using the fuzzy min-max neural network, the quantitative structure-activity relationship (QSAR) of mutagenicity is studied. With the established QSAR model, the mutagenicity is predicted and the results showed that QASR is superior to linear-regression model. Further discussion on the models and the results is presented in this paper.

Algorithms↗
PubMed2004

Automated segmentation of routinely hematoxylin-eosin-stained microscopic images by combining support vector machine clustering and active contour models.

OBJECTIVE: To develop a method for the automated segmentation of images of routinely hematoxylin-eosin (H-E)-stained microscopic sections to guarantee correct results in computer-assisted microscopy. STUDY DESIGN: Clinical material was composed 50 H-E-stained biopsies of astrocytomas and 50 H-E-stained biopsies of urinary bladder cancer. The basic idea was to use a support vector machine clustering (SVMC) algorithm to provide gross segmentation of regions holding nuclei and subsequently to refine nuclear boundary detection with active contours. The initialization coordinates of the active contour model were defined using a SVMC pixel-based classification algorithm that discriminated nuclear regions from the surrounding tissue. Starting from the boundaries of these regions, the snake fired and propagated until converging to nuclear boundaries. RESULTS: The method was validated for 2 different types of H-E-stained images. Results were evaluated by 2 histopathologists. On average, 94% of nuclei were correctly delineated. CONCLUSION: The proposed algorithm could be of value in computer-based systems for automated interpretation of microscopic images.

Algorithms↗
PubMed2004

Mining yeast transcriptional regulatory modules from factor DNA-binding sites and gene expression data.

UNLABELLED: In eukaryotes, gene expression is controlled by various transcription factors that bind to the promoter regions. Transcription factors may act positively, negatively or not at all. Different combinations of them may also activate or repress gene expression, and form regulatory networks of transcription. Uncovering such regulatory networks is a central challenge in genomic biology. In this study, we first defined a new kind of motifs in regulatory networks, transcriptional regulatory modules (TRMs), with the form factorset --> geneset, which emphasizes the combinatorial gene control of the group of factors factorset on the group of genes geneset. Second, we developed an efficient method based on a closed itemset mining technique for finding the two most informative kinds of TRMs, closed inf-TRMs and closed sup-TRMs, from factor DNA-binding sites and gene expression profiles data. The set of all closed inf-TRMs and closed sup-TRMs is often orders of magnitude smaller than the set of all TRMs but does not loss any information. When being applied to yeast data, our method produced results that are more compact, concise and comprehensive than those from previous studies to identify and interpret the transcriptional role of regulator combinations on sets of genes. AVAILABILITY: Supplementary files: http://www.jaist.ac.jp/~h-pham/regulation/.

Algorithms↗
PubMed2002

An index structure for pattern similarity searching in DNA microarray data.

The DNA microarray technology is about to bring an explosion of gene expression data that may dwarf even the human sequencing projects. Researchers are motivated to identify genes whose expression levels rise and fall coherently under a set of experimental perturbances, that is, they exhibit fluctuation of a similar shape when conditions change. In this paper, we show that queries based on pattern correlations against large-scale microarray databases can be supported by the weighted-sequence model, an index structure designed for sequence matching. A weighted-sequence is a two-dimensional structure where each element in the sequence is associated with a weight. We transform the DNA microarray data, as well as pattern-based queries, into weighted-sequences, and use subsequence matching algorithms to retrieve from the database all genes that match the query pattern. We demonstrate, using both synthetic and real-world data sets, that our method is effective and efficient.

Algorithms↗
PubMed2006

[Medical image segmentation techniques].

Medical image segmentation is an important application of image segmentation. However it is the bottleneck that restrains medical image application in clinical practice. In this paper, the aim and significance of medical image segmentation are discussed, the development of medical image segmentation techniques is sketched, and a review of the medical image segmentation techniques is given.

Algorithms↗
PubMed1991

Probabilistic diagnosis using a reformulation of the INTERNIST-1/QMR knowledge base. I. The probabilistic model and inference algorithms.

In Part I of this two-part series, we report the design of a probabilistic reformulation of the Quick Medical Reference (QMR) diagnostic decision-support tool. We describe a two-level multiply connected belief-network representation of the QMR knowledge base of internal medicine. In the belief-network representation of the QMR knowledge base, we use probabilities derived from the QMR disease profiles, from QMR imports of findings, and from National Center for Health Statistics hospital-discharge statistics. We use a stochastic simulation algorithm for inference on the belief network. This algorithm computes estimates of the posterior marginal probabilities of diseases given a set of findings. In Part II of the series, we compare the performance of QMR to that of our probabilistic system on cases abstracted from continuing medical education materials from Scientific American Medicine. In addition, we analyze empirically several components of the probabilistic model and simulation algorithm.

Algorithms↗
PubMedDate not supplied

Chronobiologic pattern discrimination of plasma hormones, notably DHEA-S and TSH, classifies an expansive personality.

Methods of chronobiologic pattern discrimination, a "monotest' involving the leave-one-out technique and a "maximin distance' algorithm for cluster analysis, along with stepwise discriminant analysis procedures and correlation techniques are used in the search for classifiers of personality. By these methods, chronoendocrine relations of an expansive personality are explored in data sampled around the clock in 4 seasons on 10 North American and 3 Japanese women. Of 12 plasma hormones determined in each season, DHEA-S shows a time-dependent relation to an expansive, egocentric personality assessed by an abbreviated version of the Minnesota Multiphasic Personality Inventory; time-dependent co-classifiers are TSH, LH, prolactin and cortisol. Cost-effective recommendations for sampling are thus proposed for further tests of endocrine factors classifying for an expansive personality and, perhaps, classifying also for any associated vulnerability or risk state.

Adolescent↗
PubMed1995

[Diagnosis and treatment of headache].

In the recent last decades, important advances have been made in the understanding of the mechanisms behind headache. This has resulted in more efficient treatment. New forms of headache have been described where a specific treatment can be used. Therefore, in order to give rational therapy to headache patients, the physician must be able to distinguish between the most important primary headaches. He should also be aware of the warning signals that should prompt further investigations or admission to hospital in search of underlying causes. The article describes the most important primary headaches and the current treatment, and discusses the problem of unclassifiable and untreatable headaches.

Algorithms↗
PubMed2006

Dynamics of the concerted triple proton transfer in cyclic water trimer using the multiconfiguration molecular mechanics algorithm.

Cyclic water clusters are important molecular species to understand the nature of hydrogen bonded networks. Theoretical studies for the dynamics of triple proton transfer in the cyclic water trimer were performed. The potential energy surface (PES) of triple proton transfer is generated by the multiconfiguration molecular mechanics (MCMM) algorithm. We have used the MP2/6-31G(d,p) level for high-level ab initio data (energies, gradients, and Hessians), which are used in the Shepard interpolation. Eight high-level reference points were added step by step, including two points for the critical configurations of the large curvature tunneling paths. The more high-level points are used, the better the potential energy surfaces become. The rate constant and kinetic isotope effect (KIE) for the triple proton transfer at 300 K, which have been calculated by the canonical variational transition-state theory with microcanonical optimized multidimensional semiclassical tunneling approximation, are 1.6 x 10(-3) s(-1) and 230, respectively. Tunneling is very important not only for the triple proton transfer but also for the triple deuterium transfer. The MCMM results show good agreement with those from the direct ab initio dynamics calculations.

Algorithms↗
PubMed1999

Neural networks for visual field analysis: how do they compare with other algorithms?

PURPOSE: To compare the performance of a neural network in identifying visual field defects with the performance of other available algorithms. METHODS: A feed-forward neural network with a single hidden layer was trained to recognize visual field defects previously collected in a longitudinal follow-up glaucoma study, and then tested on fields taken from the same study but not used in the training. The receiver operating characteristics of the network then were compared with the previously determined performance of other algorithms on the same data set. RESULTS: At a specificity greater than 90%, the neural network was more sensitive than any of the available algorithms (although only the global indices were available for comparison, as the cluster and cross-meridional algorithms did not achieve such high specificity at their current settings). At a lower specificity (80-85%), the neural network was unable to attain the high sensitivity of the cluster or cross-meridional algorithms; in fact, the cluster algorithm from the Low-Tension Glaucoma study was significantly more sensitive. CONCLUSION: The receiver operating characteristics of a feed-forward neural network designed to detect visual field defects were explored. At a very high specificity (90-95%) a neural network performed better than the global indices. However, at a lower specificity (78%-88%), the neural network performed worse than cluster and cross-meridional algorithms.

Algorithms↗
PubMed2002

Clustering of proximal sequence space for the identification of protein families.

MOTIVATION: The study of sequence space, and the deciphering of the structure of protein families and subfamilies, has up to now been required for work in comparative genomics and for the prediction of protein function. With the emergence of structural proteomics projects, it is becoming increasingly important to be able to select protein targets for structural studies that will appropriately cover the space of protein sequences, functions and genomic distribution. These problems are the motivation for the development of methods for clustering protein sequences and building families of potentially orthologous sequences, such as those proposed here. RESULTS: First we developed a clustering strategy (Ncut algorithm) capable of forming groups of related sequences by assessing their pairwise relationships. The results presented for the ras super-family of proteins are similar to those produced by other clustering methods, but without the need for clustering the full sequence space. The Ncut clusters are then used as the input to a process of reconstruction of groups with equilibrated genomic composition formed by closely-related sequences. The results of applying this technique to the data set used in the construction of the COG database are very similar to those derived by the human experts responsible for this database. AVAILABILITY: The analysis of different systems, including the COG equivalent 21 genomes are available at http://www.pdg.cnb.uam.es/GenoClustering.html.

Algorithms↗
PubMed2005

Data classification with radial basis function networks based on a novel kernel density estimation algorithm.

This paper presents a novel learning algorithm for efficient construction of the radial basis function (RBF) networks that can deliver the same level of accuracy as the support vector machines (SVMs) in data classification applications. The proposed learning algorithm works by constructing one RBF subnetwork to approximate the probability density function of each class of objects in the training data set. With respect to algorithm design, the main distinction of the proposed learning algorithm is the novel kernel density estimation algorithm that features an average time complexity of O(n log n), where n is the number of samples in the training data set. One important advantage of the proposed learning algorithm, in comparison with the SVM, is that the proposed learning algorithm generally takes far less time to construct a data classifier with an optimized parameter setting. This feature is of significance for many contemporary applications, in particular, for those applications in which new objects are continuously added into an already large database. Another desirable feature of the proposed learning algorithm is that the RBF networks constructed are capable of carrying out data classification with more than two classes of objects in one single run. In other words, unlike with the SVM, there is no need to resort to mechanisms such as one-against-one or one-against-all for handling datasets with more than two classes of objects. The comparison with SVM is of particular interest, because it has been shown in a number of recent studies that SVM generally are able to deliver higher classification accuracy than the other existing data classification algorithms. As the proposed learning algorithm is instance-based, the data reduction issue is also addressed in this paper. One interesting observation in this regard is that, for all three data sets used in data reduction experiments, the number of training samples remaining after a naive data reduction mechanism is applied is quite close to the number of support vectors identified by the SVM software. This paper also compares the performance of the RBF networks constructed with the proposed learning algorithm and those constructed with a conventional cluster-based learning algorithm. The most interesting observation learned is that, with respect to data classification, the distributions of training samples near the boundaries between different classes of objects carry more crucial information than the distributions of samples in the inner parts of the clusters.

Algorithms↗