An Improved Prediction Model for Zinc-Binding Sites in Proteins Based on Bayesian Method

The zinc ion is the second richest metal ion in organisms. The proteins binding to zinc ions have important biological functions. However, few scholars have integrated the existing tools to predict the zinc-binding sites in proteins. To make up for this gap, this paper combines three well-known prediction tools into an improved model called IBayes_Zinc to predict the zinc-binding sites, and utilizes the advantages of the Bayesian method in handling incomplete or partial missing data. Specifically, the prediction scores of three existing sequence-based prediction tools were adopted, and the missing values were padded, forming an integrated classification tool. Then, the probabilities of positive and negative samples were computed and categorized as the class with higher probabilities. Experiments were conducted on a non-redundant training dataset and an independent testing dataset. The results show that our method surpassed the other three methods by nearly 5–13% in Matthew correlation coefficient (MCC) and outperformed the latter in recall and precision. The research findings promote the detection of zinc-binding sites in protein sequence and the identification of metalloprotein functions

Beyond EHRs:External Clinical knowledge and cohort Features for medication recommendation

Medication recommendation plays an important role in healthcare by supporting clinical decision-making, while rich clinical experiences and knowledge are key factors that contribute to the success of this task. However, existing methods still face certain limitations: the acquisition of clinical experience is confined to the historical records of an individual patient, and encoded clinical knowledge is typically used as coarse auxiliary information to enhance encoded records. To address these limitations, we propose the EXternal Clinical knowlEdge and cohoRt Features (EXCERF) model for the recommendation task. EXCERF constructs an external Memory Neural Network shared across all patients to capture cohort features tailored to specific patient groups, allowing patients to access relevant clinical experiences distributed across a large cohort. Then the model incorporates clinical knowledge tuples as additional biases to refine interactions between clinically related entities within the self-attention mechanism, concurrently considering detailed clinical and semantic knowledge to build patient representations. Finally, EXCERF integrates representations of multiple admissions with GRU to generate the final recommendation. Experimental results on real-world clinical records demonstrates that EXCERF achieves superior performance and facilitates effective medication recommendation

Optimization of Self-Directed Target Coverage in Wireless Multimedia Sensor Network

Video and image sensors in wireless multimedia sensor networks (WMSNs) have directed view and limited sensing angle. So the methods to solve target coverage problem for traditional sensor networks, which use circle sensing model, are not suitable for WMSNs. Based on the FoV (field of view) sensing model and FoV disk model proposed, how expected multimedia sensor covers the target is defined by the deflection angle between target and the sensor’s current orientation and the distance between target and the sensor. Then target coverage optimization algorithms based on expected coverage value are presented for single-sensor single-target, multisensor single-target, and single-sensor multitargets problems distinguishingly. Selecting the orientation that sensor rotated to cover every target falling in the FoV disk of that sensor for candidate orientations and using genetic algorithm to multisensor multitargets problem, which has NP-complete complexity, then result in the approximated minimum subset of sensors which covers all the targets in networks. Simulation results show the algorithm’s performance and the effect of number of targets on the resulting subset

A novel discrete bat algorithm for heterogeneous redundancy allocation of multi-state systems subject to probabilistic common-cause failure

The file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link.This paper focuses on a heterogeneous redundancy allocation problem (RAP) for multi-state series-parallel systems subject to probabilistic common-cause failure and proposes a novel discrete bat algorithm to solve it. Although abundant research studies have been published for solving multi-state RAPs, few of them have studied probabilistic common cause failure, which motivates this paper. Due to the insufficient data of components, an interval-valued universal generating function is utilized to evaluate the availability of components and the whole system. The challenge of solving this kind of RAPs lies in not only the reliability estimation, but also the solution method. This paper presents a novel discrete bat algorithm (BA) for effectively dealing with the proposed RAP and alleviating the premature convergence of BA. Two main features of the adaptation are Hamming distance-based bat movement (HDBM) and Q learning-based local search (QLLS). HDBM transfers the Hamming distance between the current bat and the best bat in the swarm to the movement rate. Then, QLLS utilizes Q-learning to adjust the local search strategies dynamically during the iterations. The computational results from extensive experiments demonstrate that the proposed algorithm is powerful, which is more efficient than other state-of-the-arts on this sort of problems

Milling Surface Roughness Prediction Based on Physics-Informed Machine Learning

Surface roughness is a key indicator of the quality of mechanical products, which can precisely portray the fatigue strength, wear resistance, surface hardness and other properties of the products. The convergence of current machine-learning-based surface roughness prediction methods to local minima may lead to poor model generalization or results that violate existing physical laws. Therefore, this paper combined physical knowledge with deep learning to propose a physics-informed deep learning method (PIDL) for milling surface roughness predictions under the constraints of physical laws. This method introduced physical knowledge in the input phase and training phase of deep learning. Data augmentation was performed on the limited experimental data by constructing surface roughness mechanism models with tolerable accuracy prior to training. In the training, a physically guided loss function was constructed to guide the training process of the model with physical knowledge. Considering the excellent feature extraction capability of convolutional neural networks (CNNs) and gated recurrent units (GRUs) in the spatial and temporal scales, a CNN–GRU model was adopted as the main model for milling surface roughness predictions. Meanwhile, a bi-directional gated recurrent unit and a multi-headed self-attentive mechanism were introduced to enhance data correlation. In this paper, surface roughness prediction experiments were conducted on the open-source datasets S45C and GAMHE 5.0. In comparison with the results of state-of-the-art methods, the proposed model has the highest prediction accuracy on both datasets, and the mean absolute percentage error on the test set was reduced by 3.029% on average compared to the best comparison method. Physical-model-guided machine learning prediction methods may be a future pathway for machine learning evolution