We try to evaluate the sequential and non-sequential and flexible structural alignment methods on SCOP 1.71. Firstly, we compare the flexible method with rigid methods and compare the sequence order dependent methods ...
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Accurately identifying the pathogenicity of mutations at protein-metal binding sites is crucial for uncovering the structural and functional complexity of metalloproteins, and the molecular mechanisms behind numerous ...
Accurately identifying the pathogenicity of mutations at protein-metal binding sites is crucial for uncovering the structural and functional complexity of metalloproteins, and the molecular mechanisms behind numerous diseases. In this study, we present a novel deep learning framework, CASTLE, for the accurate pathogenicity prediction of mutations at metal-binding sites through an effective depiction of the message-passing in local environment surrounding both the metals and metal-binding sites. Specifically, CASTLE weaves multiple attention-driven units to construct comprehensive panoramic message-passing paths, enabling the capture of intricate structural patterns associated with the metal-binding conformations. In addition, CASTLE seamlessly integrates structural information at both residue and atomic levels, and further deeply fuses structural and sequence representations to ensure the incorporation of more comprehensive information. We demonstrate that CASTLE significantly outperforms other state-of-the-art methods across all datasets we evaluated, showcasing its robustness and generalization abilities. Our interpretability analysis illustrates CASTLE's capability in capturing meaningful representations from the metal coordinate-dependent environments. Moreover, the model optimization reaffirms the advantages of our model building strategy, which can effectively capture distinct binding patterns for different metal types. Overall, CASTLE provides a powerful deep learning tool that may offer valuable insights into the study of metalloprotein-related disease mechanisms and drug design.
In this paper, a new multiple-traces Poggio-Miller-Chang-Harrington-Wu-Tsai (MT-PMCHWT) equation is proposed to simulate the electromagnetic scattering from microstrip structures. Different from traditional EFIE-PMCHW...
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The critical node detection for sequential attacks based on cascading failure model is an important way for analyzing network vulnerability, which has attracted the attention of many researchers in the field of comple...
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In this paper, a new image classification method is developed. This approach applies graph decomposition and probabilistic neural networks(PNN) to the task of supervised image classification. We use relational graphs ...
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The supportive number of users achieving the maximum sum rate is studied in uplink massive MIMO systems for zero-forcing(ZF) and maximum-ratio combining(MRC). We obtain the lower bound of the achievable rate of each u...
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ISBN:
(纸本)9781509038237;9781509038220
The supportive number of users achieving the maximum sum rate is studied in uplink massive MIMO systems for zero-forcing(ZF) and maximum-ratio combining(MRC). We obtain the lower bound of the achievable rate of each user for ZF and MRC receivers with any finite number of base station(BS) antennas, respectively. By this theoretical result, this paper will focus on the supportive number of users which was constrained with the minimum achievable rate. Through analyzing the optimal problem of maximizing the overall sum rate, we obtain the expression of optimal number of users and present the user scheduling algorithm for achieving the maximized sum rate. The numerical results show that the massive MIMO systems can improve the supportive number of users, and ZF receivers can support more number of users than MRC under the same condition.
To effectively improve the frequency selectivity of the filter, a substrate integrated waveguide (SIW) filter with miniaturization and out-of-band rejection was designed. To achieve the desired filter response, symmet...
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Sum-difference driven coarray (SDCA) was paid great attention in array-signal-processing (ASP). By considering SDCA, the degrees of freedom (DOFs) for sparse arrays can be further improved. Here, a new transformed nes...
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Based on the clonal selection theory and immune memory theory, a novel artificial immune system algorithm, immune memory clonal programming algorithm (IMCPA), is put forward. Using the theorem of Markov chain, it is p...
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Based on the clonal selection theory and immune memory theory, a novel artificial immune system algorithm, immune memory clonal programming algorithm (IMCPA), is put forward. Using the theorem of Markov chain, it is proved that IMCPA is convergent. Compared with some other evolutionary programming algorithms (like Breeder genetic algorithm), IMCPA is shown to be an evolutionary strategy capable of solving complex machine learning tasks, like high-dimensional function optimization, which maintains the diversity of the population and avoids prematurity to some extent, and has a higher convergence speed.
In this paper, a novel algorithm for image classification is presented which uses the projective value of adjacency spectrum as classified samples. Firstly, the eigenvalues of adjacency matrices constructed on the fea...
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In this paper, a novel algorithm for image classification is presented which uses the projective value of adjacency spectrum as classified samples. Firstly, the eigenvalues of adjacency matrices constructed on the feature point-sets of images are obtained by singular value decomposition. Secondly, the eigenvalues are projected onto the eigenspace by means of the covariance matrix. Finally, image classification is performed by adopting RBF and PNN neural networks as classifiers respectively. Meanwhile, some theoretical analyses are given to support the proposed method.
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