In structure-based virtual screening, compound ranking through a consensus of scores from a variety of docking programs or scoring functions, rather than ranking by scores from a single program, provides better predictive performance and reduces target performance variability. Here we compare traditional consensus scoring methods with a novel, unsupervised gradient boosting approach. We also observed increased score variation among active ligands and developed a statistical mixture model consensus score based on combining score means and variances. To evaluate performance, we used the common performance metrics ROCAUC and EF1 on 21 benchmark targets from DUD-E. Traditional consensus methods, such as taking the mean of quantile normalized docking scores, outperformed individual docking methods and are more robust to target variation. The mixture model and gradient boosting provided further improvements over the traditional consensus methods. These methods are readily applicable to new targets in academic research and overcome the potentially poor performance of using a single docking method on a new target.

Recently, the robotic arm control system based on a brain-computer interface (BCI) has been employed to help the disabilities to improve their interaction abilities without body movement. However, it's the main challenge to implement the desired task by a robotic arm in a three-dimensional (3D) space because of the instability of electroencephalogram (EEG) signals and the interference by the spontaneous EEG activities. Moreover, the free motion control of a manipulator in 3D space is a complicated operation that requires more output commands and higher accuracy for brain activity recognition. Based on the above, a steady-state visual evoked potential (SSVEP)-based synchronous BCI system with six stimulus targets was designed to realize the motion control function of the seven degrees of freedom (7-DOF) robotic arm. Meanwhile, a novel template-based method, which builds the optimized common templates (OCTs) from various subjects and learns spatial filters from the common templates and the multichannel EEG signal, was applied to enhance the SSVEP recognition accuracy, called OCT-based canonical correlation analysis (OCT-CCA). The comparison results of offline experimental based on a public benchmark dataset indicated that the proposed OCT-CCA method achieved significant improvement of detection accuracy in contrast to CCA and individual template-based CCA (IT-CCA), especially using a short data length. In the end, online experiments with five healthy subjects were implemented for achieving the manipulator real-time control system. The results showed that all five subjects can accomplish the tasks of controlling the manipulator to reach the designated position in the 3D space independently.

Excellent ranking power along with well calibrated probability estimates are needed in many classification tasks. In this paper, we introduce a technique, Calibrated Boosting-Forest 1 that captures both. This novel technique is an ensemble of gradient boosting machines that can support both continuous and binary labels. While offering superior ranking power over any individual regression or classification model, Calibrated Boosting-Forest is able to preserve well calibrated posterior probabilities. Along with these benefits, we provide an alternative to the tedious step of tuning gradient boosting machines. We demonstrate that tuning Calibrated Boosting-Forest can be reduced to a simple hyper-parameter selection. We further establish that increasing this hyper-parameter improves the ranking performance under a diminishing return. We examine the effectiveness of Calibrated Boosting-Forest on ligand-based virtual screening where both continuous and binary labels are available and compare the performance of Calibrated Boosting-Forest with logistic regression, gradient boosting machine and deep learning. Calibrated Boosting-Forest achieved an approximately 48% improvement compared to a stateof-art deep learning model. Moreover, it achieved around 95% improvement on probability quality measurement compared to the best individual gradient boosting machine. Calibrated Boosting-Forest offers a benchmark demonstration that in the field of ligand-based virtual screening, deep learning is not the universally dominant machine learning model and good calibrated probabilities can better facilitate virtual screening process.

Since current literature does not explain how children aged 3-6 reacted when the walking direction was reversed or what its mechanism is, the aim of this study was to understand the mechanism of their 'Neuro-musculo-skeletal' systems in the process of direction changes, as well as its coordination features. The kinematics of forward walking (FW) and backward walking (BW) of 96 subjects were measured by the Coda motion system and their Euler angles in lower limb joints were first collected. According to the coordination algorithm, the phase angle (PA) in the knee and ankle and the continuous relative phase angle (CRP) between the two joints were calculated; further the mean, standard deviation (SD) and range of data for variables of PA and CRP were contrasted between FW and BW. All the statistical models were executed under SPSS with a significance level of 0.05 and a confidence interval of 95%. The results show that children in BW first had an unstable velocity in their ankles; further, PA in both their ankles and knees were distributed in a limited range. Meanwhile the key gait events were not obtained in BW in all age groups. A similar CRP was seen between FW and BW, but a significant difference existed between the two types of gait. The majority of the mean and range of PA and CRP were recorded with significant distinctions between FW and BW in each age group. Finally, significant gender differences existed in all variables of BW in each age group. Overall, although achieving the BW was easy for preschool toddlers (aged 3-6), but their coordination in lower-limb were still in developing and further fine tuning; moreover, their clues in backwarding also tell the detail of development in the 'Neuro-musculo-skeletal' system.