Community detection is a hot issue in the study of complex networks. Many community detection algorithms have been put forward in different fields. But most of the existing community detection algorithms are used to find disjoint community structure. In order to make full use of the disjoint community detection algorithms to adapt to the new demand of overlapping community detection, this paper proposes an overlapping community detection algorithm extended from disjoint community structure by selecting overlapping nodes (ONS-OCD). In the algorithm, disjoint community structure with high qualities is firstly taken as input, then, potential members of each community are identified. Overlapping nodes are determined according to the node contribution to the community. Finally, adding overlapping nodes to all communities they belong to and get the final overlapping community structure. ONS-OCD algorithm reduces the computation of judging overlapping nodes by narrowing the scope of the potential member nodes of each community. Experimental results both on synthetic and real networks show that the community detection quality of ONS-OCD algorithm is better than several other representative overlapping community detection algorithms.
Identifying the interaction between drugs and target proteins is an important area of drug research, which provides a broad prospect for low-risk and faster drug development. However, due to the limitations of traditional experiments when revealing drug-protein interactions (DTIs), the screening of targets not only takes a lot of time and money but also has high false-positive and false-negative rates. Therefore, it is imperative to develop effective automatic computational methods to accurately predict DTIs in the postgenome era. In this article, we propose a new computational method for predicting DTIs from drug molecular structure and protein sequence by using the stacked autoencoder of deep learning, which can adequately extract the raw data information. The proposed method has the advantage that it can automatically mine the hidden information from protein sequences and generate highly representative features through iterations of multiple layers. The feature descriptors are then constructed by combining the molecular substructure fingerprint information, and fed into the rotation forest for accurate prediction. The experimental results of fivefold cross-validation indicate that the proposed method achieves superior performance on gold standard data sets (enzymes, ion channels, GPCRs [G-protein-coupled receptors], and nuclear receptors) with accuracy of 0.9414, 0.9116, 0.8669, and 0.8056, respectively. We further comprehensively explore the performance of the proposed method by comparing it with other feature extraction algorithms, state-of-the-art classifiers, and other excellent methods on the same data set. The excellent comparison results demonstrate that the proposed method is highly competitive when predicting drug-target interactions.
Community structure is the key aspect of complex network analysis and it has important practical significance. While in real networks, some nodes may belong to multiple communities, so overlapping community detection attracts more and more attention. But most of the existing overlapping community detection algorithms increase the time complexity in some extent. In order to detect overlapping community structures in complex network more effectively, we propose a novel overlapping community detection method by local community expansion called OCDLCE. The proposed algorithm firstly partitions the network into small local communities using the local structural information, and then merges these communities to the final overlapping community structures. We present the concept of community connectivity as the criterion of community combination in the second stage of the proposed algorithm. The experimental results on both synthetic and real networks demonstrate that our algorithm improves the community detection performance, and at the same time, its time efficiency is better than the state-of-the-art methods.
Facial action unit (AU) detection is challenging due to the difficulty in capturing correlated information from subtle and dynamic AUs. Existing methods often resort to the localization of correlated regions of AUs, in which predefining local AU attentions by correlated facial landmarks often discards essential parts, or learning global attention maps often contains irrelevant areas. Furthermore, existing relational reasoning methods often employ common patterns for all AUs while ignoring the specific way of each AU. To tackle these limitations, we propose a novel adaptive attention and relation (AAR) framework for facial AU detection. Specifically, we propose an adaptive attention regression network to regress the global attention map of each AU under the constraint of attention predefinition and the guidance of AU detection, which is beneficial for capturing both specified dependencies by landmarks in strongly correlated regions and facial globally distributed dependencies in weakly correlated regions. Moreover, considering the diversity and dynamics of AUs, we propose an adaptive spatio-temporal graph convolutional network to simultaneously reason the independent pattern of each AU, the inter-dependencies among AUs, as well as the temporal dependencies. Extensive experiments show that our approach (i) achieves competitive performance on challenging benchmarks including BP4D, DISFA, and GFT in constrained scenarios and Aff-Wild2 in unconstrained scenarios, and (ii) can precisely learn the regional correlation distribution of each AU.
BRCA1-associated protein-1 (BAP1) is an important nuclear-localized deubiquitinating enzyme that serves as a tumor suppressor in lung cancer; however, its function and its regulation are largely unknown. In this study, we found that BAP1 protein levels were dramatically diminished in lung cancer tissues while its mRNA levels did not differ significantly, suggesting that a post-transcriptional mechanism was involved in BAP1 regulation. Because microRNAs (miRNAs) are powerful post-transcriptional regulators of gene expression, we used bioinformatic analyses to search for miRNAs that could potentially bind BAP1. We predicted and experimentally validated miR-31 as a direct regulator of BAP1. Moreover, we showed that miR-31 promoted proliferation and suppressed apoptosis in lung cancer cells and accelerated the development of tumor growth in xenograft mice by inhibiting BAP1. Taken together, this study highlights an important role for miR-31 in the suppression of BAP1 in lung cancer cells and may provide insights into the molecular mechanisms of lung carcinogenesis.
Dubin-Johnson syndrome (DJS), also known as chronic idiopathic jaundice, black liver-jaundice syndrome, etc. most often develops in adolescents or young adults, and is more common in males, with clinical manifestations of asymptomatic long-term mild-moderate jaundice.The disease belongs to hereditary non-hemolytic jaundice.It is an autosomal recessive genetic disease caused by variants in the ATP-binding cassette subfamily C member (ABCC2) gene.It is characterized by intermittent, predominantly conjugated hyperbilirubinemia and liver pigmentation.Routine ABCC2 gene variant analysis can help in the diagnosis of DJS.In the present study, we reported a patient with Dubin-Johnson syndrome who underwent jaundice-related gene sequencing and identified two novel unknown and significant variants: c.2439+5G>A(p.?) and c.2345_2347del (p.Tyr782_Leu783delinsPhe) of the ABCC2
Deng, et al. proposed a security-provable mutually authenticated key agreement protocol MAKAP for mobile communication in 2003. This paper demonstrates by mounting an effective attack against MAKAP that the protocol has security flaws. It is vulnerable against unknown key-share attack. This paper investigates the reasons why such flaws exist and proposes an improved protocol version (called MAKAP-I protocol). The MAKAP-I protocol is not only provably secure within the random oracle model but also more efficient and practical in terms of computation and communication cost memory requirement and implementation cost, than the original MAKAP protocol.
Four syllable words in modern Chinese are mostly new words, which are nearly all four morpheme words mainly in the form of AB|CD, consisting of a modifier and the modified, and of which noun makes 83%.
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