Focused on the issue that traditional machine learning algorithms still need to manually represent features, a protein subcellular localization algorithm based on the deep network of Stacked Denoising AutoEncoder (SDAE) was proposed. Firstly, the improved Pseudo-Amino Acid Composition (PseAAC), Pseudo Position Specific Scoring Matrix (PsePSSM) and Conjoint Traid (CT) were used to extract the features of the protein sequence respectively, and the feature vectors obtained by these three methods were fused to obtain a new feature expression model of protein sequence. Secondly, the fused feature vector was input into the SDAE deep network to automatically learn more effective feature representation. Thirdly, the Softmax regression classifier was adopted to make the classification and prediction of subcells, and leave-one-out cross validation was performed on Viral proteins and Plant proteins datasets. Finally, the results of the proposed algorithm were compared with those of the existing algorithms such as mGOASVM (multi-label protein subcellular localization based on Gene Ontology and Support Vector Machine) and HybridGO-Loc (mining Hybrid features on Gene Ontology for predicting subcellular Localization of multi-location proteins). Experimental results show that the new algorithm achieves 98.24% accuracy on Viral proteins dataset, which is 9.35 Percentage Points higher than that of mGOASVM algorithm. And the new algorithm achieves 97.63% accuracy on Plant proteins dataset, which is 10.21 percentage points and 4.07 percentage points higher than those of mGOASVM algorithm and HybridGO-Loc algorithm respectively. To sum up, it can be shown that the proposed new algorithm can effectively improve the accuracy of the prediction of protein subcellular localization.

Aiming at the problems of limited energy of nodes, short life cycle and low throughput of Wireless Sensor Network (WSN), a WSN Clustering Routing algorithm based on Genetic Algorithm (GA) and Fuzzy C-Means (FCM) clustering (GAFCMCR) was proposed, which adopted the method of centralized clustering and distributed cluster head election. Network clustering was performed by the base station using a FCM clustering algorithm optimized by GA during network initialization. The cluster head of the first round was the node closest to the center of the cluster. From the second round, the election of the cluster head was carried out by the cluster head of the previous round. The residual energy of candidate node, the distance from the node to the base station, and the mean distance from the node to other nodes in the cluster were considered in the election process, and the weights of these three factors were real-time adjusted according to network status. In the data transfer phase, the polling mechanism was introduced into intra-cluster communication. The simulation results show that, compared with the LEACH (Low Energy Adaptive Clustering Hierarchy) algorithm and the K-means-based Uniform Clustering Routing (KUCR) algorithm, the life cycle of the network in GAFCMCR is prolonged by 105% and 20% respectively. GAFCMCR has good clustering effect, good energy balance and higher throughput.