The quick diagnosis of Burkitt lymphoma (BL) and its clear-cut differentiation from diffuse large B-cell lymphoma (DLBCL) is of great clinical importance because treatment strategies for these two disease entities dif...
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The quick diagnosis of Burkitt lymphoma (BL) and its clear-cut differentiation from diffuse large B-cell lymphoma (DLBCL) is of great clinical importance because treatment strategies for these two disease entities differ markedly. As these two lymphomas are difficult to distinguish using the current World Health Organization classification, we studied 39 cases of highly proliferative peripheral blastic B-cell lymphoma (HPBCL) to establish a practical differential-diagnostic algorithm. Characteristics set for BL were a typical morphology, a mature B-cell phenotype of CD10(+), Bcl-6(+) and Bcl-2(-) tumour cells, a proliferation rate of > 95%, and the presence of C-MYC rearrangements in the absence of t(14;18)(q32;q21). Altogether, these characteristics were found in only five of 39 cases, whereas the majority of tumours revealed mosaic features. We then followed a pragmatic stepwise approach for a classification algorithm that included the assessment of C-MYC status to stratify HPBCL into four predefined diagnostic categories (DC), namely DC I (5/39, 12.8%): 'classical BL', DC II (11/39, 28.2%): 'atypical BL', DC III (9/39, 23.1%): 'C-MYC+ DLBCL' and DC IV (14/39, 35.9%): 'C-MYC- HPBCL'. This proposal may serve as a robust and objective operational basis for therapeutic decisions for HPBCL within 1 week and is applicable to be evaluated for its prognostic relevance in clinical trials with uniformly treated patients.
Recently, new machine learning classifiers for the prediction of linear B-cell epitopes were presented. Here we show the application of Receiver Operator Characteristics (ROC) convex hulls to select optimal classifier...
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Recently, new machine learning classifiers for the prediction of linear B-cell epitopes were presented. Here we show the application of Receiver Operator Characteristics (ROC) convex hulls to select optimal classifiers as well as possibilities to improve the post test probability (PTP) to meet real world requirements such as high throughput epitope screening of whole proteomes. The major finding is that ROC convex hulls present an easy to use way to rank classifiers based on their prediction conservativity as well as to select candidates for ensemble classifiers when validating against the antigenicity profile of 10 HIV-1 proteins. We also show that linear models are at least equally efficient to model the available data when compared to multi-layer feed-forward neural networks. Copyright (c) 2006 John Wiley & Sons, Ltd.
Identification and characterization of antigenic determinants on proteins has received considerable attention utilizing both, experimental as well as computational methods. For computational routines mostly structural...
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Identification and characterization of antigenic determinants on proteins has received considerable attention utilizing both, experimental as well as computational methods. For computational routines mostly structural as well as physicochemical parameters have been utilized for predicting the antigenic propensity of protein sites. However, the performance of computational routines has been low when compared to experimental alternatives. Here we describe the construction of machine learning based classifiers to enhance the prediction quality for identifying linear B-cell epitopes on proteins. Our approach combines several parameters previously associated with antigenicity, and includes novel parameters based on frequencies of amino acids and amino acid neighborhood propensities. We utilized machine learning algorithms for deriving antigenicity classification functions assigning antigenic propensities to each amino acid of a given protein sequence. We compared the prediction quality of the novel classifiers with respect to established routines for epitope scoring, and tested prediction accuracy on experimental data available for HIV proteins. The major finding is that machine learning classifiers clearly outperform the reference classification systems on the HIV epitope validation set. Copyright (c) 2006 John Wiley & Sons, Ltd.
This paper presents an error analysis for classification algorithms generated by regularization schemes with polynomial kernels. Explicit convergence rates are provided for support vector machine (SVM) soft margin cla...
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This paper presents an error analysis for classification algorithms generated by regularization schemes with polynomial kernels. Explicit convergence rates are provided for support vector machine (SVM) soft margin classifiers. The misclassification error can be estimated by the sum of sample error and regularization error. The main difficulty for studying algorithms with polynomial kernels is the regularization error which involves deeply the degrees of the kernel polynomials. Here we overcome this difficulty by bounding the reproducing kernel Hilbert space norm of Durrmeyer operators, and estimating the rate of approximation by Durrmeyer operators in a weighted L-1 space (the weight is a probability distribution). Our study shows that the regularization parameter should decrease exponentially fast with the sample size, which is a special feature of polynomial kernels.
As a key technology of digital broadcast and TV, blind equalization overcomes inter-symbol interference to improve the effect of receiving signals. A new QAM blind equalization algorithm based on fuzzy neural network ...
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ISBN:
(纸本)9781424408009
As a key technology of digital broadcast and TV, blind equalization overcomes inter-symbol interference to improve the effect of receiving signals. A new QAM blind equalization algorithm based on fuzzy neural network classifier was proposed. Simulation shows that the new algorithm improves convergence speed and reduces residual error and BER (Bit Error Ratio).
In this paper, a new blind equalization algorithm based on fuzzy neural network (FNN) is proposed. It makes use of blind estimation (BE) and FNN classifier to equalize. Firstly BE algorithm is used to identify the cha...
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ISBN:
(纸本)9812700420
In this paper, a new blind equalization algorithm based on fuzzy neural network (FNN) is proposed. It makes use of blind estimation (BE) and FNN classifier to equalize. Firstly BE algorithm is used to identify the channel character, the signals are rebuilt by deconvolution, and then the signals are classified by FNN classifier. This algorithm has the merits than the foregoing neural network algorithm, such as faster convergence speed, smaller residual error, lower bit error rate (BER), etc. The validity is proved by simulations.
As a key technology in the digital communication system, blind equalization algorithm based on fuzzy neural network classifier is proposed. The algorithm overcomes bad judgment error. Channel estimation algorithm and ...
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ISBN:
(纸本)1424405289
As a key technology in the digital communication system, blind equalization algorithm based on fuzzy neural network classifier is proposed. The algorithm overcomes bad judgment error. Channel estimation algorithm and fuzzy neural network classifier were combined to carry out equalization. The primary signal was attained by de-convolution. Judgment range of fuzzy neural network was adjusted dynamically by competition study algorithm, and then blind equalization judgment was realized. The algorithm reduces judgment error and bit-error ratio. The validity is approved by simulation.
The localization and interpretation of traffic signs by means of a wise-artificial system is one of the several applications of the recognizing-image techniques. In this work we present techniques addressed to the cor...
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ISBN:
(纸本)0889865566
The localization and interpretation of traffic signs by means of a wise-artificial system is one of the several applications of the recognizing-image techniques. In this work we present techniques addressed to the correct and efficient localization of traffic signs from real images, obtaining information about their location, colour, shape, size and orientation. Moreover, it is shown that colour models non-dependent of illumination combined with non-dimensional features are able to discriminate shapes and provide good results in the traffic signs detection. Several experimental results concerning the application of previously mentioned techniques are shown later on (1).
We develop an algorithm framework for isomorph-free exhaustive generation of designs admitting a group of automorphisms from a prescribed collection of pairwise nonconjugate groups, where each prescribed group has a l...
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We develop an algorithm framework for isomorph-free exhaustive generation of designs admitting a group of automorphisms from a prescribed collection of pairwise nonconjugate groups, where each prescribed group has a large index relative to its normalizer in the isomorphism-inducing group. We demonstrate the practicality of the framework by producing a complete classification of the Steiner triple systems of order 21 admitting a nontrivial automorphism group. The number of such pairwise nonisomorphic designs is 62336617, where 958 of the designs are anti-Pasch. We also develop consistency checking methodology for gaining confidence in the correct operation of the algorithm implementation.
This paper proposes the notion of a greylevel difference classification algorithm in fractal image compression. Then an example of the greylevel difference classification algo rithm is given as an improvement of the q...
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This paper proposes the notion of a greylevel difference classification algorithm in fractal image compression. Then an example of the greylevel difference classification algo rithm is given as an improvement of the quadrant greylevel and variance classification in the quadtree-based encoding algorithm. The algorithm incorporates the frequency feature in spatial analysis using the notion of average quadrant greylevel difference, leading to an enhancement in terms of encoding time, PSNR value and compression ratio.
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