The study results indicate that textual information from EMRs could facilitate phenotyping of ischemic stroke when this information was combined with structured information. Furthermore, decomposition of this multi-class problem into binary classification tasks followed by aggregation of classification results could improve the performance.Area under the receiver operating characteristics curve (AUC) is an important metric for a wide range of machine-learning problems, and scalable methods for optimizing AUC have recently been proposed. However, handling very large data sets remains an open challenge for this problem. This article proposes a novel approach to AUC maximization based on sampling mini-batches of positive/negative instance pairs and computing U-statistics to approximate a global risk minimization problem. The resulting algorithm is simple, fast, and learning-rate free. We show that the number of samples required for good performance is independent of the number of pairs available, which is a quadratic function of the positive and negative instances. Extensive experiments show the practical utility of the proposed method.This article proposes a real-time event-triggered near-optimal controller for the nonlinear discrete-time interconnected system. The interconnected system has a number of subsystems/agents, which pose a nonzero-sum game scenario. The control inputs/policies based on proposed event-based controller methodology attain a Nash equilibrium fulfilling the desired goal of the system. The near-optimal control policies are generated online only at events using actor-critic neural network architecture whose weights are updated too at the same instants. The approach ensures stability as the event-triggering condition for agents is derived using Lyapunov stability analysis. The lower bound on interevent time, boundedness of closed-loop parameters, and optimality of the proposed controller are also guaranteed. The efficacy of the proposed approach has been validated on a practical heating, ventilation, and air-conditioning system for achieving the desired temperature set in four zones of a building. The control update instants are minimized to as low as 27% for the desired temperature set.Control-theoretic differential games have been used to solve optimal control problems in multiplayer systems. Most existing studies on differential games either assume deterministic dynamics or dynamics corrupted with additive noise. https://www.selleckchem.com/products/caerulein.html In realistic environments, multidimensional environmental uncertainties often modulate system dynamics in a more complicated fashion. In this article, we study stochastic multiplayer differential games, where the players' dynamics are modulated by randomly time-varying parameters. We first formulate two differential games for systems of general uncertain linear dynamics, including the two-player zero-sum and multiplayer nonzero-sum games. We then show that optimal control policies, which constitute the Nash equilibrium solutions, can be derived from the corresponding Hamiltonian functions. Stability is proven using the Lyapunov type of analysis. In order to solve the stochastic differential games online, we integrate reinforcement learning (RL) and an effective uncertainty sampling method called the multivariate probabilistic collocation method (MPCM). Two learning algorithms, including the on-policy integral RL (IRL) and off-policy IRL, are designed for the formulated games, respectively. We show that the proposed learning algorithms can effectively find the Nash equilibrium solutions for the stochastic multiplayer differential games.The discovery of potential Drug-Target Interactions (DTIs) is a determining step in the drug discovery and repositioning process, as the effectiveness of the currently available antibiotic treatment is declining. Although putting efforts on the traditional in vivo or in vitro methods, pharmaceutical financial investment has been reduced over the years. Therefore, establishing effective computational methods is decisive to find new leads in a reasonable amount of time. Successful approaches have been presented to solve this problem but seldom protein sequences and structured data are used together. In this paper, we present a deep learning architecture model, which exploits the particular ability of Convolutional Neural Networks (CNNs) to obtain 1D representations from protein sequences (amino acid sequence) and compounds SMILES (Simplified Molecular Input Line Entry System) strings. These representations can be interpreted as features that express local dependencies or patterns that can then be used in a Fully Connected Neural Network (FCNN), acting as a binary classifier. The results achieved demonstrate that using CNNs to obtain representations of the data, instead of the traditional descriptors, lead to improved performance. The proposed end-to-end deep learning method outperformed traditional machine learning approaches in the correct classification of both positive and negative interactions.Due to the high consumption of cost and time for experimental verification in clinical trials, drug response prediction by computational models have become important challenges. The existing drug response data in diverse cell lines enable prediction of potential sensitive associations. Here, we propose a weight-based modular mapping method, named as WMMDCA, to predict drug-cell line associations. The method fully considers the effects of drugs' chemical structural feature, and adds modular information into the network projection. Leave-one-out cross-validation was used to evaluate the predictive ability of WMMDCA, which showed the best performance among several state-of-the-art methods in not only the whole dataset but also the major tissue types of cell lines. Literature support of highly ranked potential associations was found manually, demonstrating the effectiveness of WMMDCA on drug response prediction.This paper presents a novel Electrocardiogram (ECG) denoising approach based on the generative adversarial network (GAN). Noise is often associated with the ECG signal recording process. Denoising is central to most of the ECG signal processing tasks. The current ECG denoising techniques are based on the time domain signal decomposition methods. These methods use some kind of thresholding and filtering approaches. In our proposed technique, convolutional neural network (CNN) based GAN model is effectively trained for ECG noise filtering. In contrast to existing techniques, we performed end-to-end GAN model training using the clean and noisy ECG signals. MIT-BIH Arrhythmia database is used for all the qualitative and quantitative analyses. The improved ECG denoising performance open the door for further exploration of GAN based ECG denoising approach.