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Results for “binary vectorization”

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PubMed2000

Development and assessment of a general theory of cervical carcinogenesis utilizing a severe combined immunodeficiency murine-human xenograft model.

OBJECTIVE: Currently, we lack a theoretical explanation for why squamous cell cervical cancer develops predominantly in specific sites (i.e., along the squamocolumnar junction). We therefore implanted human cervical tissues containing the transformation zone in severe combined immunodeficiency (SCID) mice and studied morphology, steroid effects, gene expression, and human papillomavirus (HPV) factors. METHODS: Normal and dysplastic human cervical tissues (3 x 2 mm) were placed subcutaneously in SCID-beige mice and later assessed by in situ hybridization for HPV 16/18 DNA and by immunohistochemistry for expression of CD31, keratin, proliferating-cell nuclear antigen, HPV 16 E6, p53, and Notch-1 (a binary cell fate determination protein). Some normal tissues were implanted with either a 90-day release 1.7-mg 17beta-estradiol pellet or a 5-mg tamoxifen pellet; others were infected prior to implantation with human recombinant adenovirus 5 vector containing a human cytomegalovirus promoter-driven beta-galactosidase gene and later assessed by X-gal staining. RESULTS: Murine and human vessels formed anastomoses by 3 weeks. For at least 11 weeks, normal tissue retained the transformation zone and normal cell-type-specific keratin expression and exhibited normal proliferation; Notch-1 was present only in the basal cell layer. Dysplastic tissues exhibited koilocytosis, increased levels of cellular proliferation, and aberrant keratin, p53, and Notch-1 expression; HPV 16/18 DNA and HPV 16 E6 protein were detected for at least 6 weeks. Squamous metaplasia of normal cervical epithelium resulted from estrogen exposure, and a predominant columnar differentiation pattern was associated with tamoxifen administration. Through stable adenovirus infection, beta-galactosidase was expressed for at least 6 weeks. CONCLUSIONS: This small manipulatable xenograft model maintains normal and dysplastic human cervical epithelium through neovascularization. Neoplastic tissue retains HPV 16/18 DNA and a premalignant phenotype, including elevated levels of cellular proliferation and aberrant keratin, p53, and Notch-1 expression. These attributes constitute essential features of a biologic model through which one may study HPV-mediated human disease and may be superior to cell culture and transgenic murine systems. Furthermore, this may serve as a model for gene therapy. Finally, we suggest that the normal cervical epithelium is maintained through putative interactions between the Notch locus and cell cycle growth regulators such as p53 and pRb. Neoplastic cervical epithelium may arise through disruption of this pathway. This theory may be testable in our animal model.

Animals↗

PubMed1988

Estimators and tests in the analysis of multiple nonindependent 2 x 2 tables with partially missing observations.

We present methods for the analysis of a K-variate binary measure for two independent groups where some observations may be incomplete, as in the case of K repeated measures in a comparative trial. For the K 2 X 2 tables, let theta = (theta 1,..., theta K) be a vector of association parameters where theta k is a measure of association that is a continuous function of the probabilities pi ik in each group (i = 1, 2; k = 1,..., K), such as the log odds ratio or log relative risk. The asymptotic distribution of the estimates theta = (theta 1,..., theta K) is derived. Under the assumption that theta k = theta for all k, we describe the maximally efficient linear estimator theta of the common parameter theta. Tests of contrasts on the theta are presented which provide a test of homogeneity Ha: theta k = theta l for all k not equal to l. We then present maximally efficient tests of aggregate association Hb: theta = theta 0, where theta 0 is a given value. It is shown that the test of aggregate association Hb is asymptotically independent of the preliminary test of homogeneity Ha. These methods generalize the efficient estimators of Gart (1962, Biometrics 18, 601-610), and the Cochran (1954, Biometrics 10, 417-451), Mantel-Haenszel (1959, Journal of the National Cancer Institute 22, 719-748), and Radhakrishna (1965, Biometrics 21, 86-98) tests to nonindependent tables. The methods are illustrated with an analysis of repeated morphologic evaluations of liver biopsies obtained in the National Cooperative Gallstone Study.

Analysis of Variance↗

PubMed2005

Predicting rRNA-, RNA-, and DNA-binding proteins from primary structure with support vector machines.

In the post-genome era, the prediction of protein function is one of the most demanding tasks in the study of bioinformatics. Machine learning methods, such as the support vector machines (SVMs), greatly help to improve the classification of protein function. In this work, we integrated SVMs, protein sequence amino acid composition, and associated physicochemical properties into the study of nucleic-acid-binding proteins prediction. We developed the binary classifications for rRNA-, RNA-, DNA-binding proteins that play an important role in the control of many cell processes. Each SVM predicts whether a protein belongs to rRNA-, RNA-, or DNA-binding protein class. Self-consistency and jackknife tests were performed on the protein data sets in which the sequences identity was < 25%. Test results show that the accuracies of rRNA-, RNA-, DNA-binding SVMs predictions are approximately 84%, approximately 78%, approximately 72%, respectively. The predictions were also performed on the ambiguous and negative data set. The results demonstrate that the predicted scores of proteins in the ambiguous data set by RNA- and DNA-binding SVM models were distributed around zero, while most proteins in the negative data set were predicted as negative scores by all three SVMs. The score distributions agree well with the prior knowledge of those proteins and show the effectiveness of sequence associated physicochemical properties in the protein function prediction. The software is available from the author upon request.

Amino Acid Sequence↗

PubMed2003

Secondary structure prediction with support vector machines.

MOTIVATION: A new method that uses support vector machines (SVMs) to predict protein secondary structure is described and evaluated. The study is designed to develop a reliable prediction method using an alternative technique and to investigate the applicability of SVMs to this type of bioinformatics problem. METHODS: Binary SVMs are trained to discriminate between two structural classes. The binary classifiers are combined in several ways to predict multi-class secondary structure. RESULTS: The average three-state prediction accuracy per protein (Q(3)) is estimated by cross-validation to be 77.07 +/- 0.26% with a segment overlap (Sov) score of 73.32 +/- 0.39%. The SVM performs similarly to the 'state-of-the-art' PSIPRED prediction method on a non-homologous test set of 121 proteins despite being trained on substantially fewer examples. A simple consensus of the SVM, PSIPRED and PROFsec achieves significantly higher prediction accuracy than the individual methods.

Algorithms↗

PubMed2003

Effect of composition changes on the structural relaxation of a binary mixture.

Within the mode-coupling theory for idealized glass transitions, we study the evolution of structural relaxation in binary mixtures of hard spheres with size ratios delta of the two components varying between 0.5 and 1.0. We find two scenarios for the glassy dynamics. For small size disparity, the mixing yields a slight extension of the glass regime. For larger size disparity, a plasticization effect is obtained, leading to stabilization of the liquid due to mixing. For all delta, a decrease of the elastic moduli at the transition due to mixing is predicted. A stiffening of the glass structure is found as is reflected by the increase of the Debye-Waller factors at the transition points. The critical amplitudes for density fluctuations at small and intermediate wave vectors decrease upon mixing, and thus the universal formulas for the relaxation near the plateau values describe a slowing down of the dynamics upon mixing for the first step of the two-step relaxation scenario. The results explain the qualitative features of mixing effects reported by Williams and van Megen [Phys. Rev. E 64, 041502 (2001)] for dynamical light-scattering measurements on binary mixtures of hard-sphere-like colloids with size ratio delta=0.6.

Journal Article↗

PubMed1993

The ferredoxin:sulphite reductase gene from Synechococcus PCC7942.

The structural gene of the ferredoxin:sulphite reductase (EC 1.8.7.1) from the cyanobacterium Synechococcus PCC7942 (formerly 'Anacystis nidulans') was cloned and sequenced. The gene termed 'sir' was detected by heterologous Southern hybridisation with the structural gene cysI from Escherichia coli encoding the iron-sulphur haemoprotein of the NADPH:sulphite reductase. The open reading frame is comprised of 1875 bp encoding for a polypeptide of M(r) 70.028. The deduced amino acid sequence is 35.6% identical with the enterobacterial iron-sulphur haemoprotein. This putative fd-dependent sulphite reductase is only distantly related to the fd-dependent nitrite reductase (binary matching coefficient SAB: 0.23) or with the NADPH-sulphite reductase (SAB: 0.32). Highly conserved residues are found within the two Cys clusters forming the reactive Fe4S4-sirohaem centre of the enzyme. Expression of the sir gene using a fusion vector gave a single gene product which is immunologically related with the fd-sulphite reductase from the wild-type bacterium.

Amino Acid Sequence↗

PubMed2002

Delivery of exogenous protein antigens to major histocompatibility complex class I pathway in cytosol.

A fragment of anthrax lethal factor possesses the interesting function of delivering recombinant protein antigens through the classical major histocompatibility complex (MHC) class I pathway. This region of the lethal factor lacks the domain associated with anthrax cytotoxicity and functions independently of its binary partner, protective antigen. Experiments that used inhibitors at different steps of the MHC class I pathway supported this hypothesis. Application of this discovery to current T cell assays allows for the measurement of cytotoxic T lymphocyte function without resorting to live vectors and provides a useful new tool to design and test T cell-dependent vaccines.

Antigen Presentation↗

PubMed2004

Systematic benchmarking of microarray data classification: assessing the role of non-linearity and dimensionality reduction.

MOTIVATION: Microarrays are capable of determining the expression levels of thousands of genes simultaneously. In combination with classification methods, this technology can be useful to support clinical management decisions for individual patients, e.g. in oncology. The aim of this paper is to systematically benchmark the role of non-linear versus linear techniques and dimensionality reduction methods. RESULTS: A systematic benchmarking study is performed by comparing linear versions of standard classification and dimensionality reduction techniques with their non-linear versions based on non-linear kernel functions with a radial basis function (RBF) kernel. A total of 9 binary cancer classification problems, derived from 7 publicly available microarray datasets, and 20 randomizations of each problem are examined. CONCLUSIONS: Three main conclusions can be formulated based on the performances on independent test sets. (1) When performing classification with least squares support vector machines (LS-SVMs) (without dimensionality reduction), RBF kernels can be used without risking too much overfitting. The results obtained with well-tuned RBF kernels are never worse and sometimes even statistically significantly better compared to results obtained with a linear kernel in terms of test set receiver operating characteristic and test set accuracy performances. (2) Even for classification with linear classifiers like LS-SVM with linear kernel, using regularization is very important. (3) When performing kernel principal component analysis (kernel PCA) before classification, using an RBF kernel for kernel PCA tends to result in overfitting, especially when using supervised feature selection. It has been observed that an optimal selection of a large number of features is often an indication for overfitting. Kernel PCA with linear kernel gives better results.

Algorithms↗

PubMed1995

Segmentation of mammograms using multiple linked self-organizing neural networks.

A possible first stage in the analysis of the mammographic scene is its segmentation into four major components: background (the nonbreast area), pectoral muscle, fibroglandular region (parenchyma), and adipose region. An algorithm has been developed for this task. It is based on the classification of a feature vector constructed from statistical measures of texture calculated at two window sizes. Separate self-organizing neural networks are trained on sample data taken from each of the four regions. The feature vectors from the entire mammogram are then classified with the trained networks linked via a decision logic. To overcome the variability of texture between mammograms the algorithm uses data from a mammogram to classify itself in a staged approach consisting of several binary decisions. The training regions for each successive stage are determined from geometric information produced by the previous stages. The dataset in the study consisted of thirty (fifteen pairs) digitized normal mammograms of variable radiographic appearance. As a measure of performance, the outlines of the parenchyma were compared to those drawn by a radiologist experienced in reading mammograms. Comparison of the areas and perimeters generated by the human and computer observers gives a relationship with correlation coefficients of 0.74 and 0.59 for each measure, respectively. The overlapping areas of the parenchymas segmented by the observers normalized by the combined area was also calculated for each case. The mean and standard deviation of this measure was 0.69 +/- 0.12.

Adipose Tissue↗

PubMed1995

On the equivalence of two-layered perceptrons with binary neurons.

We consider two-layered perceptions consisting of N binary input units, K binary hidden units and one binary output unit, in the limit N >> K > or = 1. We prove that the weights of a regular irreducible network are uniquely determined by its input-output map up to some obvious global symmetries. A network is regular if its K weight vectors from the input layer to the K hidden units are linearly independent. A (single layered) perceptron is said to be irreducible if its output depends on every one of its input units; and a two-layered perceptron is irreducible if the K + 1 perceptrons that constitute such network are irreducible. By global symmetries we mean, for instance, permuting the labels of the hidden units. Hence, two irreducible regular two-layered perceptrons that implement the same Boolean function must have the same number of hidden units, and must be composed of equivalent perceptrons.

Computer Simulation↗

PubMed2003

Content-based searching of multimedia databases by use of approximate digital signatures.

We propose the use of approximate digital signatures of selected multimedia feature vectors for fast content-based retrieval in large multimedia databases. We adapt and extend the approximate message authentication code (AMAC), introduced by some of the authors recently in the area of message authentication, to the multimedia searching problem. An AMAC is a binary signature with the ability to reflect changes in the message it represents. The Hamming distance between two AMACs is used to measure the degree of the similarity between multimedia objects. We develop a method to compress AMAC signatures to create a direct look-up table that allows for fast searching of a database. The color histogram is used as the example feature space to show how the signature is applied. Experimental results show that the performance of the proposed method is comparable with existing methods based on other popular metrics, but it significantly decreases search time.

Journal Article↗

PubMed2005

Combined association and linkage analysis for general pedigrees and genetic models.

A combined score test for association and linkage analysis is introduced, based on a biologically plausible model with association between markers and causal genes and penetrance between phenotypes and the causal gene. The test is based on a retrospective likelihood of marker data given phenotypes, treating the alleles of the causal gene as hidden data. It is defined for arbitrary outbred pedigrees, a wide class of genetic models including polygenic and shared environmental effects and allows for missing marker data. It is multipoint, taking marker genotypes from several loci into account simultaneously. The score vector has one association and one linkage component, which can be used to define separate tests for association and linkage. For complete marker data, we give closed form expressions for the efficiency of the linkage, association and combined tests. These are examplified for binary and quantitative phenotypes with or without polygenic effects. The conclusion is that association tests are comparatively more efficient than linkage tests for strong association, weak penetrance models, small families and non-extreme phenotypes, whereas the linkage test is more efficient for weak association, strong penetrance models, large families and extreme phenotypes. The combined test is a robust alternative, which never performs much worse than the best of the linkage and association tests, and sometimes significantly better than both of them. It should be particularly useful when little is known about the genetic model.

Journal Article↗

PubMed1977

Polydisperse particulate solids mixing and segregation: nonstationary Markov chains.

The feasibility of analyzing interparticulate translocations within agitated beds of polydisperse particulate solids in terms of Markov chains was investigated. A binary mixture of spherical particles subjected to vertical sine wave vibration is shown to behave in accordance with a nonstationary. Markov chain having singly stochastic transition matrixes. The transition probability elements of such matrixes, calculated from a knowledge of the initial and final occupancy vectors, agree with those estimated using tracer particles. With appropriate restrictions, the former method, based on the solution of simultaneous equations, permits a quantitative evaluation of particle mobilities throughout the bed.

Chemistry, Pharmaceutical↗

PubMed2000

Learning strategies for the maximally stable diluted binary perceptron

I show analytically that an optimally chosen continuous precursor J in the hypercube is highly correlated to the maximally stable diluted binary perceptron which solves the same storage problem. J allows the construction of a diluted binary perceptron D by a simple rule. Performing simulations for perceptrons of size N=100 I demonstrate that D is highly stable and can be improved in an efficient manner by partial enumeration thereby incorporating information from the precursor components. The precursor highlights the vector components on which partial enumeration improves the stability of the vector most efficiently. Moreover, it discriminates for each vector component i at least one of the three possible values D(i)=-1,0,1 as being extremely unlikely.

Journal Article↗

PubMed2001

On the analysis of spatial binary images.

This paper deals with the analysis of spatial images taken from microscopically heterogeneous but macroscopically homogeneous microstructures. A new method is presented, which is strictly based on integral-geometric formulae such as Crofton's intersection formulae and Hadwiger's recursive definition of the Euler number. By means of this approach the quermassdensities can be expressed as the inner products of two vectors where the first vector carries the 'integrated local knowledge' about the microstructure and the second vector depends on the lateral resolution of the image as well as the quadrature rules used in the discretization of the integral-geometric formulae. As an example of application we consider the analysis of spatial microtomographic images obtained from natural sandstones.

Journal Article↗

PubMed2004

Multidimensional support vector machines for visualization of gene expression data.

MOTIVATION: Since DNA microarray experiments provide us with huge amount of gene expression data, they should be analyzed with statistical methods to extract the meanings of experimental results. Some dimensionality reduction methods such as Principal Component Analysis (PCA) are used to roughly visualize the distribution of high dimensional gene expression data. However, in the case of binary classification of gene expression data, PCA does not utilize class information when choosing axes. Thus clearly separable data in the original space may not be so in the reduced space used in PCA. RESULTS: For visualization and class prediction of gene expression data, we have developed a new SVM-based method called multidimensional SVMs, that generate multiple orthogonal axes. This method projects high dimensional data into lower dimensional space to exhibit properties of the data clearly and to visualize a distribution of the data roughly. Furthermore, the multiple axes can be used for class prediction. The basic properties of conventional SVMs are retained in our method: solutions of mathematical programming are sparse, and nonlinear classification is implemented implicitly through the use of kernel functions. The application of our method to the experimentally obtained gene expression datasets for patients' samples indicates that our algorithm is efficient and useful for visualization and class prediction. CONTACT: komura@hal.rcast.u-tokyo.ac.jp.

Algorithms↗

PubMed2003

Support vector machines for predicting rRNA-, RNA-, and DNA-binding proteins from amino acid sequence.

Classification of gene function remains one of the most important and demanding tasks in the post-genome era. Most of the current predictive computer methods rely on comparing features that are essentially linear to the protein sequence. However, features of a protein nonlinear to the sequence may also be predictive to its function. Machine learning methods, for instance the Support Vector Machines (SVMs), are particularly suitable for exploiting such features. In this work we introduce SVM and the pseudo-amino acid composition, a collection of nonlinear features extractable from protein sequence, to the field of protein function prediction. We have developed prototype SVMs for binary classification of rRNA-, RNA-, and DNA-binding proteins. Using a protein's amino acid composition and limited range correlation of hydrophobicity and solvent accessible surface area as input, each of the SVMs predicts whether the protein belongs to one of the three classes. In self-consistency and cross-validation tests, which measures the success of learning and prediction, respectively, the rRNA-binding SVM has consistently achieved >95% accuracy. The RNA- and DNA-binding SVMs demonstrate more diverse accuracy, ranging from approximately 76% to approximately 97%. Analysis of the test results suggests the directions of improving the SVMs.

Computational Biology↗

PubMed1995

Suppression of single and double nonsense mutations introduced into the diphtheria toxin A-chain gene: a potential binary system for toxin gene therapy.

We have previously shown that ablation of specific cells can be achieved through the transcriptionally regulated expression of the diphtheria toxin A-chain (DT-A) gene in both cell culture and transgenic mice. Such targeted toxin gene expression provides a novel approach to cancer and acquired immunodeficiency syndrome (AIDS) therapy. The use of mutants of DT-A with attenuated toxicity may allow targeting of cells for which only moderately selective gene regulatory elements are available. Alternatively, conditional mutants might be used to target cells in which conditions can be established for suppression of the mutation. We have investigated the effects of mutating selected serine codons to amber (TAG) nonsense codons in the DT-A coding sequence. In transient transfection of HeLa cells, DT-A activity was markedly reduced by the introduction of a single amber codon and was virtually eliminated by two amber mutations. Cotransfection of a serine inserting suppressor tRNA expression plasmid substantially restored DT-A expression from both single and double amber mutants. Expression of the same suppressor tRNA also suppressed a previously described amber mutation at the tyrosine codon 28 in DT-A. Thus, nonsense suppression can be used to control the expression of DT-A in mammalian cells, potentially allowing binary control over the targeting of tissues for selective ablation.

Amino Acid Sequence↗