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Accurate and stable cost forecasting of substation projects is of great significance to ensure the economic construction and sustainable operation of power engineering projects. In this paper, a forecasting model based on the improved least squares support vector machine (ILSSVM) optimized by wolf pack algorithm (WPA) is proposed to improve the accuracy and stability of the cost forecasting of substation projects. Firstly, the optimal features are selected through the data inconsistency rate (DIR), which helps reduce redundant input vectors. Secondly, the wolf pack algorithm is used to optimize the parameters of the improved least square support vector machine. Lastly, the cost forecasting method of WPA-DIR-ILSSVM is established. In this paper, 88 substation projects in different regions from 2015 to 2017 are chosen to conduct the training tests to verify the validity of the model. The results indicate that the new hybrid WPA-DIR-ILSSVM model presents better accuracy, robustness, and generality in cost forecasting of substation projects. [ABSTRACT FROM AUTHOR]

Synthetic lethality is an important concept for the development of targeted anti-cancer drugs. Predicting synthetic lethality genes through computational methods can provide target guidance for biological research, thus improving research efficiency and reducing experimental costs. This paper proposed a general, multi-view variational graph auto-encoder framework to solve the synthetic lethality prediction problem. The algorithm introduced known synthetic lethality interactions as a supervise signal and performed supervised training on both local single-view data and global multi-view synthetic lethality graph reconstructions. Through supervised training, the algorithm found the latent representation associated with synthetic lethality in each view at a fine-grained level and fused them together for synthetic lethality prediction. Experimental results on the SynLethDB dataset demonstrate the effectiveness of the method. [ABSTRACT FROM AUTHOR]