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NetBCE: An Interpretable Deep Neural Network for Accurate Prediction of Linear B-cell Epitopes

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Genomics, Proteomics & Bioinformatics 2022年第5期20卷 1002-1012页

作者： Haodong Xu Zhongming Zhao Center for Precision Health School of Biomedical InformaticsThe University of Texas Health Science Center at HoustonHoustonTX 77030USA Human genetics Center School of Public HealthThe University of Texas Health Science Center at HoustonHoustonTX 77030USA The University of Texas MD Anderson Cancer Center UTHealth Houston Graduate School of biomedical Sciences HoustonTX 77030USA department of biomedical informatics Vanderbilt University Medical CenterNashvilleTN 37203USA

Identification of B-cell epitopes(BCEs)plays an essential role in the development of peptide vaccines and immuno-diagnostic reagents,as well as antibody design and *** this work,we generated a large benchmark dataset comprising 124,879 experimentally supported linear epitope-containing regions in 3567 protein clusters from over 1.3 million B cell *** of this curated dataset showed large pathogen diversity covering 176 different *** accuracy in linear BCE prediction was found to strongly vary with different features,while all sequencederived and structural features were *** search more efficient and interpretive feature representations,a ten-layer deep learning framework for linear BCE prediction,namely Net BCE,was *** BCE achieved high accuracy and robust performance with the average area under the curve(AUC)value of 0.8455 in five-fold cross-validation through automatically learning the informative classification *** BCE substantially outperformed the conventional machine learning algorithms and other tools,with more than 22.06%improvement of AUC value compared to other tools using an independent *** investigating the output of important network modules in Net BCE,epitopes and non-epitopes tended to be presented in distinct regions with efficient feature representation along the network layer *** Net BCE is freely available at https://***/bsml320/Net BCE.

关键词： prediction network value

Angiogenic markers endoglin and vascular endothelial growth factor in gastroenteropancreatic neuroendocrine tumors

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World Journal of Gastroenterology 2011年第2期17卷 219-225页

作者： Patricia Kuiper Lukas JAC Hawinkels Eveline SM de Jonge-Muller Izk Biemond Cornelis BHW Lamers Hein W Verspaget department of Gastroenterology and Hepatology Leiden University Medical Centre department of Molecular Cell Biology and Centre for biomedical genetics Leiden University Medical Centre

AIM:To investigate the expression and potential prognostic role of vascular endothelial growth factor(VEGF) and endoglin in gastroenteropancreatic neuroendocrine tumors(GEP-NETs) . METHODS:Microvessel density(MVD) in GEP-NETs was evaluated using endoglin and CD31 *** addition,tissue levels of endoglin and VEGF were determined in homogenates by ELISA. RESULTS:Endoglin was highly expressed on tumor endothelial ***31 MVD in GEP-NETs was significantly higher compared to endoglin MVD(P<0.01) .Two-tofour-fold higher tissue levels of endoglin and VEGF were seen in tumors compared to associated normal tissue. This increased endoglin tissue expression in tumors was significantly related to tumor size(P<0.01) ,presence of metastases(P=0.04) ,and a more advanced tumor stage(P=0.02) ,whereas expression of VEGF was not. CONCLUSION:We suggest that endoglin is a potential marker to indicate and predict metastases,which might be useful in the post-resection therapeutic approach of patients with GEP-NETs.

关键词： Neuroendocrine tumor Carcinoid tumor Angiogenesis factors Endoglin Vascular endothelial growth factor

HEDC++:An Extended Histogram Estimator for Data in the Cloud

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Journal of Computer Science & Technology 2013年第6期28卷 973-988页

作者：史英杰孟小峰 Fusheng Wang 干艳桃 School of InformationRenmin University of China CCF ACM IEEE School of Information Renmin University of China department of biomedical informatics Emory University department of Mathematics and Computer Science Emory University

With increasing popularity of cloud-based data management, improving the performance of queries in the cloud is an urgent issue to solve. Summary of data distribution and statistical information has been commonly used in traditional databases to support query optimization, and histograms are of particular interest. Naturally, histograms could be used to support query optimization and efficient utilization of computing resources in the cloud. Histograms could provide helpful reference information for generating optimal query plans, and generate basic statistics useful for guaranteeing the load balance of query processing in the cloud. Since it is too expensive to construct an exact histogram on massive data, building an approximate histogram is a more feasible solution. This problem, however, is challenging to solve in the cloud environment because of the special data organization and processing mode in the cloud. In this paper, we present HEDC＋＋, an extended histogram estimator for data in the cloud, which provides efficient approximation approaches for both equi-width and equi-depth histograms. We design the histogram estimate workflow based on an extended MapReduce framework, and propose novel sampling mechanisms to leverage the sampling efficiency and estimate accuracy. We experimentally validate our techniques on Hadoop and the results demonstrate that HEDC＋＋ can provide promising histogram estimate for massive data in the cloud.

关键词： histogram estimate sampling cloud computing MapReduce

NORMA:The Network Makeup Artist—A Web Tool for Network Annotation Visualization

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Genomics, Proteomics & Bioinformatics 2022年第3期20卷 578-586页

作者： Mikaela Koutrouli Evangelos Karatzas Katerina Papanikolopoulou Georgios A.Pavlopoulos Institute for Fundamental biomedical Research BSRC“Alexander Fleming”Vari 16672Greece department of informatics and Telecommunications University of AthensAthens 15703Greece

The Network Makeup Artist(NORMA) is a web tool for interactive network annotation visualization and topological analysis, able to handle multiple networks and annotations simultaneously. Precalculated annotations(e.g., Gene Ontology, Pathway enrichment, community detection,or clustering results) can be uploaded and visualized in a network, either as colored pie-chart nodes or as color-filled areas in a 2D/3D Venn-diagram-like style. In the case where no annotation exists,algorithms for automated community detection are offered. Users can adjust the network views using standard layout algorithms or allow NORMA to slightly modify them for visually better group separation. Once a network view is set, users can interactively select and highlight any group of interest in order to generate publication-ready figures. Briefy, with NORMA, users can encode three types of information simultaneously. These are 1) the network, 2) the communities or annotations of interest, and 3) node categories or expression values. Finally, NORMA offers basic topological analysis and direct topological comparison across any of the selected networks. NORMA service is available at http://***, whereas the code is available at https://***/Pavlopoulos Lab/NORMA.

关键词： Network annotation Visualization Topological analysis Community detection Functional enrichment visualization

Two-color emissive AIEgens with anti-Kasha property for dual-organelle imaging and phototherapy

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Science China Chemistry 2024年第5期67卷 1740-1752页

作者： Pu Chen Guogang Shan Qingli Nie Yuting Yan Pengfei Zhang Zujin Zhao Hai-Tao Feng Ben Zhong Tang AIE Research Center Shaanxi Key Laboratory of PhytochemistryCollege of Chemistry and Chemical EngineeringBaoji University of Arts and SciencesBaoji 721013China Institute of Functional Material Chemistry and National&Local United Engineering Lab for Power Battery Faculty of ChemistryNortheast Normal UniversityChangchun 130024China College of Chemistry&Materials Science Northwest UniversityXi’an 710127China Guangdong Key Laboratory of Nanomedicine CAS-HK Joint Lab of BiomaterialsCAS Key Laboratory of Biomedical Imaging Science and SystemShenzhen Engineering Laboratory of Nanomedicine and NanoformulationsCAS Key Lab for Health InformaticsShenzhen Institutes of Advanced TechnologyChinese Academy of SciencesShenzhen 518055China State Key Laboratory of Luminescent Materials and Devices Key Laboratory of Luminescence from Molecular Aggregates of Guangdong ProvinceSouth China University of TechnologyGuangzhou 510640China School of Science and Engineering Shenzhen Institute of Aggregate Science and TechnologyThe Chinese University of Hong KongShenzhen(CUHK-Shenzhen)Shenzhen 518172China

Despite the rapid development of probes for targeting single organelle, the construction of robust dual-organelle targeting probes with multicolor emission was rarely reported. Herein, two dual-emissive aggregation-induced emission luminogens(AIEgens)with donor-π-acceptor structures were designed and synthesized, namely QT-1 and QF-2. Both two AIEgens exhibited excitation wavelength-dependence defying the Kasha's rule, and could stain lipid droplets(LDs) and mitochondria in blue and red fluorescence, respectively. Moreover, thanks to the near-infrared emission and abundant reactive oxygen species(ROS) generation efficiency of QT-1, it was chosen as a photodynamic therapy agent to selectively kill cancer cells from normal cells. Upon light irradiation, an obvious decrease of mitochondrial membrane potential(MMP) and serious change of mitochondrial shape in cells were observed, which corresponded to the efficient inhibition of tumor growth in vivo. This work afforded a promising strategy for the construction of multicolor emission by tuning anti-Kasha behaviors and expanding their application in dualorganelle targeting-based phototheranostics.

关键词： aggregation-induced emission anti-Kasha luminescence fluorescent probe bioimaging photodynamic therapy

A Cross-Domain Ontology Semantic Representation Based on NCBI-BlueBERT Embedding

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Chinese Journal of Electronics 2022年第5期31卷 860-869页

作者： ZHAO Lingling WANG Junjie WANG Chunyu GUO Maozu Faculty of Computing Harbin Institute of Technology department of Medical informatics School of Biomedical Engineering and InformaticsNanjing Medical University Beijing Key Laboratory of Intelligent Processing for Building Big Data School of Electrical and Information Engineering Beijing University of Civil Engineering and Architecture

A common but critical task in biological ontologies data analysis is to compare the difference between ontologies. There have been numerous ontologybased semantic-similarity measures proposed in specific ontology domain, but it still remains a challenge for crossdomain ontologies comparison. An ontology contains the scientific natural language description for the corresponding biological aspect. Therefore, we develop a new method based on natural language processing(NLP) representation model bidirectional encoder representations from transformers(BERT) for cross-domain semantic representation of biological ontologies. This article uses the BERT model to represent the word-level of the ontologies as a set of vectors, facilitating the semantic analysis or comparing the biomedical entities named in an ontology or associated with ontology terms. We evaluated the ability of our method in two experiments: calculating similarities of pair-wise disease ontology and human phenotype ontology terms and predicting the pair-wise of proteins interaction. The experimental results demonstrated the comparative performance. This gives promise to the development of NLP methods in biological data analysis.

关键词： Ontology Semantic representation Semantic similarity Protein-protein interaction

GTF2H4 regulates partial EndMT via NF-κB activation through NCOA3 phosphorylation in ischemic diseases

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The Innovation 2024年第2期5卷 27-41页

作者： Zheyan Fang Gang Zhao Shuang Zhao Xueting Yu Runyang Feng You-en Zhang Haomin Li Lei Huang Zhenyang Guo Zhentao Zhang Mukaddas Abdurahman Hangnan Hong Peng Li Bing Wu Jinhang Zhu Xin Zhong Dong Huang Hao Lu Xin Zhao Zhaoyang Chen Wenbin Zhang Junjie Guo Hongchao Zheng Yue He Shengying Qin Haojie Lu Yun Zhao Xiangdong Wang Junbo Ge Hua Li department of Cardiology Zhongshan HospitalFudan UniversityShanghai Institute of Cardiovascular DiseasesShanghai 200032China department of Cardiology Zhongshan HospitalFudan UniversityShanghai 200032China department of Medical Examination Shanghai Xuhui District Central HospitalShanghai 200031China department of Cardiology and Institute of Clinical Medicine Renmin HospitalHubei University of MedicineShiyan 442000China Clinical Data Center Children’s HospitalZhejiang University School of MedicineNational Clinical Research Center for Child HealthHangzhou 310052China department of Molecular Celland Cancer BiologyUniversity of Massachusetts Chan Medical SchoolWorcesterMA 01605USA Bio-X Institute Key Laboratory for The Genetics of Developmental and Neuropsychiatric DisordersShanghai Jiao Tong UniversityShanghai 200030China department of Cardiology Heart Center of Fujian ProvinceFujian Medical University Union HospitalFuzhou 350001China department of Cardiology Sir Run Run Shaw Hospitalaffiliated with Zhejiang University School of MedicineHangzhou 310020China department of Cardiology The Affiliated Hospital of Qingdao UniversityQingdao 266003China department of Cardiology Shanghai Xuhui District Central HospitalShanghai 200031China department of Cardiology Shanghai Eighth People’s HospitalShanghai 200235China department of Chemistry and Institutes of biomedical Sciences Fudan UniversityShanghai 200032China State Key Laboratory of Cell Biology Shanghai Institute of Biochemistry and Cell BiologyCenter for Excellence in Molecular Cell ScienceChinese Academy of SciencesUniversity of Chinese Academy of SciencesShanghai 200031China School of Life Science and Technology Shanghai Tech University100 Haike RoadShanghai 201210China Key Laboratory of Systems Health Science of Zhejiang Province School of Life ScienceHangzhou Institute for Advanced StudyUniversity of Chinese Academy of SciencesHangzhou 310024China department of Pulmonary and Critical Care Medicine Zhongshan Hospital

Partial endothelial-to-mesenchymal transition(EndMT)is an intermediate phenotype observed in endothelial cells(ECs)undergoing a transition toward a mesenchymal state to support neovascularization during(patho)physiological ***,we investigated the occurrence of partial EndMT in ECs under hypoxic/ischemic conditions and identified general transcription factor IIH subunit 4(GTF2H4)as a positive regulator of this *** addition,we discovered that GTF2H4 collaborates with its target protein excision repair cross-complementation group 3(ERCC3)to co-regulate partial ***,by using phosphorylation proteomics and site-directed mutagenesis,we demonstrated that GTF2H4 was involved in the phosphorylation of receptor coactivator 3(NCOA3)at serine 1330,which promoted the interaction between NCOA3 and p65,resulting in the transcriptional activation of NF-κB and the NF-kB/Snail signaling axis during partial *** vivo experiments confirmed that GTF2H4 significantly promoted partial EndMT and angiogenesis after ischemic ***,our findings reveal that targeting GTF2H4 is promising for tissue repair and offers potential opportunities for treating hypoxic/ischemic diseases.

关键词： ischemic activation GTF

RECENT DEVELOPMENTS OF TERAHERTZ TECHNOLOGY IN BIOMEDICINE

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Journal of Innovative Optical Health Sciences 2008年第1期1卷 29-44页

作者： EMMA PICKWELL-MACPHERSON SHENGYANG HUANG KANIS WAI CHI KAN YIWEN SUN YUAN TING ZHANG Joint Research Centre for biomedical Engineering department of Electronic Engineering The Chinese University of Hong Kong†Institute of biomedical and Health Engineering Shenzhen Institute of Advanced Technology Chinese Academy of Sciences China Key Laboratory for biomedical informatics and Health Engineering Chinese Academy of Science China

Terahertz technology is continually evolving and much progress has been made in recent *** new applications are being discovered and new ways to implement terahertz imaging *** this review,we limit our discussion to biomedical applications of terahertz imaging such as cancer detection,genetic sensing and molecular *** discussion of the development of new terahertz techniques is also focused on those that may accelerate the progress of terahertz imaging and spectroscopy in biomedicine.

关键词： spectroscopy. terahertz limit

Radiomic features from computed tomography to differentiate invasive pulmonary adenocarcinomas from non-invasive pulmonary adenocarcinomas appearing as part-solid groundglass nodules

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Chinese Journal of Cancer Research 2019年第2期31卷 329-338页

作者： Ting Luo Ke Xu Zheng Zhang Lina Zhang Shandong Wu department of Radiology the First Affiliated Hospital of China Medical University departments of Radiology Biomedical Informatics Bioengineering and Intelligent Systems University of Pittsburgh

Objective:We aim to investigate radiomic imaging features extracted in computed tomography(CT)images to differentiate invasive pulmonary adenocarcinomas(IPAs)from non-IPAs appearing as part-solid ground-glass nodules(GGNs),and to incorporate significant radiomic features with other clinically-assessed features to develop a diagnostic nomogram model for ***:This retrospective study was performed,with Institutional Review Board approval,on 88 patients with a total of 100 part-solid nodules(56 IPAs and 44 non-IPAs)that were surgically confirmed between February 2014and November 2016 in the First Affiliated Hospital of China Medical *** radiomic features were computed automatically on 3D nodule volume segmented from arterial-phase contrast-enhanced CT images.A set of regular risk factors and visually-assessed qualitative CT imaging features were compared with the radiomic features using logistic regression *** diagnostic models,i.e.,a basis model using the clinical factors and qualitative CT features,a radiomics model using significant radiomic features,and a nomogram model combining all significant features,were built and compared in terms of receiver operating characteristic(ROC)*** curve analysis was performed for the nomogram model to explore its potential clinical ***:In addition to three visually-assessed qualitative imaging features,another three quantitative features selected from hundreds of radiomic features were found to be significantly(all P<0.05)associated with *** diagnostic nomogram model showed a significantly higher performance[area under the ROC curve(AUC)=0.903]in differentiating IPAs from non-IPAs than either the basis model(AUC=0.853,P=0.0009)or the radiomics model(AUC=0.769,P<0.0001).Decision curve analysis indicates a potential benefit of using such a nomogram model in clinical ***:Quantitative radiomic features provide additional information over clinically-as

关键词： Radiomics lung adenocarcinomas tomography X-ray computed