Proteomics is the study of proteins and their interactions in a cell. With the successful completion of the Human Cenome Project, it comes the postgenome era when the proteomics technology is emerging. This paper stud...
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Proteomics is the study of proteins and their interactions in a cell. With the successful completion of the Human Cenome Project, it comes the postgenome era when the proteomics technology is emerging. This paper studies protein molecule from the algebraic point of view. The algebraic system (∑, +, *) is introduced, where ∑ is the set of 64 codons. According to the characteristics of (∑, +, *), a novel quasi-amino acids code classification method is introduced and the corresponding algebraic operation table over the set ZU of the 16 kinds of quasi-amino acids is established. The internal relation is revealed about quasi-amino acids. The results show that there exist some very close correlations between the properties of the quasi-amino acids and the codon. All these correlation relationships may play an important part in establishing the logic relationship between codons and the quasi-amino acids during the course of life origination. According to Ma F et al (2003 J. Anhui Agricultural University 30 439), the corresponding relation and the excellent properties about amino acids code are very difficult to observe. The present paper shows that (ZU, +,×) is a field. Furthermore, the operational results display that the eodon tga has different property from other stop codons. In fact, in the mitochondrion from human and ox genomic codon, tga is just tryptophane, is not the stop codon like in other genetic code, it is the case of the Chen W C et al (2002 Acta Biophysiea Siniea 18(1) 87). The present theory avoids some inexplicable events of the 20 kinds of amino acids code, in other words it solves the problem of 'the 64 codon assignments of mRNA to amino acids is probably completely wrong' proposed by Yang (2006 Progress in Modern Biomedicine 6 3).
In this paper, we proposed a tunnel morph model for bio-signal waveform in measuring their similarity. Firstly, the formal specifications of bio-signal waveforms are given. And then, a series of model establishing rel...
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In this paper, we proposed a tunnel morph model for bio-signal waveform in measuring their similarity. Firstly, the formal specifications of bio-signal waveforms are given. And then, a series of model establishing related definitions are presented. These definitions contain waveform segmentation; waveforms distance measurement, and tunnel width computation. Moreover, on the base of the model, a similarity measuring strategy which takes the curve feature of bio-signal into account was presented. In the end, the strategy was compared with other similarity measurement methods by AECG (Ambulatory Electrocardiogram) waveform data. The data are adopted from MIT/BIH arrhythmia database. Experiment results show that the sensitivity and the positive predictivity of the strategy based on tunnel morph model are prior to other strategies.
In this article, a novel approach is proposed to predict RNA secondary structure called RNA secondary structure prediction based on Tabu Search (RNATS). In the RNATS algorithm, two search models, intensification searc...
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In this article, a novel approach is proposed to predict RNA secondary structure called RNA secondary structure prediction based on Tabu Search (RNATS). In the RNATS algorithm, two search models, intensification search and diversification search, are developed to exploit the local regions around the current solution and explore the unvisited solution space, respectively. Simulation experiments are conducted on eight RNA sequences to show that the proposed method is feasible and effective.
An important aim in pattern recognition is to cluster the given shapes. This paper presents a shape recognition and retrieval algorithm. The algorithm first extracts the skeletal features using the medial axis transfo...
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An important aim in pattern recognition is to cluster the given shapes. This paper presents a shape recognition and retrieval algorithm. The algorithm first extracts the skeletal features using the medial axis transform. Then, the features are transformed into a string of symbols with the similarity among those symbols computed based on the edit distance. Finally, the shapes are identified using dynamic programming. Two public datasets are analyzed to demonstrate that the present approach is better than previous approaches.
Risk evaluation is very important to the design and improvement of physical protection systems. In this paper, an evaluation method of multi-source information fusion is proposed based on the D-S evidence theory. In t...
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In order to decrease dispersion penalty and increase the optical bandwidth efficiency,an optical single-side-band modulation(SSBM) scheme in sub-carrier multiplexing(SCM) is *** principle of the SSBM is analytically p...
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In order to decrease dispersion penalty and increase the optical bandwidth efficiency,an optical single-side-band modulation(SSBM) scheme in sub-carrier multiplexing(SCM) is *** principle of the SSBM is analytically presented,and a configuration for generating optical SSB signal is proposed using a balanced Mach-Zehnder electro-optic modulator.
In many areas of pattern recognition and machine learning, subspace selection is an essential step. Fisher's linear discriminant analysis (LDA) is one of the most well-known linear subspace selection methods. Howe...
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In many areas of pattern recognition and machine learning, subspace selection is an essential step. Fisher's linear discriminant analysis (LDA) is one of the most well-known linear subspace selection methods. However, LDA suffers from the class separation problem. The projection to a subspace tends to merge close class pairs. A recent result, named maximizing the geometric mean of Kullback-Leibler (KL) divergences of class pairs (MGMD), can significantly reduce the class separation problem. Furthermore, maximizing the harmonic mean of Kullback-Leibler (KL) divergences of class pairs (MHMD) emphasizes smaller divergences more than MGMD, and deals with the class separation problem more effectively. However, in many applications, labeled data are very limited while unlabeled data can be easily obtained. The estimation of divergences of class pairs is unstable using inadequate labeled data. To take advantage of unlabeled data for subspace selection, semi-supervised MHMD (SSMHMD) is proposed using graph Laplacian as normalization. Quasi-Newton method is adopted to solve the optimization problem. Experiments on synthetic data and real image data show the validity of SSMHMD.
The problem of enhancing speech degraded by uncorrelated additive noise, when only the noisy speech is available, has been widely studied in the past and it is still an active field of research. Wiener filter, which i...
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The problem of enhancing speech degraded by uncorrelated additive noise, when only the noisy speech is available, has been widely studied in the past and it is still an active field of research. Wiener filter, which is the most fundamental approach, has been delineated in different forms and adopted in diversified applications. An improved wiener filtering algorithm is proposed in this study, which utilizes band-partitioning spectral entropy to achieve accurate and robust speech endpoint detection and a dynamic noise power spectrum is estimated for updating a priori SNR. Experimental results reveal that the proposed algorithm can extract the embedded speech segments from utterances containing a variety of background noise successfully.
Spiking neural P systems (SN P systems, for short) are a class of distributed parallel computing devices inspired from the way neurons communicate by means of spikes. In this work, a discrete structure representation ...
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Detection of shot transitions servers as the preliminary step to video indexing and retrieval. Locally linear embedding (LLE) algorithm fails when it is applied to video with multi-shot. In this paper, we present a ...
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ISBN:
(纸本)9781424450015
Detection of shot transitions servers as the preliminary step to video indexing and retrieval. Locally linear embedding (LLE) algorithm fails when it is applied to video with multi-shot. In this paper, we present a novel framework of shot transitions detection. The method involves two processes: First we extract the manifold feature of shot transition using LLE through addition of virtual frames on an enriched set, and then they are classified by *** show that the recognition rate of shot transition is reached over 90%.
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