Identifying lethal proteins is important for understanding the intricate mechanism governing life. Researchers have shown that the lethality of a protein can be computed based on its topological position in the protein-protein interaction (PPI) network. Performance of current approaches has been less than satisfactory as the lethality of a protein is a functional characteristic that cannot be determined solely by network topology. Furthermore, a significant number of lethal proteins have low connectivity in the interaction networks but are overlooked by most current methods.Our work reveals that a protein's lethality correlates more strongly with its "functional centrality" than pure topological centrality. We define functional centrality as the topological centrality within a subnetwork of proteins with similar functions. Evaluation experiments on four Saccharomyces cerevisiae P P I datasets showed that NFC performed significantly better than all the other existing computational techniques. Our method was able t o detect low connectivity lethal proteins that were previously undetected by conventional methods. T h e results and an online version of NFC is available at http://lethalproteins.i2r.a-star.edu.sg
Identifying the rising stars is an important but difficult human resource exercise in all organizations. Rising stars are those who currently have relatively low profiles but may eventually emerge as prominent contributors to the organizations. In this paper, we propose a novel PubRank algorithm to identify rising stars in research communities by mining the social networks of researchers in terms of their co-authorship relationships. Experimental results show that PubRank algorithm can be used to effectively mine the bibliography networks to search for rising stars in the research communities.
The deployment of a sensor node to manage a group of sensors and collate their readings for system health monitoring is gaining popularity within the manufacturing industry. Such a sensor node is able to perform real-time configurations of the individual sensors that are assigned to it. Sensors are capable of acquiring data at different sampling frequencies based on the sensing requirements. The different sampling rates not only affect the power consumption, sensor lifespan, and the resultant network bandwidth usage due to the data transfer incurred. These settings also have an immediate impact on the accuracy of the diagnostics/prognostics models that are employed for system health monitoring. In this paper we propose an adaptive classification system architecture for system health monitoring that is well suited to accommodate and to take advantage of the variable sampling rate of sensors. In this paper, we demonstrate how our proposed system is able to work and control a sensor network with adaptive sampling frequencies. This will in turn yield a more effective health monitoring system with reduced power consumption thereby extending the sensors' lifespan and reducing the resultant network traffic and data logging requirements.
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