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Microarray technology is used as a source of data for a wide range of biology studies. Useful biological information can be extracted from the analysis of microarray data, namely, the impact of a particular gene expression on the expression of other genes or the determination of expressed genes under different conditions. The purpose of this paper is to find co-behavioral genes in different data sets for different times and conditions. In other words, genes with similar behavior, same increase or decrease, under different medical, stress, and time conditions in terms of expression are determined. Multi-valued discretization of expression data was used for extracting genes with identical behavior. The algorithm proposed in this study is based on data and methods ensemble. The data ensemble technique was used to extract candidate genes with identical behavior. Other methods were also applied on all the data sets; as a result, many co-behavioral candidate genes with different similarity and correlation values were identified. Finally, the ultimate output was created from the ensemble of different methods. By applying the algorithm on yeast gene expression data, meaningful relations among genes were extracted. [ABSTRACT FROM AUTHOR]

Orthogonal Matching Pursuit (OMP) is an effective solution to sparse approximation based on redundant dictionary, but there are plenty of matrix-vector multiplications in order to seek for the most matching atom. This full search scheme can consume much CPU time, especially for high dimension data such as image. Although some existing schemes proposed some accelerating methods by exploiting the property of atoms in parametric dictionary, they cannot be extended to other dictionaries, especially learned dictionary. Considering statistical nature of atoms in learned dictionary, this paper proposes a novel method which utilizes modified K-mean algorithm to cluster all the atoms in the learned dictionary while sufficiently considering the inner product operation between every two atoms, determines the class in which the desired atom locates and finally find it in the class. Some analyses and experiments have shown the success of the method. In addition, this paper gives some empirical analysis of the effect of related parameters of indexed dictionary. [ABSTRACT FROM AUTHOR]

Metabolic networks are useful representations of the metabolic capabilities of cells. A comparison of metabolic networks across species is essential to better understand how evolutionary pressures shape these networks. By comparing the set of reactions that are expected to occur in an organism with the set of reactions in reference metabolic pathways, it is possible to infer the main metabolic functions of an organism. In this paper, the metabolic networks of the mesophilic archaeon Methanosarcina acetivorans and the thermophilic archaeon Methanopyrus kandleri have been reconstructed based on the KEGG LIGAND database, followed by four topological statistical analyses of the nodes in the two networks to compare their metabolic networks. The values of average degree and characteristic path length are very small but clustering coefficient is relatively large. The results show that the complete metabolic networks of M. acetivorans and M. kandleri possessed "small-world" network properties. Then we used Girvan-Newman modular algorithm to identify hub modules and compared hub modules with non-hub modules, respectively. The results show that M. kandleri metabolic network has a bet-ter modular organization than the M. acetivorans network. M. acetivorans includes 39 modules, 25 modules of them are independent, and 15 modules are functionally pure. On the other hand, M. kandleri includes 30 modules. Among them, there are 20 indepen-dent modules, and 14 of them are functionally pure. These results further indicated that the present approach for identifying modules yields modules that have biologically sig-nificant functions. We also identified hub modules of the metabolic networks and found that these hub modules are carbohydrate metabolism and amino acid metabolism. The conclusions obtained from such studies provide a broad overview of the similarities and differences between organism's metabolic networks. These will be very helpful for further research on thermostability of methanogens. [ABSTRACT FROM AUTHOR]

Due to the high computational cost associated with calculation of optimal control policies for a family of Markov chains, sub-optimal policy generation approaches are often required for development of intervention strategies in systems medicine. In this paper, a new infinite-horizon suboptimal policy generation algorithm using a sequential-selection approach with efficient re-calculation of steady-state distributions is presented and compared against brute-force and other sub-optimal algorithms. Our results show that for a system represented by a family of Markov chains, the presented approach is able to generate robust stationary control policies with superior expected behavior in a computationally inexpensive manner. [ABSTRACT FROM AUTHOR]

The goal of this paper is to solve mathematical model equations on solid tumour growth and compute their parameter values by applying growth rates of prostate cancer cell lines in vivo. For these computations, we investigate previously developed C3(1)/Tag transgenic models of prostate cancer. To make the computations fast, we have constructed an algorithm, which is based on small amounts of spatial grid-points and obtained a correspondence between the in vivo growth of tumours and the solutions of the model equations. [ABSTRACT FROM AUTHOR]

Sub-pixel accuracy stripe boundary location is essential for stripe coded structured light based 3D reconstruction. So far, the intersecting point method might be the most accurate for stripe boundary location. But in this method two groups of patterns have to be projected, where one group is normal and the other is inverse patterns. So the intersecting point method can not be used in several time- or resource-constrained applications because of its complexity of projection. In this paper, after a brief survey on several commonly used stripe boundary detection methods, an improved sub-pixel location scheme based on weighted centroid algorithm is proposed. In the proposed scheme, a green slit is first added between every stripe to avoid false detection and false rejection. Then, universal gravity-based edge detection and weighted centroid method are utilized for sub-pixel location. Compared with the intersecting point method, the proposed location scheme can achieve comparable detection accuracy with only half number of patterns projected. [ABSTRACT FROM AUTHOR]

With a different speed function, Level Set Method has been widely applied to many applications. Generally speaking, speed function may depend on many factors, such as curvature, normal direction. In this paper, we discuss a novel speed function which is only determined by neighbors' support. With enough support, zero level set can move or stop. Otherwise, it must wait for a moment before a decision to move or stop is made. In addition, an algorithm based on normal diffusion is proposed to smooth the zero level set, which can preserve the sharp feature and round corner at the same time. Experimentally, the proposed method has been successfully used for interested objection segmentation and mesh segmentation. [ABSTRACT FROM AUTHOR]

The syndrome is the basic pathological unit and the key concept in traditional Chinese medicine (TCM), and the herbal remedy is prescribed according to the syndrome a patient catches. Nevertheless, few studies are dedicated to investigate the number of syndromes in chronic renal failure (CRF) patients and what these syndromes are. In this paper, we carry out a clinical epidemiology survey and obtain 601 CRF cases, including 72 symptoms in each report. Based on association delineated by mutual information, we propose a novel pattern discovery algorithm to discover syndromes, which probably have overlapped symptoms in TCM. A revised version of mutual information is presented here to discriminate positive and negative association. The algorithm self-organizedly discovers 16 effective patterns, each of which is verified manually by TCM physicians to recognize the syndrome it belongs to. The super-additivity of cluster by mutual information is proved and n-class association concept is introduced in our model to reduce computational complexity. Validation of the algorithm is performed by using the syndrome data and consolidated clinically to have 16 patterns. The results indicate that the algorithm achieves a high sensitivity with 96.48% and each classified pattern is of clinical significance. Therefore, we conclude that the algorithm provides an excellent solution to chronic renal failure problem in the context of traditional Chinese medicine. [ABSTRACT FROM AUTHOR]

This paper deals with a new middle path approach developed for reducing alignment calculations in BLAST algorithm. This is a new step which is introduced in BLAST algorithm in between the ungapped and gapped alignments. This step of middle path approach between the ungapped and gapped alignments reduces the number of sequences going for gapped alignment. This results in the improvement in speed for alignment up to 30%. [ABSTRACT FROM AUTHOR]