Hopaaske2070

Over the years, numerous evidences have demonstrated that microbes living in the human body are closely related to human life activities and human diseases. However, traditional biological experiments are time-consuming and expensive, so it has become a research topic in bioinformatics to predict potential microbe-disease associations by adopting computational methods. selleck chemicals In this study, a novel calculative method called BPNNHMDA is proposed to identify potential microbe-disease associations. In BPNNHMDA, a novel neural network model is first designed to infer potential microbe-disease associations, its input signal is a matrix of known microbe-disease associations, and its output signal is matrix of potential microbe-disease associations probabilities. And moreover, a new activation function is designed based on the hyperbolic tangent function, and its initial connection weights are optimized by adopting GIP similarity for microbes, which can improve the training speed of BPNNHMDA efficiently. Finally, in order to verify the performance of our prediction model, Leave-One-Out Cross Validation and k-Fold Cross Validation are implemented on BPNNHMDA respectively. Simulation results illustrate that BPNNHMDA can achieve reliable AUCs of 0.9242, 0.9127 ± 0.0009 and 0.8955 ± 0.0018 in LOOCV, 5-Fold CV and 2-Fold CV separately, which are superior to previous state-of-the-art methods. Furthermore, case studies demonstrate that BPNNHMDA has excellent prediction ability in practical applications as well.Molecular biomarkers are certain molecules or set of molecules that can be of help for diagnosis or prognosis of diseases or disorders. In the past decades, thanks to the advances in high-throughput technologies, a huge amount of molecular 'omics' data, e.g. transcriptomics and proteomics, have been accumulated. The availability of these omics data makes it possible to screen biomarkers for diseases or disorders. Accordingly, a number of computational approaches have been developed to identify biomarkers by exploring the omics data. In this review, we present a comprehensive survey on the recent progress of identification of molecular biomarkers with machine learning approaches. Specifically, we categorize the machine learning approaches into supervised, un-supervised and recommendation approaches, where the biomarkers including single genes, gene sets and small gene networks. In addition, we further discuss potential problems underlying bio-medical data that may pose challenges for machine learning, and provide possible directions for future biomarker identification.The global alignment of biological networks (GABN) aims to find an optimal alignment between proteins across species, such that both the biological structures and the topological structures of the proteins are maximally conserved. The research on GABN has attracted great attention due to its applications on species evolution, orthology detection and genetic analyses. Most of the existing methods for GABN are difficult to obtain a good tradeoff between the conservation of the biological structures and topological structures. In this paper, we propose a multi-neighborhood learning method for solving GABN (called as CLMNA). CLMNA first models GABN as an optimization of a weighted similarity which evaluates the conserved biological and topological similarities of an alignment, and then it combines a first-proximity, second-proximity and individual-aware proximity learning algorithm to solve the modeled problem. Finally, systematic experiments on 10 pairs of biological networks across 5 species show the superiority of CLMNA over the state-of-the-art network alignment algorithms. They also validate the effectiveness of CLMNA as a refinement method on improving the performance of the compared algorithms.Hemiparesis resulting from a stroke has a direct impact on patients' daily activities. New approaches for motor rehabilitation include Serious Games (SG) because they include (in a motivating way) the three fundamental elements for rehabilitation intensive, repetitive and task-oriented training. This study aims to evaluate the therapeutic effects of a biomedical SG and a scoring system developed for lower limb motor rehabilitation of hemiparetic stroke patients. The SG was inspired by the classic videogame called Pong, where the goal is to control a tennis racquet, but using muscular strength. A knee extensor apparatus was adapted with a load cell and mechanical adjustments for measuring the muscular strength of the quadriceps femoris (QFG) and hamstrings (HSG). A scoring system was proposed to evaluate muscular control. Eleven hemiparetic stroke patients participated in an exercise program using the SG twice a week for ten weeks and only the paretic side was trained. Significant Effect Sizes (d) were found for QFG strength (d=0.5; p=0.021), QFG control (d=1.1; p less then 0.001), HSG strength (d=1.1; p=0.001), HSG control (d=1.5; p=0.003), functional mobility (d=0.3; p less then 0.001), gait speed (d=0.4; p=0.007) and motor recovery (d=1.0; p less then 0.001). Results indicate that the intervention of a SG with both proper apparatus and evaluation system may effectively promote lower limb motor rehabilitation of hemiparetic stroke patients.People with tetraplegia resulting from spinal cord injury experience debilitating hand impairments that may lead to lifelong dependence on others to perform activities of daily living. Wearable robotic devices that actively support hand function during daily living tasks could bring great benefits to this population. In this work, the performance of a textile-based soft robotic glove controlled by the user with a button was evaluated in thirteen participants with tetraplegia. Performance outcomes included activities of daily living using the Jebsen Taylor Hand Function Test, active range of motion of the fingers, and grasp strength for power and pinch grasps. In the Jebsen Test, participants showed significant improvements in performance of activities of daily living with glove assistance, completing a median of 50% more tasks than in their baseline attempt without the glove. Significant improvements were also found for power and pinch grasp forces and active range of motion of the fingers with the glove assistance.