73 research outputs found
DeepMHCI: an anchor position-aware deep interaction model for accurate MHC-I peptide binding affinity prediction
No full textPublisher Copyright: © The Author(s) 2023. Published by Oxford University Press.MOTIVATION: Computationally predicting major histocompatibility complex class I (MHC-I) peptide binding affinity is an important problem in immunological bioinformatics, which is also crucial for the identification of neoantigens for personalized therapeutic cancer vaccines. Recent cutting-edge deep learning-based methods for this problem cannot achieve satisfactory performance, especially for non-9-mer peptides. This is because such methods generate the input by simply concatenating the two given sequences: a peptide and (the pseudo sequence of) an MHC class I molecule, which cannot precisely capture the anchor positions of the MHC binding motif for the peptides with variable lengths. We thus developed an anchor position-aware and high-performance deep model, DeepMHCI, with a position-wise gated layer and a residual binding interaction convolution layer. This allows the model to control the information flow in peptides to be aware of anchor positions and model the interactions between peptides and the MHC pseudo (binding) sequence directly with multiple convolutional kernels. RESULTS: The performance of DeepMHCI has been thoroughly validated by extensive experiments on four benchmark datasets under various settings, such as 5-fold cross-validation, validation with the independent testing set, external HPV vaccine identification, and external CD8+ epitope identification. Experimental results with visualization of binding motifs demonstrate that DeepMHCI outperformed all competing methods, especially on non-9-mer peptides binding prediction. AVAILABILITY AND IMPLEMENTATION: DeepMHCI is publicly available at https://github.com/ZhuLab-Fudan/DeepMHCI.Peer reviewe
Topology-Driven Attribute Recovery for Attribute Missing Graph Learning in Social Internet of Things
No full textWith the advancement of information technology, the Social Internet of Things (SIoT) has fostered the integration of physical devices and social networks, deepening the study of complex interaction patterns. Text Attribute Graphs (TAGs) capture both topological structures and semantic attributes, enhancing the analysis of complex interactions within the SIoT. However, existing graph learning methods are typically designed for complete attributed graphs, and the common issue of missing attributes in Attribute Missing Graphs (AMGs) increases the difficulty of analysis tasks. To address this, we propose the Topology-Driven Attribute Recovery (TDAR) framework, which leverages topological data for AMG learning. TDAR introduces an improved pre-filling method for initial attribute recovery using native graph topology. Additionally, it dynamically adjusts propagation weights and incorporates homogeneity strategies within the embedding space to suit AMGs' unique topological structures, effectively reducing noise during information propagation. Extensive experiments on public datasets demonstrate that TDAR significantly outperforms state-of-the-art methods in attribute reconstruction and downstream tasks, offering a robust solution to the challenges posed by AMGs. The code is available at https://github.com/limengran98/TDAR.This project is jointly supported by the Shenzhen Fundamental Research Program (No. JCYJ20240813151129038), the National Natural Science Foundation of China (Nos. 52172350, 51775565), the Guangdong Basic and Applied Research Foundation (No. 2022B1515120072), the Guangzhou Science and Technology Plan Project (No. 2024B01W0079), the Nansha Key R&D Program (No. 2022ZD014). (Corresponding author: Ronghui Zhang.)http://arxiv.org/abs/2501.1015
Adaptive Dual-Channel Event-Triggered Fuzzy Control for Autonomous Underwater Vehicles With Multiple Obstacles Environment
No full textThis article investigates the formation control of autonomous underwater vehicles (AUVs) suffering from unknown sea loads, unmoulded structure, limited communication and multiple static and moving obstacles. Given the challenge, a novel adaptive dual-channel event-triggered control scheme is proposed for formation tracking and obstacles avoidance. To economize the communication resources, the dual-channel event-triggered mechanism is designed in the sensor-to-controller and controller-to-actuator channels respectively. By adopting the approximation of fuzzy systems in the form of one-parameter integrated learning, the uncertainties consisted of the unmoulded structure and unknown sea loads are compressed together to be compensated online, which ensures a lower computational cost. To solve the multiple obstacles, the modified artificial potential field approach is employed, and the derived repulsive potential field can ensure that the multi-AUV formation can avoid obstacles smoothly regardless of static or moving obstacles. It is showed by the Lyapunov stability theorem that the tracking errors are guaranteed to be semi-globally uniformly ultimately bounded. Finally, three simulation examples illustrate the effectiveness and superiority of the proposed scheme.This work was supported in part by the National Natural
Science Foundation of China under Grant 52172350 and Grant 51775565,
in part by Guangdong Basic and Applied Research Foundation under Grant
2021B1515120032 and Grant 2022B1515120072, in part by Guangzhou
Science and Technology Plan Project under Grant 2024B01W0079, in part by
Nansha Key Research and Development Program under Grant 2022ZD014,
and in part by the Science and Technology Planning Project of Guangdong
Province under Grant 2023B1212060029. The Associate Editor for this article
was Z. Li. (Corresponding author: Ronghui Zhang.)https://ieeexplore.ieee.org/abstract/document/1051018
POSTER
No full textIt's important to hit a space-time balance for a real-world algorithm to achieve high performance on modern shared-memory multi-core or many-core systems. However, a large class of dynamic programs with more than dependency achieve optimality either in space or time, but not both. In the literature, the problem is known as the fundamental space-time tradeoff. By exploiting properly on the runtime system, we show that our STAR (Space-Time Adaptive and Reductive) technique can help these dynamic programs to achieve sublinear parallel time bounds while still maintaining work-, space-, and cache-optimality in a processor- and cache-oblivious fashion.</jats:p
DeepText2Go: Improving large-scale protein function prediction with deep semantic text representation
No full text
DeepMHCII: a novel binding core-aware deep interaction model for accurate MHC-II peptide binding affinity prediction
No full textPublisher Copyright: © The Author(s) 2022. Published by Oxford University Press.MOTIVATION: Computationally predicting major histocompatibility complex (MHC)-peptide binding affinity is an important problem in immunological bioinformatics. Recent cutting-edge deep learning-based methods for this problem are unable to achieve satisfactory performance for MHC class II molecules. This is because such methods generate the input by simply concatenating the two given sequences: (the estimated binding core of) a peptide and (the pseudo sequence of) an MHC class II molecule, ignoring biological knowledge behind the interactions of the two molecules. We thus propose a binding core-aware deep learning-based model, DeepMHCII, with a binding interaction convolution layer, which allows to integrate all potential binding cores (in a given peptide) with the MHC pseudo (binding) sequence, through modeling the interaction with multiple convolutional kernels. RESULTS: Extensive empirical experiments with four large-scale datasets demonstrate that DeepMHCII significantly outperformed four state-of-the-art methods under numerous settings, such as 5-fold cross-validation, leave one molecule out, validation with independent testing sets and binding core prediction. All these results and visualization of the predicted binding cores indicate the effectiveness of our model, DeepMHCII, and the importance of properly modeling biological facts in deep learning for high predictive performance and efficient knowledge discovery. AVAILABILITY AND IMPLEMENTATION: DeepMHCII is publicly available at https://github.com/yourh/DeepMHCII. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.Peer reviewe
DeepGraphGO: graph neural network for large-scale, multispecies protein function prediction
No full textPublisher Copyright: © 2021 Oxford University Press. All rights reserved.Motivation: Automated function prediction (AFP) of proteins is a large-scale multi-label classification problem. Two limitations of most network-based methods for AFP are (i) a single model must be trained for each species and (ii) protein sequence information is totally ignored. These limitations cause weaker performance than sequence-based methods. Thus, the challenge is how to develop a powerful network-based method for AFP to overcome these limitations. Results: We propose DeepGraphGO, an end-to-end, multispecies graph neural network-based method for AFP, which makes the most of both protein sequence and high-order protein network information. Our multispecies strategy allows one single model to be trained for all species, indicating a larger number of training samples than existing methods. Extensive experiments with a large-scale dataset show that DeepGraphGO outperforms a number of competing state-of-the-art methods significantly, including DeepGOPlus and three representative network-based methods: GeneMANIA, deepNF and clusDCA. We further confirm the effectiveness of our multispecies strategy and the advantage of DeepGraphGO over so-called difficult proteins. Finally, we integrate DeepGraphGO into the stateof- the-art ensemble method, NetGO, as a component and achieve a further performance improvement. Availability and implementation: https://github.com/yourh/DeepGraphGO.Peer reviewe
A literature review of Artificial Intelligence applications in railway systems
No full textNowadays it is widely accepted that Artificial Intelligence (AI) is significantly influencing a large number of domains, including railways. In this paper, we present a systematic literature review of the current state-of-the-art of AI in railway transport. In particular, we analysed and discussed papers from a holistic railway perspective, covering sub-domains such as maintenance and inspection, planning and management, safety and security, autonomous driving and control, revenue management, transport policy, and passenger mobility. This review makes an initial step towards shaping the role of AI in future railways and provides a summary of the current focuses of AI research connected to rail transport. We reviewed about 139 scientific papers covering the period from 2010 to December 2020. We found that the major research efforts have been put in AI for rail maintenance and inspection, while very limited or no research has been found on AI for rail transport policy and revenue management. The remaining sub-domains received mild to moderate attention. AI applications are promising and tend to act as a game-changer in tackling multiple railway challenges. However, at the moment, AI research in railways is still mostly at its early stages. Future research can be expected towards developing advanced combined AI applications (e.g. with optimization), using AI in decision making, dealing with uncertainty and tackling newly rising cybersecurity challenges
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