With the continuous expansion of China's power grid scale and the increasing number of power users year by year, it is necessary to ensure the normal power supply of users. When a power failure occurs, it is particularly critical whether the emergency repair task can be completed quickly and scientifically. In this paper, an intelligent repair model of “unmanned” distribution network based on deep reinforcement learning is proposed, which adopts speech recognition tech-nology and deep reinforcement learning algorithm to achieve the “unmanned” of the whole system. Users can transmit the emergency repair information to the voice recognition module of the power supply emergency repair center by voice, SMS and IMS, and the module will get the emergency repair position and the amount of emergency repair tasks. Then, the resource allocation module is used to learn the emergency repair resource allocation strategy online, and the intelligent control of emergency repair in distribution network is realized. To verify the proposed algorithm, it is compared with two typical allocation strategies under the same settings. The results of the experiments demonstrate that the method based on deep reinforcement learning performs better in terms of emergency repair delay and intelligent emergency repair of the power supply in distribution networks.

MOTIVATION: Cancer somatic driver mutations associated with genes within a pathway often show a mutually exclusive pattern across a cohort of patients. This mutually exclusive mutational signal has been frequently used to distinguish driver from passenger mutations and to investigate relationships among driver mutations. Current methods for de novo discovery of mutually exclusive mutational patterns are limited because the heterogeneity in background mutation rate can confound mutational patterns, and the presence of highly mutated genes can lead to spurious patterns. In addition, most methods only focus on a limited number of pre-selected genes and are unable to perform genome-wide analysis due to computational inefficiency. RESULTS: We introduce a statistical framework, MEScan, for accurate and efficient mutual exclusivity analysis at the genomic scale. Our framework contains a fast and powerful statistical test for mutual exclusivity with adjustment of the background mutation rate and impact of highly mutated genes, and a multi-step procedure for genome-wide screening with the control of false discovery rate. We demonstrate that MEScan more accurately identifies mutually exclusive gene sets than existing methods and is at least two orders of magnitude faster than most methods. By applying MEScan to data from four different cancer types and pan-cancer, we have identified several biologically meaningful mutually exclusive gene sets. AVAILABILITY AND IMPLEMENTATION: MEScan is available as an R package at https://github.com/MarkeyBBSRF/MEScan. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.