A5101465265- Bioinformatics and Genomic Networks
- Machine Learning in Bioinformatics
- Computational Drug Discovery Methods
- Protein Structure and Dynamics
- Interconnection Networks and Systems
- Genomics and Phylogenetic Studies
- Algorithms and Data Compression
- Distributed and Parallel Computing Systems
- Mobile Ad Hoc Networks
- Cancer-related molecular mechanisms research
- Parallel Computing and Optimization Techniques
- Gene expression and cancer classification
- Advanced Optical Network Technologies
- MicroRNA in disease regulation
- RNA and protein synthesis mechanisms
- Microbial Metabolic Engineering and Bioproduction
- IoT and Edge/Fog Computing
- Cloud Computing and Resource Management
- Circular RNAs in diseases
- Energy Efficient Wireless Sensor Networks
- Functional Brain Connectivity Studies
- Data Mining Algorithms and Applications
- Cooperative Communication and Network Coding
- Wireless Communication Networks Research
- Rough Sets and Fuzzy Logic
Central South University
2013-2025
Shenzhen Institutes of Advanced Technology
2021-2025
Shenzhen University
2021-2025
Chinese Academy of Sciences
2016-2025
Shenzhen Technology University
2024-2025
China University of Geosciences
2024-2025
Nanjing Normal University
2025
Yangtze University
2025
Georgia State University
2015-2024
University Town of Shenzhen
2021-2024
In OFDM-based optical networks, multiple subcarriers can be allocated to accommodate various size of traffic demands. By using the multi-carrier modulation technique, for same node-pair overlapping in spectrum domain. Compared traditional wavelength routed networks (WRNs), Spectrum-sliced Elastic Optical Path (SLICE) network has higher efficiency due its finer granularity and frequency-resource saving. this work, first time, we comprehensively study routing allocation (RSA) problem SLICE...
Drug repositioning, which aims to identify new indications for existing drugs, offers a promising alternative reduce the total time and cost of traditional drug development. Many computational strategies repositioning have been proposed, are based on similarities among drugs diseases. Current studies typically use either only drug-related properties (e.g. chemical structures) or disease-related phenotypes) calculate disease similarity, respectively, while not taking into account influence...
Brain tumor segmentation aims to separate the different tissues such as active cells, necrotic core, and edema from normal brain of White Matter (WM), Gray (GM), Cerebrospinal Fluid (CSF). MRI-based studies are attracting more attention in recent years due non-invasive imaging good soft tissue contrast Magnetic Resonance Imaging (MRI) images. With development almost two decades, innovative approaches applying computer-aided techniques for segmenting becoming mature coming closer routine...
Identification of essential proteins is key to understanding the minimal requirements for cellular life and important drug design. The rapid increase available protein-protein interaction (PPI) data has made it possible detect protein essentiality on network level. A series centrality measures have been proposed discover based topology. However, most them tended focus only location single protein, but ignored relevance between interactions essentiality. In this paper, a new measure...
Deep learning provides exciting solutions in many fields, such as image analysis, natural language processing, and expert system, is seen a key method for various future applications. On account of its non-invasive good soft tissue contrast, recent years, Magnetic Resonance Imaging (MRI) has been attracting increasing attention. With the development deep learning, innovative methods have proposed to improve MRI processing analysis performance. The purpose this article provide comprehensive...
Accumulating evidences indicate that long non-coding RNAs (lncRNAs) play pivotal roles in various biological processes. Mutations and dysregulations of lncRNAs are implicated miscellaneous human diseases. Predicting lncRNA–disease associations is beneficial to disease diagnosis as well treatment. Although many computational methods have been developed, precisely identifying associations, especially for novel lncRNAs, remains challenging. In this study, we propose a method (named SIMCLDA)...
The security and privacy preservation issues are prerequisites for vehicular ad hoc networks. Recently, secure enhancing communication schemes (SPECS) was proposed focused on intervehicle communications. SPECS provided a software-based solution to satisfy the requirement gave lower message overhead higher successful rate than previous solutions in verification phase. also presented first group protocol allow vehicles authenticate securely communicate with others of known vehicles....
Identification of essential proteins is always a challenging task since it requires experimental approaches that are time-consuming and laborious. With the advances in high throughput technologies, large number protein-protein interactions available, which have produced unprecedented opportunities for detecting proteins' essentialities from network level. There been series computational proposed predicting based on topologies. However, topology-based centrality measures very sensitive to...
Increasing evidences have demonstrated that long noncoding RNAs (lncRNAs) play important roles in many human diseases. Therefore, predicting novel lncRNA-disease associations would contribute to dissect the complex mechanisms of disease pathogenesis. Some computational methods been developed infer associations. However, most these only based on single data resource.In this paper, we propose a new method predict by integrating multiple biological resources. Then, implement as web server for...
Regions of interest (ROIs) based classification has been widely investigated for analysis brain magnetic resonance imaging (MRI) images to assist the diagnosis Alzheimer's disease (AD) including its early warning and developing stages, e.g., mild cognitive impairment (MCI) MCI converted AD (MCIc) not (MCInc). Since an ROI representation structures is obtained either by pre-definition or adaptive parcellation, corresponding in different brains can be measured. However, due noise small sample...
Essential proteins are vital for an organism's viability under a variety of conditions. There many experimental and computational methods developed to identify essential proteins. Computational prediction based on the global protein-protein interaction (PPI) network is severely restricted because insufficiency PPI data, but fortunately gene expression profiles help make up deficiency. In this work, Pearson correlation coefficient (PCC) used bridge gap between data. Based PCC edge clustering...
A novel coronavirus, called 2019-nCoV, was recently found in Wuhan, Hubei Province of China, and now is spreading across China other parts the world. Although there are some drugs to treat no proper scientific evidence about its activity on virus. It high significance develop a drug that can combat virus effectively save valuable human lives. usually takes much longer time using traditional methods. For it better rely alternative methods such as deep learning disease since 2019-nCoV highly...
Along with the popularity of Internet Things (IoT) techniques several computational paradigms, such as cloud and edge computing, microservice has been viewed a promising architecture in large-scale application design deployment. Due to limited computing ability devices distributed IoT, only small scale data can be used for model training. In addition, most machine-learning-based intrusion detection methods are insufficient when dealing imbalanced dataset under resources. this article, we...
Medical knowledge graphs (MKGs) are the basis for intelligent health care, and they have been in use a variety of medical applications. Thus, understanding research application development MKGs will be crucial future relevant biomedical field. To this end, we offer an in-depth review MKG work. Our begins with examination four types information sources, graph creation methodologies, six major themes development. Furthermore, three popular models reasoning from viewpoint discussed. A...
With the flourish of digital technologies and rapid development 5G beyond networks, Metaverse has become an increasingly hotly discussed topic, which offers users with multiple roles for diversified experience interacting virtual services. How to capture model users' multi-platform or cross-space data/behaviors essential enrich people more realistic immersed in Metaverse-enabled smart applications over networks. In this study, we propose a Personalized Federated Learning Model-Contrastive...
This study explores the potential of Artificial Intelligence (AI) in early screening and prognosis Dry Eye Disease (DED), aiming to enhance accuracy therapeutic approaches for eye-care practitioners. Despite promising opportunities, challenges such as diverse diagnostic evidence, complex etiology, interdisciplinary knowledge integration impede interpretability, reliability, applicability AI-based DED detection methods. The research conducts a comprehensive review datasets, standards, well...
Abstract Deep learning-based multi-omics data integration methods have the capability to reveal mechanisms of cancer development, discover biomarkers and identify pathogenic targets. However, current ignore potential correlations between samples in integrating data. In addition, providing accurate biological explanations still poses significant challenges due complexity deep learning models. Therefore, there is an urgent need for a method explore provide model interpretability. Herein, we...
This paper proposes a blind watermarking algorithm based on the significant difference of wavelet coefficient quantization for copyright protection. Every seven nonoverlap coefficients host image are grouped into block. The largest two in block called this and their is difference. We quantized local maximum by comparing value with average all blocks. so that between watermark bit 0 1 exhibits large energy which can be used extraction. During extraction, an adaptive threshold designed to...
As advances in the technologies of predicting protein interactions, huge data sets portrayed as networks have been available. Identification functional modules from such is crucial for understanding principles cellular organization and functions. However, interaction produced by high-throughput experiments are generally associated with high false positives, which makes it difficult to identify accurately. In this paper, we propose a fast hierarchical clustering algorithm HC-PIN based on...