The exploration of drug-target interactions (DTI) is an essential stage in the drug development pipeline. Thanks to the assistance of computational models, notably in the deep learning approach, scientists have been able to shorten the time spent on this stage. Widely practiced deep learning algorithms such as convolutional neural networks and recurrent neural networks are commonly employed in DTI prediction projects. However, they can hardly utilize the natural graph structure of molecular inputs. For that reason, a graph neural network (GNN) is an applicable choice for learning the chemical and structural characteristics of molecules when it represents molecular compounds as graphs and learns the compound features from those graphs. In an effort to construct an advanced deep learning-based model for DTI prediction, we propose Deep Neural Computation (DeepNC), which is a framework utilizing three GNN algorithms: Generalized Aggregation Networks (GENConv), Graph Convolutional Networks (GCNConv), and Hypergraph Convolution-Hypergraph Attention (HypergraphConv). In short, our framework learns the features of drugs and targets by the layers of GNN and 1-D convolution network, respectively. Then, representations of the drugs and targets are fed into fully-connected layers to predict the binding affinity values. The models of DeepNC were evaluated on two benchmarked datasets (Davis, Kiba) and one independently proposed dataset (Allergy) to confirm that they are suitable for predicting the binding affinity of drugs and targets. Moreover, compared to the results of baseline methods that worked on the same problem, DeepNC proves to improve the performance in terms of mean square error and concordance index.

Road traffic accidents have been listed in the top 10 global causes of death for many decades. Traditional measures such as education and legislation have contributed to limited improvements in terms of reducing accidents due to people driving in undesirable statuses, such as when suffering from stress or drowsiness. Attention is drawn to predicting drivers’ future status so that precautions can be taken in advance as effective preventative measures. Common prediction algorithms include recurrent neural networks (RNNs), gated recurrent units (GRUs), and long short-term memory (LSTM) networks. To benefit from the advantages of each algorithm, nondominated sorting genetic algorithm-III (NSGA-III) can be applied to merge the three algorithms. This is named NSGA-III-optimized RNN-GRU-LSTM. An analysis can be made to compare the proposed prediction algorithm with the individual RNN, GRU, and LSTM algorithms. Our proposed model improves the overall accuracy by 11.2–13.6% and 10.2–12.2% in driver stress prediction and driver drowsiness prediction, respectively. Likewise, it improves the overall accuracy by 6.9–12.7% and 6.9–8.9%, respectively, compared with boosting learning with multiple RNNs, multiple GRUs, and multiple LSTMs algorithms. Compared with existing works, this proposal offers to enhance performance by taking some key factors into account—namely, using a real-world driving dataset, a greater sample size, hybrid algorithms, and cross-validation. Future research directions have been suggested for further exploration and performance enhancement.

We introduce an information visualization technique, known as GreenCurve, for large multivariate sparse graphs that exhibit small-world properties. Our fractal-based design approach uses spatial cues to approximate the node connections and thus eliminates the links between the nodes in the visualization. The paper describes a robust algorithm to order the neighboring nodes of a large sparse graph by solving the Fiedler vector of its graph Laplacian, and then fold the graph nodes into a space-filling fractal curve based on the Fiedler vector. The result is a highly compact visualization that gives a succinct overview of the graph with guaranteed visibility of every graph node. GreenCurve is designed with the power grid infrastructure in mind. It is intended for use in conjunction with other visualization techniques to support electric power grid operations. The research and development of GreenCurve was conducted in collaboration with domain experts who understand the challenges and possibilities intrinsic to the power grid infrastructure. The paper reports a case study on applying GreenCurve to a power grid problem and presents a usability study to evaluate the design claims that we set forth.

Cryptocurrencies are rapidly growing and are increasingly accepted by major commercial vendors. However, along with their rising popularity, they have also become the go-to currency for illicit activities driven by the anonymity they provide. Cryptocurrencies such as the one on the Ethereum blockchain provide a way for entities to hide their real-world identities behind pseudonyms, also known as addresses. Hence, the purpose of this work is to uncover the level of anonymity in Ethereum by investigating multiclass classification models for Externally Owned Accounts (EOAs) of Ethereum. The researchers aim to achieve this by examining patterns of transaction activity associated with these addresses. Using a labelled Ethereum address dataset from Kaggle and the Ethereum crypto dataset by Google BigQuery, an address profiles dataset was compiled based on the transaction history of the addresses. The compiled dataset, consisting of 4371 samples, was used to tune and evaluate the Random Forest, Gradient Boosting and XGBoost classifier for predicting the category of the addresses. The best-performing model found for the problem was the XGBoost classifier, achieving an accuracy of 75.3% with a macro-averaged F1-Score of 0.689. Following closely was the Random Forest classifier, with an accuracy of 73.7% and a macro-averaged F1-Score of 0.641. Gradient Boosting came in last with 73% accuracy and a macro-averaged F1-Score of 0.659. Owing to the data limitations in this study, the overall scores of the best model were weaker in comparison to similar research, with the exception of precision, which scored slightly higher. Nevertheless, the results proved that it is possible to predict the category of an Ethereum wallet address such as Phish/Hack, Scamming, Exchange and ICO wallets based on its transaction behaviour.

Applying deep learning to the pervasive graph data is significant because of the unique characteristics of graphs. Recently, substantial amounts of research efforts have been keen on this area, greatly advancing graph-analyzing techniques. In this study, the authors comprehensively review different kinds of deep learning methods applied to graphs. They discuss with existing literature into sub-components of two: graph convolutional networks, graph autoencoders, and recent trends including chemoinformatics research area including molecular fingerprints and drug discovery. They further experiment with variational autoencoder (VAE) analyze how these apply in drug target interaction (DTI) and applications with ephemeral outline on how they assist the drug discovery pipeline and discuss potential research directions.

The research aspiration is to develop a visual framework for the examination timetabling problem. To perform the task we identified visual analytics as a multi-disciplinary field, directly supports the timetable designers, assist with visual metaphors, patterns to make decisions. Here we deemed three stages on the problem domain. It deals with pre-processing, during the processing and post-processing; in each of the processes are with transaction on data which transform into timetables. These processes are interrelated with one another for any heuristic based, evolutionary algorithm guided timetabling systems. However, the importance aspect is the significance of visualization among the three processes and how it contributes in each process. This paper discuss two aspects, it wraps the framework with literature and an implication on the pre-processing through visualizing data and knowledge with graph visualization.