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2022 IEEE International Conference on bioinformatics and Biomedicine, BIBM 2022

作者： Wang, Ziyan Hong, Chengzhi Liu, Xuan Xiong, Zhankun Liu, Feng Zhang, Wen Wuhan University School of Computer Science Wuhan430072 China Huazhong Agricultural University College of Informatics Wuhan430070 China Huazhong Agricultural University Agricultural Bioinformatics Key Laboratory of Hubei Province Key Laboratory of Smart Farming for Agricultural Animals Ministry of Agriculture Hubei Engineering Technology Research Center of Agricultural Big Data Engineering Research Center of Intelligent Technology for Agriculture Ministry of Education Wuhan430070 China

ISBN: (纸本)9781665468190

Exploring the transcriptional response after employing chemical compounds assists in treating gene-related diseases and understanding biological activity of compounds. Calculating the similarity of drug transcriptional response can help to discover novel compounds that have the similar biological activity to known drugs for treating the same disease. Considering the transcriptional profiles of compounds are limited and harder to get than the structure of compounds, it is worth modeling the structure-transcriptional response similarity relationship. In this paper, we propose a signed graph convolutional network (SGCN)-based method, namely SGCN-DTRS, to predict drug transcriptional response similarity, which is quantitatively measured by Connectivity Map (CMap) scores, from their structures. SGCNDTRS constructs a CMap signed network from compounds and their CMap scores in the training data, which takes compounds as nodes, molecular structural representations of compounds as the attributes of nodes, and similarity relations between compounds as edges. Then SGCN-DTRS learns the CMap compound embeddings to predict CMap scores of pairwise compounds. Extensive experiments verify the superiority of the proposed method against the compared state-of-the-art methods and reveal that the relational information of the CMap data, which is learned from CMap signed network, is important for the CMap score prediction. SGCN-DTRS can not only work for the compounds in the training set but also is applicable to unseen compounds. © 2022 IEEE.

关键词： Graph neural networks

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Meta-analysis of Traditional Chinese Medicine Lianhua Qingwen in the Treatment of Coronavirus Disease 2019

Journal of anesthesia and translational medicine

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Journal of anesthesia and translational medicine 2023年第2期2卷 20-26页

作者： Qian Xu Kaicheng Song Sean P Clifford Maiying Kong Jiapeng Huang Biometrics and Data Science Fosun Pharma Beijing 100026 PR China. Department of Bioinformatics and Biostatistics SPHIS University of Louisville Louisville KY 40202 USA. Department of Anesthesiology Peking Union Medical College Hospital Chinese Academy of Medical Science No.1 Shuaifuyuan Wangfujing Dongcheng District Beijing 100730 PR China. Department of Anesthesiology & Perioperative Medicine University of Louisville 530 South Jackson Street Louisville KY 40202 USA.

The rapid global spread of Coronavirus disease 2019 (COVID-19) has seriously threatened human life and health. Effects of traditional Chinese medicine, Lianhua Qingwen, combined with western medicine remains controversial for treatment of COVID-19. Evidence to support use of Lianhua Qingwen in COVID-19 is lacking. In this study, we systematically reviewed literature on the use of Lianhua Qingwen in COVID-19, and we performed meta-analysis to assess the effect of Lianhua Qingwen in COVID-19 management. We found that when combined with western medicine in the treatment of COVID-19 patients, Lianhua Qingwen may shorten duration of fever, reduce adverse events, decrease rate of conversion to severe disease, and improve symptom recovery and chest radiographic signs of pneumonia.

关键词： COVID-19 Lianhua Qingwen Meta-analysis Traditional medicine

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A Novel Method for Quantitative Evaluation of Anti-Infection Immune Defense Function and its Application

SSRN

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SSRN 2024年

作者： Tan, Zhen-Lin Luo, Tao Wu, Xiao-Jun Lei, Qian Chen, Jie Guo, An-Yuan Department of Laboratory Medicine West China Biomedical Big Data Center West China Hospital Sichuan University Chengdu610041 China Hubei Key Laboratory of Bioinformatics and Molecular-imaging College of Life Science and Technology Huazhong University of Science and Technology Hubei Wuhan430074 China College of Life Sciences University of Chinese Academy of Sciences Beijing101408 China BGI Education Center University of Chinese Academy of Sciences Shenzhen518083 China

ImmuDef is a novel algorithm for precise, quantitative assessment of anti-infection immune defense based on RNA-seq data. Using enrichment scores of immunologic signature gene sets, we defined three feature sets for bacteria, viruses, and fungi infection. Three normalized scores based on modified Manhattan distances were defined as immune scores (IS) to evaluate immunity against these infections. Tested on 3,202 samples across 39 datasets, ImmuDef distinguished well between healthy individuals and patients with AIDS, COVID-19, sepsis, tuberculosis (AUC: 0.71-1.00). It accurately predicted survival and death outcomes in critical sepsis (AUC = 0.89) and severe COVID-19 (AUC = 0.84). In hepatitis B patients, IS was significantly lower in those with active HBV replication and negatively correlated with ALT levels. ImmuDef offers a novel approach for precise measurement of anti-infection immunity, aiding in disease prognosis prediction and individual health status ***: Financial support was provided by the National Natural science Foundation of China (32370717) and the 1.3.5 project for disciplines of excellence from West China Hospital of Sichuan University (ZYYC23007).Declaration of Interest: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this *** Approval: This study was approved by Ethics Committee on Biomedical Research West China Hospital of Sichuan University (Reference: HX-IRB-AF-21-V3.0). © 2024, The Authors. All rights reserved.

关键词： COVID-19

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Comparative analysis of deep learning models for detecting face mask

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Procedia computer science 2023年 216卷 48-56页

作者： M Vickya Ramadhan Kahlil Muchtar Yudha Nurdin Maulisa Oktiana Maya Fitria Novi Maulina Gregorius Natanael Elwirehardja Bens Pardamean Department of Electrical and Computer Engineering Universitas Syiah Kuala Banda Aceh 23111 Indonesia. Telematics Research Center (TRC) Universitas Syiah Kuala Banda Aceh 23111 Indonesia. Microbiology Department School of Medicine Universitas Syiah Kuala Banda Aceh 23111 Indonesia. Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta 11480 Indonesia. Computer Science Department BINUS Graduate Program - Master of Computer Science Program Bina Nusantara University Jakarta 11480 Indonesia.

The spread of Corona Virus Disease 19 (COVID-19) in Indonesia is still relatively high and has not shown a significant decrease. One of the main reasons is due to the lack of supervision on the implementation of health protocols such as wearing masks in daily activities. Recently, state-of-the-art algorithms were introduced to automate face mask detection. To be more specific, the researchers developed various kinds of architectures for the detection of masks based on computer vision methods. This paper aims to evaluate well-known architectures, namely the ResNet50, VGG11, InceptionV3, EfficientNetB4, and YOLO (You Only Look Once) to recommend the best approach in this specific field. By using the MaskedFace-Net dataset, the experimental results showed that the EfficientNetB4 architecture has better accuracy at 95.77% compared to the YOLOv4 architecture of 93.40%, InceptionV3 of 87.30%, YOLOv3 of 86.35%, ResNet50 of 84.41%, VGG11 of 84.38%, and YOLOv2 of 78.75%, respectively. It should be noted that particularly for YOLO, the model was trained using a collection of MaskedFace-Net images that had been pre-processed and labelled for the task. The model was initially able to train faster with pre-trained weights from the COCO dataset thanks to transfer learning, resulting in a robust set of features expected for face mask detection and classification.

关键词： Binary Classification Deep Learning Face mask detection

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Multi-modal Multi-kernel Graph Learning for Autism Prediction and Biomarker Discovery

arXiv

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arXiv 2023年

作者： Liu, Jin Mao, Junbin Lin, Hanhe Kuang, Hulin Pan, Shirui Wu, Xusheng Xie, Shan Liu, Fei Pan, Yi Hunan Provincial Key Lab on Bioinformatics School of Computer Science and Engineering Central South University Changsha410083 China Xinjiang Engineering Research Center of Big Data and Intelligent Software School of software Xinjiang University Wulumuqi830000 China Hunan Province Key Lab on Bioinformatics School of Computer Science and Engineering Central South University Changsha410083 China Faculty of Computer Science and Control Engineering Shenzhen University of Advanced Technology Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen518055 China School of Science and Engineering University of Dundee DundeeDD1 4HN United Kingdom School of Information and Communication Technology Griffith University Gold CoastQLD4215 Australia Shenzhen Health Development Research and Data Management Center Shenzhen518109 China

Graph learning-based multi-modal integration and classification is one of the most challenging tasks for disease prediction. To effectively offset the negative impact among modalities in the process of multi-modal integration and heterogeneous information extractions from graphs, we propose a novel method called Multi-modal Multi-Kernel Graph Learning (MMKGL). To solve the problem of negative impact among modalities, we propose a multi-modal graph embedding module to construct a multi-modal graph. Different from conventional methods that manually construct static graphs for all modalities, each modality generates a separate graph by adaptive learning, where a function graph and a supervision graph are introduced for optimization during the multi-graph fusion embedding process. We then propose a multi-kernel graph learning module to extract heterogeneous information from the multi-modal graph. The information in the multi-modal graph at different levels is aggregated by convolutional kernels with different receptive field sizes, followed by generating a cross-kernel discovery tensor for disease prediction. Our method is evaluated on the benchmark Autism Brain Imaging data Exchange (ABIDE) dataset and outperforms the state-of-the-art methods. In addition, discriminative brain regions associated with autism are identified by our model, providing guidance for the study of autism pathology. The source code will be available at https://***/yutian0315/MMKGL. © 2023, CC BY-NC-ND.

关键词： Graph embeddings

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The Occurrence and Genetic Characteristics of Carbapenemase-Producing Enterobacteriaceae Isolated from Shared Water Bodies for Livestock and Human Utilization in Central Thailand

SSRN

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SSRN 2024年

作者： Songsaeng, Wipawee Kurilung, Alongkorn Prapasarakul, Nuvee Wongsurawat, Thidathip Am-In, Nutthee Lugsomya, Kittitat Sirichokchatchawan, Wandee College of Public Health Science Chulalongkorn University Bangkok Thailand Department of Biomedical Informatics University of Arkansas for Medical Sciences Little RockAR United States Department of Veterinary Microbiology Faculty of Veterinary Science Chulalongkorn University Bangkok Thailand Bangkok Thailand Division of Bioinformatics and Data Management for Research Research Group and Research Network Division Faculty of Medicine Siriraj Hospital Mahidol University Bangkok Thailand Department of Obstetrics Gynecology and Reproduction Faculty of Veterinary Science Chulalongkorn University Bangkok Thailand Veterinary and Life Science Murdoch University MurdochWA6150 Australia

The environmental dissemination of antimicrobial resistant Enterobacteriaceae poses a remarkable threat to public health. This study investigates the environmental presence and dissemination of carbapenemase-producing Enterobacteriaceae (CPE) in 30 important water bodies selected according to the interconnection and utilization by livestock and community people in central Thailand. From all selected samples, 6 isolates (20%) were identified as multi-drug resistant CPE including one E. coli, one K. pneumoniae and four E. roggemkampii carrying blaNDM-5, blaNDM-1 and blaIMI-1 genes respectively. The blaNDM-5 and blaNDM-1 genes were located on plasmids, p0111 and IncC, while blaIMI-1 was located on chromosomes. The plasmids also consisted of components that closely resembled those found in resistance plasmids obtained from clinical and environmental isolates worldwide. Additionally, through plasmid conjugation experiment, carbapenemase genes were transferable with high rate among bacterial species. These findings indicated that water bodies are polluted and there is an urgent need for integrated strategies to monitor and mitigate the spread of antibiotic resistance across human, animal, and environmental health domains in aquatic environments. © 2024, The Authors. All rights reserved.

关键词： Genes

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uniLIVER: a human liver cell atlas for data-driven cellular state mapping

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Journal of Genetics and Genomics 2025年

作者： Wu, Yanhong Fan, Yuhan Miao, Yuxin Li, Yuman Du, Guifang Chen, Zeyu Diao, Jinmei Chen, Yu-Ann Ye, Mingli You, Renke Chen, Amin Chen, Yixin Li, Wenrui Guo, Wenbo Dong, Jiahong Zhang, Xuegong Wang, Yunfang Gu, Jin MOE Key Lab of Bioinformatics BNRIST Bioinformatics Division Department of Automation Tsinghua University Beijing 100084 China Hepato-Pancreato-Biliary Center Beijing Tsinghua Changgung Hospital Tsinghua University Beijing 102218 China Clinical Translational Science Center Beijing Tsinghua Changgung Hospital Tsinghua University Beijing 102218 China Fuzhou Institute of Data Technology Fujian Fuzhou 350207 China Center for Synthetic and Systems Biology School of Life Sciences and School of Medicine Tsinghua University Beijing 100084 China

The liver performs several vital functions such as metabolism, toxin removal, and glucose storage through the coordination of various cell types. With the recent breakthrough of the single-cell/single-nucleus RNA-seq (sc/snRNA-seq) techniques, there is a great opportunity to establish a reference cell map of the liver at single-cell resolution with transcriptome-wise features. In this study, we build a unified liver cell atlas uniLIVER (http://***/database/uniliver) by integrative analysis of a large-scale sc/snRNA-seq data collection of normal human liver with 331,125 cells and 79 samples from 6 datasets. Moreover, we introduce LiverCT, a novel machine learning based method for mapping any query dataset to the liver reference map by introducing the definition of “variant” cellular states analogy to the sequence variants in genomic analysis. Applying LiverCT on liver cancer datasets, we find that the “deviated” states of T cells are highly correlated with the stress pathway activities in hepatocellular carcinoma, and the enrichments of tumor cells with the hepatocyte-cholangiocyte “intermediate” states significantly indicate poor prognosis. Besides, we find that the tumor cells of different patients have different zonation tendencies and this zonation tendency is also significantly associated with the prognosis. This reference atlas mapping framework can also be extended to any other tissues. © 2025 Institute of Genetics and Developmental Biology, Chinese Academy of sciences, and Genetics Society of China

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OUCopula: Bi-Channel Multi-Label Copula-Enhanced Adapter-Based CNN for Myopia Screening Based on OU-UWF Images

arXiv

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arXiv 2024年

作者： Li, Yang Huang, Qiuyi Zhong, Chong Yang, Danjuan Li, Meiyan Welsh, A.H. Liu, Aiyi Fu, Bo Liu, Catherine C. Zhou, Xingtao Department of Applied Mathematics The Hong Kong Polytechnic University Hong Kong School of Data Science Fudan University China College of Business and Economics Australian National University Australia Eye Institute Department of Ophthalmology Eye & ENT Hospital Fudan University China Biostatistis and Bioinformatics Branch Eunice Kennedy Shriver National lnstitute of Child Health and Human Developnent United States

Myopia screening using cutting-edge ultra-widefield (UWF) fundus imaging is potentially significant for ophthalmic outcomes. Current multidisciplinary research between ophthalmology and deep learning (DL) concentrates primarily on disease classification and diagnosis using single-eye images, largely ignoring joint modeling and prediction for Oculus Uterque (OU, both eyes). Inspired by the complex relationships between OU and the high correlation between the (continuous) outcome labels (Spherical Equivalent and Axial Length), we propose a framework of copula-enhanced adapter convolutional neural network (CNN) learning with OU UWF fundus images (OUCopula) for joint prediction of multiple clinical scores. We design a novel bi-channel multi-label CNN which can (1) take bi-channel image inputs subject to both high correlation and heterogeneity (by sharing the same backbone network and employing adapters to parameterize the channel-wise discrepancy), and (2) incorporate correlation information between continuous output labels (using a copula). Solid experiments show that OUCopula achieves satisfactory performance in myopia score prediction compared to backbone models. Moreover, OUCopula can far exceed the performance of models constructed for single-eye inputs. Importantly, our study also hints at the potential extension of the bi-channel model to a multi-channel paradigm and the generalizability of OUCopula across various backbone CNNs. Copyright © 2024, The Authors. All rights reserved.

关键词： Ophthalmology

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OU-CoViT: Copula-Enhanced Bi-Channel Multi-Task Vision Transformers with Dual Adaptation for OU-UWF Images

arXiv

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arXiv 2024年

作者： Li, Yang Deng, Jianing Zhong, Chong Yang, Danjuan Li, Meiyan Welsh, A.H. Liu, Aiyi Zhou, Xingtao Liu, Catherine C. Fu, Bo School of Data Science Fudan University China Department of Applied Mathematics The Hong Kong Polytechnic University Hong Kong Eye Institute Department of Ophthalmology Eye & ENT Hospital Fudan University China College of Business and Economics Australian National University Australia Biostatistics and Bioinformatics Branch Eunice Kennedy Shriver National Institute of Child Health and Human Development United States

Myopia screening using cutting-edge ultra-widefield (UWF) fundus imaging and joint modeling of multiple discrete and continuous clinical scores presents a promising new paradigm for multi-task problems in Ophthalmology. The bi-channel framework that arises from the Ophthalmic phenomenon of "interocular asymmetries" of both eyes (OU) calls for new employment on the SOTA transformer-based models. However, the application of copula models for multiple mixed discrete-continuous labels on deep learning (DL) is challenging. Moreover, the application of advanced large transformer-based models to small medical datasets is challenging due to overfitting and computational resource constraints. To resolve these challenges, we propose OU-CoViT: a novel Copula-Enhanced Bi-Channel Multi-Task Vision Transformers with Dual Adaptation for OU-UWF images, which can i) incorporate conditional correlation information across multiple discrete and continuous labels within a deep learning framework (by deriving the closed form of a novel Copula Loss);ii) take OU inputs subject to both high correlation and interocular asymmetries using a bi-channel model with dual adaptation;and iii) enable the adaptation of large vision transformer (ViT) models to small medical datasets. Solid experiments demonstrate that OU-CoViT significantly improves prediction performance compared to single-channel baseline models with empirical loss. Furthermore, the novel architecture of OU-CoViT allows generalizability and extensions of our dual adaptation and Copula Loss to various ViT variants and large DL models on small medical datasets. Our approach opens up new possibilities for joint modeling of heterogeneous multichannel input and mixed discrete-continuous clinical scores in medical practices and has the potential to advance AI-assisted clinical decision-making in various medical domains beyond Ophthalmology. © 2024, CC BY.

关键词： Ophthalmology

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'EMDB-the Electron Microscopy data Bank' (vol 1, gkad1019, 2023)

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NUCLEIC ACIDS RESEARCH 2023年第D1期52卷 D1701-D1701页

作者： Turner, Jack Abbott, Sanja Fonseca, Neli Pye, Ryan Carrijo, Lucas Duraisamy, Amudha Kumari Salih, Osman Wang, Zhe Kleywegt, Gerard J. Morris, Kyle L. Patwardhan, Ardan Cellular Structure and 3D Bioimaging European Molecular Biology Laboratory (EMBL) European Bioinformatics Institute (EBI) Wellcome Genome Campus Hinxton Cambridgeshire CB10 1SD United Kingdom Research Collaboratory for Structural Bioinformatics Protein Data Bank Institute for Quantitative Biomedicine Rutgers The State University of New Jersey Piscataway 08854 NJ United States Department of Chemistry and Chemical Biology Rutgers The State University of New Jersey Piscataway 08854 NJ United States Rutgers Cancer Institute of New Jersey Rutgers The State University of New Jersey New Brunswick 08901 NJ United States Research Collaboratory for Structural Bioinformatics Protein Data Bank San Diego Supercomputer Center University of California San Diego La Jolla 92093 CA United States Protein Data Bank in Europe European Molecular Biology Laboratory (EMBL) European Bioinformatics Institute (EBI) Wellcome Genome Campus Hinxton Cambridgeshire CB10 1SD United Kingdom CEITEC Central European Institute of Technology Masaryk University Kamenice 5 Brno 62500 Czech Republic Molecular Biophysics Unit Indian Institute of Science Bangalore India Protein Data Bank Japan Protein Research Foundation Minoh Osaka 562-8686 Japan Protein Data Bank Japan Institute for Protein Research Osaka University Suita Osaka 565-0871 Japan Biological Magnetic Resonance Data Bank Department of Molecular Biology and Biophysics UConn Health 263 Farmington Ave. Farmington 06030-3305 CT United States National Facility for Protein Science Shanghai Advanced Research Institute Chinese Academy of Sciences Shanghai 201210 China School of Life Science and Technology Shanghai Tech University Shanghai 201210 China

The Electron Microscopy data Bank (EMDB) is the global public archive of three-dimensional electron microscopy (3DEM) maps of biological specimens derived from transmission electron microscopy experiments. As of 2021, EMDB is managed by the Worldwide Protein data Bank consortium (wwPDB; ***) as a wwPDB Core Archive, and the EMDB team is a core member of the consortium. Today, EMDB houses over 30 000 entries with maps containing macromolecules, complexes, viruses, organelles and cells. Herein, we provide an overview of the rapidly growing EMDB archive, including its current holdings, recent updates, and future plans.

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