Recently, many researchers have been focusing on the definition of neural networks for graphs. The basic component for many of these approaches remains the graph convolution idea proposed almost a decade ago. In this paper, we extend this basic component, following an intuition derived from the well-known convolutional filters over multi-dimensional tensors. In particular, we derive a simple, efficient and effective way to introduce a hyper-parameter on graph convolutions that influences the filter size, i.e. its receptive field over the considered graph. We show with experimental results on real-world graph datasets that the proposed graph convolutional filter improves the predictive performance of Deep Graph Convolutional Networks.

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Background Cross-linking and immunoprecipitation followed by next-generation sequencing (CLIP-seq) is the state-of-the-art technique used to experimentally determine transcriptome-wide binding sites of RNA-binding proteins (RBPs). However, it relies on gene expression, which can be highly variable between conditions and thus cannot provide a complete picture of the RBP binding landscape. This creates a demand for computational methods to predict missing binding sites. Although there exist various methods using traditional machine learning and lately also deep learning, we encountered several problems: many of these are not well documented or maintained, making them difficult to install and use, or are not even available. In addition, there can be efficiency issues, as well as little flexibility regarding options or supported features. Results Here, we present RNAProt, an efficient and feature-rich computational RBP binding site prediction framework based on recurrent neural networks. We compare RNAProt with 1 traditional machine learning approach and 2 deep-learning methods, demonstrating its state-of-the-art predictive performance and better run time efficiency. We further show that its implemented visualizations capture known binding preferences and thus can help to understand what is learned. Since RNAProt supports various additional features (including user-defined features, which no other tool offers), we also present their influence on benchmark set performance. Finally, we show the benefits of incorporating additional features, specifically structure information, when learning the binding sites of an hairpin loop binding RBP. Conclusions RNAProt provides a complete framework for RBP binding site predictions, from data set generation over model training to the evaluation of binding preferences and prediction. It offers state-of-the-art predictive performance, as well as superior run time efficiency, while at the same time supporting more features and input types than any other tool available so far. RNAProt is easy to install and use, comes with comprehensive documentation, and is accompanied by informative statistics and visualizations. All this makes RNAProt a valuable tool to apply in future RBP binding site research.

CLIP-seq is the state-of-the-art technique to experimentally determine transcriptome-wide binding sites of RNA-binding proteins (RBPs). However, it relies on gene expression which can be highly variable between conditions, and thus cannot provide a complete picture of the RBP binding landscape. This necessitates the use of computational methods to predict missing binding sites. Here we present GraphProt2, a computational RBP binding site prediction method based on graph convolutional neural networks (GCN). In contrast to current CNN methods, GraphProt2 supports variable length input as well as the possibility to accurately predict nucleotide-wise binding profiles. We demonstrate its superior performance compared to GraphProt and a CNN-based method on single as well as combined CLIP-seq datasets.Introduction 1 RNA-binding proteins (RBPs) regulate many vital steps in the RNA life cycle, such as 2 splicing, transport, stability, and translation [1]. Recent studies suggest a total number 3 of more than 2,000 human RBPs, including 100s of unconventional RBPs, i.e., RBPs 4 lacking known RNA-binding domains [2-4]. Numerous RBPs have been implicated in 5 diseases like cancer, neurodegeneration, and genetic disorders [5-7], urging the need to 6 speed up their functional characterization and shed light on their complex cellular 7 interplay. 8 An important step to understand RBP function is to identify the precise RBP 9 binding locations on regulated RNAs. In this regard, CLIP-seq (cross-linking and 10 immunoprecipitation followed by next generation sequencing) [8] together with its 11 popular modifications PAR-CLIP [9], iCLIP [10], and eCLIP [11] has become the 12state-of-the-art technique to experimentally determine transcriptome-wide binding sites 13 of RBPs. A CLIP-seq experiment for a specific RBP results in a library of reads bound 14 and protected by the RBP, making it possible to deduce its binding sites by mapping 15 the reads back to the respective reference genome or transcriptome. In practice, 16 computational analysis of CLIP-seq data has to be adapted for each CLIP-seq 17 protocol [12]. Within the analysis, arguably the most critical part is the process of peak 18 calling, i.e., to infer RBP binding sites from the mapped read profiles. Among the many 19 existing peak callers, some popular tools are Piranha [13], CLIPper [14], 20 PEAKachu [15], and PureCLIP [16].While peak calling is essential to separate authentic binding sites from unspecific 22 interactions and thus reduce the false positive rate, it cannot solve the problem of 23 expression dependency. In order to detect RBP binding sites by CLIP-seq, the target 24 RNA has to be expressed at a certain level in the experiment. Since gene expression 25 naturally varies between conditions, CLIP-seq data cannot be used directly to make 26 condition-independent binding assumptions on a transcriptome-wide scale. Doing so 27 would only increase the false negative rate, i.e., marking all non-peak regions as 28 non-binding, while in fact one cannot tell from the dat...

Secure localization of vehicles is gaining the attention of researchers from both academia and industry especially due to the emergence of internet of things (IoT). The modern vehicles are usually equipped with circuitries that gives connectivity with other vehicles and with cellular networks such as 4G/Fifth generation cellar network (5G). The challenge of secure localization and positioning is magnified further with the invention of technologies such as autonomous or driverless vehicles based on IoT, satellite, and 5G. Some satellite and IoT based localization techniques exploit machine learning, semantic segmentation, and access control mechanism. Access control provides access grant and secure information sharing mechanism to authorized users and restricts unauthorized users, which is necessary regarding security and privacy of government or military vehicles. Previously, static conflict of interest (COI) based access control was used for security proposes. However, static COI based access control creates excesses and administrative overload that creates latency in execution, which is the least tolerable factor in modern IoT or 5G control vehicles. Therefore, in this paper, a hybrid access control (HAC) model is proposed that implements the dynamic COI in the HAC model on the level of roles. The proposed model is enhanced by modifying the role-based access control (RBAC) model by inserting new attributes of the RBAC entities. The HAC model deals with COI on the level of roles in an efficient manner as compared to previously proposed models. Moreover, this model features significant improvement in terms of dynamic behavior, decreased administrative load, and security especially for vehicular localization. Furthermore, the mathematical modeling of the proposed model is implemented with an example scenario to validate the concept. INDEX TERMS Access control, hybrid access control, secure vehicle localization, machine learning, neural networks, Internet of Things.