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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]

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]

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 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]

This study is aimed to develop a parameter estimation procedure for a stochastic model of gastric emptying of pellets under fasting conditions of healthy subjects that represent the irregular decrements of gastric contents after administration of pellets. An algorithm for the identification of experimental subjects into subgroups based on their gastric emptying rates has been proposed. The parameter estimation procedure was performed with observational data of gastric emptying profiles of 19 subjects from the existing literature. The identification algorithm was validated through comparison against the original results, showing that seven subjects indicated slow gastric emptying and 12 subjects indicated fast emptying. After excluding one subject with gastric stasis, the optimal results obtained from this study classified subjects into three subgroups: two subjects with slow, five subjects with intermediate and 11 subjects with fast gastric emptying. The proposed parameter estimation procedure of the stochastic model is relatively easy to implement and can be used to analyze real patients' data. The algorithm for identification of the gastric emptying dynamics in experimental subjects could provide useful information for further investigations of long-term energy balance forecasting. [ABSTRACT FROM AUTHOR]

In order to ameliorate the lung defects caused by missed juxtapleural nodules in lung segmentation on chest computed tomography (CT) images, we develop a Newton-Cotes-based smoothing algorithm (NCBS) which is used as a preliminary step to remove noises as many as possible. Next considering the crescent outline features of the lung, we propose a curvature-based correction algorithm (CBC) for the determination of the correction threshold. The application of the proposed algorithms is demonstrated in the process of lung segmentation and the experimental results on 25 real datasets are illustrated. Furthermore, some experiments are conducted to investigate the effects of the key parameters in CBC on the performances of lung segmentation so as to decide their optimal values. In addition, the CBC is compared with other methods analytically and experimentally. The overall results show that our proposed algorithm in lung segmentation excels the related methods on the capability of automatic selection of the correction threshold, as well as the performances of accuracy, efficiency and feasibility. [ABSTRACT FROM AUTHOR]

The Neutral Biodiversity Theory (NBT) argues that community-level patterns are not determined by trait differences among species, but rather by demographic stochasticity. If true, deterministic patterns of species co-existence, transient or otherwise, should not occur. We tested this critical prediction using a spatially explicit, reiterative algorithm (SERA) that incorporates demographic stochasticity to examine how artificial plant species compete for light and space. An additional level of ecological realism was added by simulating species competition in heterogeneous and disturbed environments. Across 108 simulations, small niche differences influenced interspecific competition and persistence, particularly in spatiotemporally heterogeneous environments. One species re-emerged as the dominant, and the identities of subdominant and rare species remained constant. Although stochastic processes played an important role, the results of these simulations are inconsistent with NBT, i.e., very small niche differences dictated patterns even when all species obey the same simple physical rules. [ABSTRACT FROM AUTHOR]

The classification of breast cancer patients is of great importance in cancer diagnosis. Most classical cancer classification methods are clinical-based and have limited diagnostic ability. The recent advances in machine learning technique has made a great impact in cancer diagnosis. In this research, we develop a new algorithm: Kernel-Based Naive Bayes (KBNB) to classify breast cancer tumor based on memography data. The performance of the proposed algorithm is compared with that of classical navie bayes algorithm and kernel-based decision tree algorithm C4.5. The proposed algorithm is found to outperform in the both cases. We recommend the proposed algorithm could be used as a tool to classify the breast patient for early cancer diagnosis. [ABSTRACT FROM AUTHOR]