Regulation of gene expression is a carefully regulated phenomenon in the cell. "Reverse-engineering" algorithms try to reconstruct the regulatory interactions among genes from genome-scale measurements of ge...
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Regulation of gene expression is a carefully regulated phenomenon in the cell. "Reverse-engineering" algorithms try to reconstruct the regulatory interactions among genes from genome-scale measurements of gene expression profiles (microarrays). Mammalian cells express tens of thousands of genes;hence, hundreds of gene expression profiles are necessary in order to have acceptable statistical evidence of interactions between genes. As the number of profiles to be analyzed increases, so do computational costs and memory requirements. In this work, we designed and developed a parallel computing algorithm to reverse-engineer genome-scale gene regulatory networks from thousands of gene expression profiles. The algorithm is based on computing pairwise Mutual Information between each gene-pair. We successfully tested it to reverse engineer the Mus Musculus (mouse) gene regulatory network in liver from gene expression profiles collected from a public repository. A parallel hierarchical clustering algorithm was implemented to discover "communities" within the gene network. Network communities are enriched for genes involved in the same biological functions. The inferred network was used to identify two mitochondrial proteins.
This paper presents the clustering algorithmS' REFEREE PACKAGE or CARP, an open source GNU GPL-licensed C package for evaluating clustering algorithms. Calibrating performance of such algorithms is important and C...
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This paper presents the clustering algorithmS' REFEREE PACKAGE or CARP, an open source GNU GPL-licensed C package for evaluating clustering algorithms. Calibrating performance of such algorithms is important and CARP addresses this need by generating datasets of different clustering complexity and by assessing the performance of the concerned algorithm in terms of its ability to classify each dataset relative to the true grouping. This paper briefly describes the software and its capabilities.
Conservation planning requires knowledge of the distribution of all species in the area of interest. Surrogates for biodiversity are considered as a possible solution. The two major types are biological and environmen...
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Conservation planning requires knowledge of the distribution of all species in the area of interest. Surrogates for biodiversity are considered as a possible solution. The two major types are biological and environmental surrogates. Here, we evaluate four different methods of hierarchical clustering, as well as one non-hierarchical method, in the context of producing surrogates for biodiversity. Each clustering method was used to produce maps of both surrogate types. We evaluated the representativeness of each clustering method by finding the average number of species represented in a set of sites, one site of each domain, which was carried out with Monte-Carlo permutations procedure. We propose an additional measure of surrogate performance, which is the degree of evenness of the different domains, e.g., by calculating Simpson's diversity index. Surrogates with low evenness leave little flexibility in site selection since often some of the domains may be represented by a single or very few sites, and thus surrogate maps with a high Simpson's index value may be more relevant for actual decision making. We found that there is a trade-off between species representativeness and evenness. Centroid clustering represented the most species, but had very low values of evenness. Ward's method of minimum variance represented more species than a random choice, and had high evenness values. Using the typical evaluation measures, the Centroid clustering method was most efficient for surrogate production. However, when Simpson's index is also considered, Ward's method of minimum variance is more appropriate for managers. (C) 2010 Elsevier Ltd. All rights reserved.
This paper presents an algorithm to extract symbolic rules from trained artificial neural networks (ANNs), called ERANN. In many applications, it is desirable to extract knowledge from ANNs for the users to gain a bet...
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This paper presents an algorithm to extract symbolic rules from trained artificial neural networks (ANNs), called ERANN. In many applications, it is desirable to extract knowledge from ANNs for the users to gain a better understanding of how the networks solve the problems. Although ANN usually achieves high classification accuracy, the obtained results sometimes may be incomprehensible, because the knowledge embedded within them is distributed over the activation functions and the connection weights. This problem can be solved by extracting rules from trained ANNs. To do so, a rule extraction algorithm has been proposed in this paper to extract symbolic rules from trained ANNs. A standard three-layer feedforward ANN with four-phase training is the basis of the proposed algorithm. Extensive experimental studies on a set of benchmark classification problems, including breast cancer, iris, diabetes, wine, season, golfplaying, and lenses classification, demonstrates the applicability of the proposed method. Extracted rules are comparable with other methods in terms of number of rules, average number of conditions for a rule, and the rules accuracy. The proposed method achieved accuracy values 96.28, 98.67, 76.56, 91.01, 100, 100, and 100 for the above problems, respectively. It has been seen that these results are one of the best results comparing with results obtained from related previous studies.
The efficient subdivision of a sensor network into uniform clusters of physically close nodes is an important building block in the design of efficient upper layer network functions such as routing, broadcast, data ag...
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ISBN:
(纸本)9781424419814
The efficient subdivision of a sensor network into uniform clusters of physically close nodes is an important building block in the design of efficient upper layer network functions such as routing, broadcast, data aggregation, and query processing. This paper analyzes a low energy adaptive clustering hierarchy (LEACH) in terms of the optimum number of clusters and demonstrates through simulation that the optimum number calculated are not suitable for sensor networks including large number of nodes or covering large area. Based on the analysis results, we give a new formula of calculating the optimum number of clusters on an improved data gathering model. To decrease the energy dissipation further, we develop a new efficient uniform clustering algorithm in ad-hoc sensor networks. Simulation results show that it achieves fairly uniform cluster-head distribution across the network.
Since Ad hoc network Is vulnerable to attack, it's imperative to carry out intrusion detection research on ad hoc network. In this paper, according to the requirement of cluster based intrusion detection system, w...
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ISBN:
(纸本)9781424421077
Since Ad hoc network Is vulnerable to attack, it's imperative to carry out intrusion detection research on ad hoc network. In this paper, according to the requirement of cluster based intrusion detection system, we propose a new algorithm of clustering for IDS. Analyse and simulation results show that our method can decrease the number of cluster head and extend network survival time.
Wireless machine-to-machine sensor networks with multiple radio interfaces are expected to have several advantages, including high spatial scalability, low event detection latency, and low energy consumption. Here, we...
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Wireless machine-to-machine sensor networks with multiple radio interfaces are expected to have several advantages, including high spatial scalability, low event detection latency, and low energy consumption. Here, we propose a network model design method involving network approximation and an optimized multi-tiered clustering algorithm that maximizes node lifespan by minimizing energy consumption in a non-uniformly distributed network. Simulation results show that the cluster scales and network parameters determined with the proposed method facilitate a more efficient performance compared to existing methods.
A high resolution detector is being developed for our small animal position emission tomography (MicroPET). The detector unit consist of 8x8 crystal blocks, coupled to four photomultiplier tubes (PMTs). Each scintilla...
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ISBN:
(纸本)9780769531182
A high resolution detector is being developed for our small animal position emission tomography (MicroPET). The detector unit consist of 8x8 crystal blocks, coupled to four photomultiplier tubes (PMTs). Each scintillation event is mapped in a two dimensional (2-D) position through the relative ratio of the output signals of the PMTS. Crystal Look-up table (CLT) used in ThuMicroPET scanner defines the matching relation between signal position of a detected event to a corresponding detector pixel location. It has a direct impact on imaging quality and brings significant influence to the gantry overall performance. However, the currently used method involves a lot of human interaction for CLT corrections, and cannot be implemented as a general process due to its complexity. This paper introduces a fast accuracy method based on Fuzzy C-Means (FCM) clustering algorithm for crystal identification. In the FCM, a cluster center and a fuzzy partition matrix of individual events in the 2-D position are defined. By iteratively updating the cluster centers and the membership grades for each event, we can move the cluster center to the right location in a short time, based on minimizing objective function that represents the distance from any given events to a cluster center weighted by its membership grade. The preliminary result shows that FCM can be used effectively in CLT construction, which significantly reduces the time, and brings excellent accuracy than we expected.
clustering is an efficient and important method to facilitate energy conservation. Aiming to alleviate the high overheads of migrating management information in the re-clustering process of a clustering algorithm, an ...
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ISBN:
(纸本)9781424421831
clustering is an efficient and important method to facilitate energy conservation. Aiming to alleviate the high overheads of migrating management information in the re-clustering process of a clustering algorithm, an energy efficient, Complete Graph-based clustering algorithm (CGCA) is proposed in a densely deployed sensor network. CGCA divides the network into a few complete graphs, each complete graph independently being a cluster. CGCA is only executed at the system activation time and the cluster head role needs only to be rotated among the internal nodes in each cluster at the subsequent re-clustering phase, which incurs greatly reduced communication and computation overheads. Another contribution of our work is to add some mobile nodes to act as gateways so as to join the disconnected cluster heads, which puts the heavy inter-cluster traffic burden on the resource-rich mobile nodes thus saving much energy in the energy constrained sensor nodes. Extensive simulation experiments demonstrate that the number of exchanged messages produced by CGCA is only about 20% that of the traditional identifier based clustering algorithm in a densely deployed case. Furthermore, our proposed achieves an improvement in system lifetime of factor 2 that of the LEACH in a dense sensor network.
Ad hoc network does not depend on any fixed infrastructure, and Its main features are no central structure and the rapid dynamic change. This paper presents the Enhanced Maximum Stability Weighted clustering algorithm...
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ISBN:
(纸本)9781424421077
Ad hoc network does not depend on any fixed infrastructure, and Its main features are no central structure and the rapid dynamic change. This paper presents the Enhanced Maximum Stability Weighted clustering algorithm (EMSWCA), based on the WCA and the MSWCA algorithm. The EMSWCA explicitly identifies the maximum load parameters of clusters, accelerates the convergence speed, and provide a certain degree of QOS. In addition, the simulation experiment proves superiority of the EMSWCA than the WCA and the MSWCA.
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