Efficient and stable resource management in ultra-dense networks is essential for interference reduction and quality of service guarantee for large-scale users. Although reinforcement learning is currently the most talked-about method for achieving efficient resource allocation, it still faces significant challenges when dealing with the key adaptive issues, such as strong interference, large-scale users dynamic demands, and differentiated user priority requirements. In the case, this paper proposes a pervasive power allocation method in the framework of deep reinforcement learning. Concretely, we develop a noisy deep Q-network for guaranteeing stable exploration of average rate and accelerating the model convergence, where the parameterized noise is incorporated into the connection weights of neural networks. On the other hand, considering the varying priority levels of different users, we devise a prioritized experience replay mechanism aimed at enhancing the service quality for users with high importance. Simulation results demonstrate that our proposal can surpass the state-of-the-art methods in terms of average rate, convergence, stability, and model complexity, achieving more adaptive power allocation.

SUMMARY: MeRIPseqPipe is an integrated and automatic pipeline that can provide users a friendly solution to perform in-depth mining of MeRIP-seq data. It integrates many functional analysis modules, range from basic processing to downstream analysis. All the processes are embedded in Nextflow with Docker support, which ensures high reproducibility and scalability of the analysis. MeRIPseqPipe is particularly suitable for analyzing a large number of samples at once with a simple command. The final output directory is structured based on each step and tool. And visualization reports containing various tables and plots are provided as HTML files. AVAILABILITY AND IMPLEMENTATION: MeRIPseqPipe is freely available at https://github.com/canceromics/MeRIPseqPipe. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

N6-methyladenosine (m6A) is the most abundant posttranscriptional modification in mammalian mRNA molecules and has a crucial function in the regulation of many fundamental biological processes. The m6A modification is a dynamic and reversible process regulated by a series of writers, erasers and readers (WERs). Different WERs might have different functions, and even the same WER might function differently in different conditions, which are mostly due to different downstream genes being targeted by the WERs. Therefore, identification of the targets of WERs is particularly important for elucidating this dynamic modification. However, there is still no public repository to host the known targets of WERs. Therefore, we developed the m6A WER target gene database (m6A2Target) to provide a comprehensive resource of the targets of m6A WERs. M6A2Target provides a user-friendly interface to present WER targets in two different modules: 'Validated Targets', referred to as WER targets identified from low-throughput studies, and 'Potential Targets', including WER targets analyzed from high-throughput studies. Compared to other existing m6A-associated databases, m6A2Target is the first specific resource for m6A WER target genes. M6A2Target is freely accessible at http://m6a2target.canceromics.org.

Unsupervised clustering of high-throughput gene expression data is widely adopted for cancer subtyping. However, cancer subtypes derived from a single dataset are usually not applicable across multiple datasets from different platforms. Merging different datasets is necessary to determine accurate and applicable cancer subtypes but is still embarrassing due to the batch effect. CrossICC is an R package designed for the unsupervised clustering of gene expression data from multiple datasets/platforms without the requirement of batch effect adjustment. CrossICC utilizes an iterative strategy to derive the optimal gene signature and cluster numbers from a consensus similarity matrix generated by consensus clustering. This package also provides abundant functions to visualize the identified subtypes and evaluate subtyping performance. We expected that CrossICC could be used to discover the robust cancer subtypes with significant translational implications in personalized care for cancer patients. AVAILABILITY AND IMPLEMENTATION: The package is implemented in R and available at GitHub (https://github.com/bioinformatist/CrossICC) and Bioconductor (http://bioconductor.org/packages/release/bioc/html/CrossICC.html) under the GPL v3 License.

As one of the three main scenarios of 5G, massive machine-type communications (mMTC) is expected to play an essential role in the future. However, the limited battery of the machine-type communication devices (MTCDs) restricts the network performance. Unmanned aerial vehicles (UAVs) attract more attention recently in cellular networks. The collaboration between the UAV and MTCDs has been a crucial topic to enhance the network performance. In this paper, an adaptive and cost-efficient model is proposed to collect data from MTCDs in a large area assisted by the UAVs. We propose the strategies for higher energy efficiency of MTCDs and longer lifetime of the network based on the model. According to the strategies, we propose a Global Energy-efficient ant colony optimization (ACO) routing algorithm for UAV-assisted mMTC networks (GEAU) to reduce the energy consumption and prolong the lifetime of the networks. Furthermore, in order to reduce the complexity, we propose a practical algorithm with similar performances. Simulations are carried out and illustrate that the proposed algorithms have superior performances compared with existing works.

To avoid the Radio Access Network overload caused by massive random access attempts from Machine-Type Communication (MTC), we propose a Quality of Service (QoS)-based Dynamic and Adaptive Mechanism (QDAM). Moreover, we propose a more practical algorithm based on QDAM to solve the difficulty in obtaining the total number of accessing devices in reality. Both of the proposed algorithms combine dynamic allocation of Random Access Channel (RACH) resource scheme and adaptive Access Class Barring (ACB) scheme. First, we give priority to the delay-sensitive devices when allocating preambles. Afterwards, based on the number of allocated preambles and the number of devices failed to access, we adaptively adjust the ACB factors of delay-sensitive devices and delay-tolerant devices. In addition, to minimize the delay of delay-sensitive devices and improve the resource efficiency, we derive the required number of preambles for them. Simulation results demonstrate that the proposed two algorithms outperform other reference algorithms in terms of access delay, resource efficiency and average throughput.