期刊：
IEEE INTERNET OF THINGS JOURNAL,2019年6(1):718-733 ISSN：2327-4662
通讯作者：
Qin, Hua
作者机构：
[Qin, Hua; Cao, Buwen; Zeng, Min; Chen, Weihong] Hunan City Univ, Coll Informat & Elect Engn, Yiyang 413000, Peoples R China.;[Li, Jessica; Peng, Yang] Univ Washington, Div Comp & Software Syst, Bothell, WA 98011 USA.
通讯机构：
[Qin, Hua] H;Hunan City Univ, Coll Informat & Elect Engn, Yiyang 413000, Peoples R China.
关键词：
Dual radio;energy efficiency;Internet of Things (IoT);pipeline
摘要：
The future Internet of Things (IoT) will enable Internet connectivity for a vast amount of battery-powered devices, which usually need to communicate with each other or to some remote gateways through multihop communications. Although ZigBee has become a widely used communication technology in IoT, Wi-Fi, on the other hand, has its unique advantages such as high throughput and native IP compatibility, despite its potentially higher energy consumption. With the development of IoT, more and more IoT devices are equipped with multiple radio interfaces, such as both Wi-Fi and ZigBee. Inspired by this, we propose a dual-interface dual-pipeline scheduling (DIPS) scheme, which leverages an activation pipeline mainly constructed by low-power ZigBee interfaces to wake up a data pipeline constructed by high-power Wi-Fi interfaces on demand, toward enabling multihop data delivery in IoT. The objective is to minimize network energy consumption while satisfying certain end-to-end delay requirements. Extensive simulations and prototype-based experiments have been conducted. The results show that the energy consumption of DIPS is 96.5% and 92.8% lower than that of the IEEE 802.11's standard power saving scheme and a state-of-the-art pipeline-based scheme in moderate traffic scenarios, respectively.
摘要：
The identification of overlapping protein complexes in proteinprotein interaction (PPI) networks may elucidate cellular functional organizations and their underlying cellular mechanisms. Recently, many protein complex mining algorithms have been developed for PPI networks. However, the majority of available algorithms primarily depend on mining dense subgraphs as protein complexes, thereby failing to consider the inherent biological meanings between protein pairs. Thus, methods for identifying protein complexes using the biological significance hidden in edges need to be investigated. In this paper, we propose IK-medoids, an improved method that detects overlapping protein complexes from weighted PPI networks based on the rough fuzzy relationships between protein pairs. The presented algorithm is primarily based on the fuzzy relationship that obtains the non-overlapping protein substructure, and then K-medoids is executed from the proteins in the PPI network. Next, the similarity between one protein and each candidate complex is calculated to determine whether the protein belongs to one or multiple complexes with the ration of each similarity to maximum similarity. In the end, overlapped protein complexes are merged to form the final protein complexes. We apply the method to three PPI networks and validate the results using two reference protein complexes retrieved from public databases. Experimental results show that our method outperforms classical algorithms, such as ClusterONE, CMC, MCL, OSLOM, and RFC, and achieves ideal overall performance in terms of F-measure, sensitivity, and accuracy.
关键词：
Synergistic drug combinations;Computational methods;Feature;Similarity measure;Machine learning;Network.
摘要：
Synergistic drug combinations play an important role in the treatment of complex diseases. The identification of effective drug combination is vital to further reduce the side effects and improve therapeutic efficiency. In previous years, in vitro method has been the main route to discover synergistic drug combinations. However, many limitations of time and resource consumption lie within the in vitro method. Therefore, with the rapid development of computational models and the explosive growth of large and phenotypic data, computational methods for discovering synergistic drug combinations are an efficient and promising tool and contribute to precision medicine. It is the key of computational methods how to construct the computational model. Different computational strategies generate different performance. In this review, the recent advancements in computational methods for predicting effective drug combination are concluded from multiple aspects. First, various datasets utilized to discover synergistic drug combinations are summarized. Second, we discussed feature-based approaches and partitioned these methods into two classes including feature-based methods in terms of similarity measure, and feature-based methods in terms of machine learning. Third, we discussed network-based approaches for uncovering synergistic drug combinations. Finally, we analyzed and prospected computational methods for predicting effective drug combinations.
