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Rapid detection of traditional Chinese medicine with neuraminidase inhibitory activities based on high-throughput and virtual screening strategy

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Phytomedicine Plus 2023年第1期3卷 100417-100417页

作者： Huang, Yuheng Wang, Zhen Fang, Senbiao Tan, Ying Chen, Jiajun Xie, Jiaming Tu, Zhengchao Huang, Weihuan Li, Ning Tian, Haiyan Institute of Traditional Chinese Medicine and Natural Products College of Pharmacy Jinan University Guangzhou 510632 China Department of Scientific Research KMHD GeneTech Co.Ltd. Shenzhen 518000 China Key Laboratory of Regenerative Medicine Ministry of Education Jinan University Guangzhou 510632 China Hunan Provincial Key Lab on Bioinformatics School of Computer Science and Engineering Central South University Changsha 410083 China

Background: Oseltamivir, a neuraminidase inhibitor (NAI), is the primary and first-line anti-influenza drug. In recent years, more and more oseltamivir resistant strains appeared frequently. Purpose: To identify anti-influenza candidates to overcome oseltamivir resistance from traditional Chinese medicine. Methods: High-throughput screening platform was used to screen candidates with oseltamivir sensitive and resistant neuraminidase (NA) inhibitory activities from traditional Chinese medicine (TCM) and in validation of potential active components in vitro. Molecular docking is used for virtual screening of TCM compound libraries and analysis of the molecular mechanism of active compounds. Results: Six out of 188 TCM formula granules were screened out with good inhibitory activities in oseltamivir sensitive (H5N1) and resistant NA (H274Y mutant). A compound library containing 679 candidates in the above six plants were docked into binding pockets of H5N1 NA and its H274Y mutant. The results indicated that diverse structural types, including flavonoids glycosides, alkaloids, caffeoylquinic acid derivatives, etc. showed good docking scores on the two NAs. Furthermore, 10 representative compounds tested in vitro showed inhibitory activities in both oseltamivir sensitive and resistant NA. The docking simulations revealed that natural TCM molecules bind with NA in an absolute different mode compared with oseltamivir, which occupy the extra cavities adjacent to the active site of NA, thus might make them non-susceptible to be influenced by mutation around the active sites. Conclusions: The combination of high-throughput screening and molecular docking can efficiently screen out candidates that have the potential to overcome oseltamivir resistance from TCM. © 2023

关键词： High throughput screening NA inhibitory activity Traditional chinese medicine Virtual screening

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Research on Prediction Methods of Prevalence Perception under Information Exposure

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computers, Materials & Continua 2020年第12期65卷 2263-2275页

作者： Weijin Jiang Fang Ye Wei Liu Xiaoliang Liu Guo Liang Yuhui Xu Lina Tan School of Computer and Information Engineering Hunan University of Technology and BusinessChangsha410205China Key Laboratory of Hunan Province for New Retail Virtual Reality Technology Hunan University of Technology and BusinessChangsha410205China Institute of Big Data and Internet Innovation Mobile E-Business Collaborative Innovation Center of Hunan ProvinceHunan University of Technology and BusinessChangsha410205China School of Computer Science and Technology Wuhan University of TechnologyWuhan430073China School of Bioinformatics University of MinnesotaTwin CitiesUSA

With the rapid development of information technology,the explosive growth of data information has become a common challenge and *** network services represented by WeChat,Weibo and Twitter,drive a large amount of information due to the continuous spread,evolution and emergence of users through these *** dynamic modeling,analysis,and network information prediction,has very important research and application value,and plays a very important role in the discovery of popular events,personalized information recommendation,and early warning of bad *** these reasons,this paper proposes an adaptive prediction algorithm for network information transmission.A popularity prediction algorithm is designed to control the transmission trend based on the gray Verhulst model to analyze the law of development and capture popular *** simulations show that the proposed perceptual prediction model in this paper has a better fitting effect than the existing models.

关键词： Social network situational awareness adaptive prediction prediction of popularity

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MiRNA-Disease Associations Prediction Based on Negative Sample Selection and Multi-layer Perceptron 1

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16th International Symposium on bioinformatics Research and Applications, ISBRA 2020

作者： Li, Na Duan, Guihua Yan, Cheng Wu, Fang-Xiang Wang, Jianxin Hunan Provincial Key Lab on Bioinformatics School of Computer Science and Engineering Central South University Changsha410083 China School of Computer and Information Qiannan Normal University for Nationalities DuyunGuizhou558000 China Division of Biomedical Engineering and Department of Mechanical Engineering University of Saskatchewan SaskatoonSKS7N5A9 Canada

ISBN: (数字)9783030578213

ISBN: (纸本)9783030578206

MicroRNAs (miRNAs) are a class of non-coding RNAs of approximately 22 nucleotides. Cumulative evidence from biological experiments has confirmed that miRNAs play a key role in many complex human diseases. Therefore, the accurate identification of potential associations between miRNAs and diseases is beneficial to understanding the mechanisms of diseases, developing drugs and treating complex diseases. We propose a new method to predict miRNA-disease associations based on a negative sample selection strategy and multi-layer perceptron (called NMLPMDA). For obtaining more similarity information, NMLPMDA integrates the miRNA functional similarity and the Gaussian interaction profile (GIP) kernel similarity of miRNAs as the final miRNA similarity, and integrates the disease semantic similarity and the GIP kernel similarity of diseases as the final disease similarity. In particular, we propose a negative sample selection strategy based on common gene information to select more reliable negative samples from unknown miRNA-disease associations. The 5-fold cross validation is used to evaluate the performance of NMLPMDA and other competing methods. On four datasets (HMDD2.0-Yan, HMDD2.0-Lan, HMDD2.0-You, HMDD3.0), the AUC values of NMLPMDA are 0.9278, 0.9206, 0.9301 and 0.9350, respectively. In addition, we also illustrate the prediction ability of NMLPMDA in Lymphoma. As a result, 28 of the top 30 miRNAs associated with the disease have been validated experimentally in dbDEMC and previous studies, respectively. These experimental results indicate that NMLPMDA is a reliable model for predicting associations between miRNAs and diseases. © 2020, Springer Nature Switzerland AG.

关键词： Forecasting

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Prediction of Egfr Mutation Status in Lung Adenocarcinoma Using Multi-Source Feature Representations

Prediction of Egfr Mutation Status in Lung Adenocarcinoma Us...

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IEEE International Conference on Acoustics, Speech and Signal Processing

作者： Jianhong Cheng Jin Liu Meilin Jiang Hailin Yue Lin Wu Jianxin Wang Hunan Provincial Key Lab on Bioinformatics School of Computer Science and Engineering Central South University Changsha China Institute of Guizhou Aerospace Measuring and Testing Technology Guiyang China Hunan Cancer Hospital the Affiliated Cancer Hospital of Xiangya School of Medicine Central South University Changsha China

Epidermal growth factor receptor (EGFR) genotyping is essential to treatment guidelines for the use of tyrosine kinase inhibitors in lung adenocarcinoma. However, accurate and noninvasive methods to detect the EGFR gene are ongoing challenges. In this study, we propose a hybrid framework, namely HC-DLR, to noninvasively predict EGFR mutation status by fusing multi-source features including low-level handcrafted radiomics (HCR) features, high-level deep learning-based radiomics (DLR) features, and demographics features. The HCR features first are selected from massive handcrafted features extracted from CT images. The DLR features are also extracted from CT images using the pre-trained 3D DenseNet. Then, multi-source feature representations are refined and fused to build an HC-DLR model for improving the predictive performance of EGFR mutations. The proposed method is evaluated on a newly collected dataset with 670 patients. Experimental results show that the HC-DLR model achieves an encouraging predictive performance with an AUC of 0.76, an accuracy of 72.47%, and an F1-score of 71.35%, which may have potential clinical value for predicting EGFR mutations in lung adenocarcinoma.

关键词： Three-dimensional displays Inhibitors Computed tomography Lung Predictive models Signal processing Feature extraction

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On Fixed-Order Book Thickness Parameterized by the Pathwidth of the Vertex Ordering 14th

On Fixed-Order Book Thickness Parameterized by the Pathwidth...

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14th International Conference on Algorithmic Aspects in Information and Management, AAIM 2020

作者： Liu, Yunlong Chen, Jie Huang, Jingui Wang, Jianxin Hunan Provincial Key Laboratory of Intelligent Computing and Language Information Processing Hunan Normal University Changsha410081 China School of Computer Science and Engineering Central South University Changsha410083 China

ISBN: (纸本)9783030576011

Given a graph (Formula Presented) and a fixed linear order (Formula Presented) of V, the problem fixed-order book thickness asks whether there is a page assignment (Formula Presented) such that (Formula Presented) is a k-page book embedding of G. Recently, Bhore et al.(GD2019) presented an algorithm parameterized by the pathwidth of the vertex ordering (denoted by (Formula Presented)). In this paper, we first re-analyze the running time for Bhore et al.’s algorithm, and prove a bound of (Formula Presented) improving on Bhore et al.’s bound of (Formula Presented). We further show that this parameterized problem does not admit a polynomial kernel unless NP (Formula Presented) coNP/poly. Finally, we show that the general fixed-order book thickness problem, in which a budget of at most c crossings over all pages was given, admits an algorithm running in time (Formula Presented). © 2020, Springer Nature Switzerland AG.

关键词： Budget control

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Local Adaptive U-net for Medical Image Segmentation

Local Adaptive U-net for Medical Image Segmentation

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IEEE International Conference on bioinformatics and Biomedicine (BIBM)

作者： Ning Liu Liangliang Liu Jianxin Wang Hunan Provincial Key Lab on Bioinformatics School of Computer Science and Engineering Central South University Changsha China Pingdingshan University Pingdingshan China

ISBN: (数字)9781728162157

ISBN: (纸本)9781728162164

Medical image segmentation is the primary measure of medical image analysis. With the development of deep learning, U-net based approaches have presented for different medical image segmentation tasks. However, the pooling and the simple convolution operation for deep feature maps in the U-shaped network would lead to the coarse segmentation result. In this paper, we design a local adaptive U-net (LA U-net) for medical image segmentation. There are two major modules: the Local Adaptive Module (LAM) and Multi-scale Convolution Module (MCM) in the network. The LAM get more feature maps from each down-sampling process. The MCM capture more global information for the encoding path. To validate the proposed network's performance, we verify it on two datasets: DRIVE dataset, and ISIC 2018 dataset; the results show that LA U-net achieves superior performance on two datasets.

关键词： Image segmentation Biomedical imaging Convolution Feature extraction Task analysis Lesions Sensitivity

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Joint Learning of Primary and Secondary labels based on Multi-scale Representation for Alzheimer’s Disease Diagnosis

Joint Learning of Primary and Secondary Labels based on Mult...

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IEEE International Conference on bioinformatics and Biomedicine (BIBM)

作者： Hong-Dong Li Rui Guo Junjian Li Jianxin Wang Yi Pan Jin Liu Hunan Provincial Key Lab on Bioinformatics School of Computer Science and Engineering Central South University Changsha China Georgia State University Atlanta USA

ISBN: (数字)9781728162157

ISBN: (纸本)9781728162164

The cause of Alzheimer's disease (AD) is insufficient to understand so far, and its diagnosis is challenging in clinical practice. Recently, the convolutional neural network (CNN) model has shown impressive performance in medical image analysis. Combining CNN with magnetic resonance imaging (MRI) image has excellent potential for AD diagnosis. However, it is still a challenging task. To address the challenge, we propose a joint learning method based on multi-scale representation (JL-MSR). The multi-scale representation is proposed to obtain more feature maps by the multi-scale atrous convolutions. Furthermore, in order to use the intrinsic relationship between diagnostic results and clinical scores, we propose a joint learning strategy using the diagnosis result as the primary label and the Mini-Mental State Examination (MMSE) score as the secondary label to joint training. The proposed method is evaluated on a dataset of 417 subjects (including 188 AD and 229 health controls (HC)) from the Alzheimer's Disease Neuroimaging Initiative (ADNI). The experimental results show that our proposed method achieves an accuracy of 88.1% and an area under the receiver operating characteristic (ROC) curve (AUC) value of 0.942 for AD diagnosis, respectively. Compared with a state-of-the-art method in AD diagnosis, our proposed method performs better, and has potential in clinical diagnosis.

关键词： Magnetic resonance imaging Convolution Diseases Three-dimensional displays Clinical diagnosis Alzheimer's disease Tools

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Protein function prediction using GO similarity-based heterogeneous network propagation

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Scientific reports 2025年第1期15卷 19131页

作者： Sai Hu Bihai Zhao School of Mathematics Changsha University Changsha 410022 Hunan China. School of Computer Science and Engineering Changsha University Changsha 410022 Hunan China. bihaizhao@***. Hunan Provincial Key Laboratory of Industrial Internet Technology and Security Changsha University Changsha 410022 Hunan China. bihaizhao@***.

Protein function prediction is a fundamental cornerstone in bioinformatics, providing critical insights into biological processes and disease mechanisms. Despite significant advances, challenges persist due to data sparsity and functional ambiguity. We introduce GOHPro (GO Similarity-based Heterogeneous Network Propagation), a novel method that constructs a heterogeneous network by integrating protein functional similarity (derived from domain profiles and modular complexes) with GO semantic relationships. This method applies a network propagation algorithm to prioritize annotations based on multi-omics context. When evaluated on yeast and human datasets, GOHPro outperformed six state-of-the-art methods. Specifically, it achieved F improvements ranging from 6.8 to 47.5% over methods like exp2GO across the Biological Process (BP), Molecular Function (MF), and Cellular Component (CC) ontologies in both yeast and human species. Rigorous case studies on proteins with shared domains, such as AAA + ATPases, demonstrated GOHPro's ability to resolve functional ambiguity by leveraging contextual interactions and modular complexes. Further validation on the CAFA3 benchmark confirmed its generalizability, with F gains exceeding 62% compared to baseline approaches in human species. Our analysis revealed that homology and network connectivity critically influence prediction robustness, with the modular similarity network compensating for evolutionary gaps in dark proteins. The framework's extensibility to de novo structural predictions highlights its potential to bridge the annotation gap in uncharacterized proteomes.

关键词： Heterogeneous network Network propagation Protein function

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A novel approach based on deep residual learning to predict drug’s anatomical therapeutic chemical code

A novel approach based on deep residual learning to predict ...

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IEEE International Conference on bioinformatics and Biomedicine (BIBM)

作者： Haochen Zhao Peng Ni Cheng Yan Yaohang Li Jianxin Wang School of Computer Science and Engineering Central South University Changsha China Old Dominion University Norfolk Virginia USA Hunan Provincial Key Lab on Bioinformatics School of Computer Science and Engineering Central South University Changsha China

ISBN: (数字)9781728162157

ISBN: (纸本)9781728162164

Correctly identifying the potential Anatomical Therapeutic Chemical (ATC) codes for drugs can accelerate drug development and reduce the cost of experiments. However, most of the existing methods only analyze the first-level ATC code of drugs and lack of the ability to learn basic features from sparsely known drug-ATC code associations. In this paper, we propose a novel method based on deep residual network framework, named RNPredATC, to predict potential drug-ATC code associations by integrating drug structure similarity, ATC sematic similarity, and known drug-ATC code associations. RNPredATC can extract dense feature vectors from sparsely known drug-ATC code associations and reduce the impact from degradation problem, such as gradient vanishing or gradient explosion of deep network. The experimental results show that RNPredATC achieves better performances.

关键词： Drugs Chemicals Training computer science Semantics Residual neural networks Databases

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XMAP: Cross-population fine-mapping by leveraging genetic diversity and accounting for confounding bias

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Nature Communications 2023年第1期14卷 6870页

作者： Cai, Mingxuan Wang, Zhiwei Xiao, Jiashun Hu, Xianghong Chen, Gang Yang, Can Department of Biostatistics City University of Hong Kong Hong Kong Guangzhou HKUST Fok Ying Tung Research Institute Guangzhou 511458 China Department of Mathematics The Hong Kong University of Science and Technology Hong Kong Shenzhen Research Institute of Big Data Shenzhen 518172 China Hunan Provincial Key Lab on Bioinformatics School of Computer Science and Engineering Central South University Changsha 410083 China WeGene Shenzhen Zaozhidao Technology Co. Ltd Shenzhen 518040 China Graduate Affairs Faculty of Medicine Chulalongkorn University Bangkok 10330 Thailand

Fine-mapping prioritizes risk variants identified by genome-wide association studies (GWASs), serving as a critical step to uncover biological mechanisms underlying complex traits. However, several major challenges still remain for existing fine-mapping methods. First, the strong linkage disequilibrium among variants can limit the statistical power and resolution of fine-mapping. Second, it is computationally expensive to simultaneously search for multiple causal variants. Third, the confounding bias hidden in GWAS summary statistics can produce spurious signals. To address these challenges, we develop a statistical method for cross-population fine-mapping (XMAP) by leveraging genetic diversity and accounting for confounding bias. By using cross-population GWAS summary statistics from global biobanks and genomic consortia, we show that XMAP can achieve greater statistical power, better control of false positive rate, and substantially higher computational efficiency for identifying multiple causal signals, compared to existing methods. Importantly, we show that the output of XMAP can be integrated with single-cell datasets, which greatly improves the interpretation of putative causal variants in their cellular context at single-cell resolution. © 2023, The Author(s).

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