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GenoDrawing:An Autoencoder Framework for Image Prediction from SNP Markers

Plant Phenomics 2023年第4期5卷 816-824页

作者： Federico Jurado-Ruiz David Rousseau Juan A.Botía Maria José Aranzana Center for Research in Agricultural Genomics(CRAG) 08193 BarcelonaCerdanyolaSpain Universitéd'Angers LARISINRAe UMR IRHS49000 AngersFrance Department of Information and Communication Engineering University of Murcia30071 MurciaSpain IRTA(Institut de Recerca i Tecnologia Agroalimentàries) BarcelonaSpain

Advancements in genome sequencing have facilitated whole-genome characterization of numerous plant species,providing an abundance of genotypic data for genomic analysis.Genomic selection and neural networks(NNs),particularly deep learning,have been developed to predict complex traits from dense genotypic data.Autoencoders,an NN model to extract features from images in an unsupervised manner,has proven to be useful for plant phenotyping.This study introduces an autoencoder framework,GenoDrawing,for predicting and retrieving apple images from a low-depth single-nucleotide polymorphism(SNP)array,potentially useful in predicting traits that are difficult to define.GenoDrawing demonstrates proficiency in its task using a small dataset of shape-related SNPs.Results indicate that the use of SNPs associated with visual traits has substantial impact on the generated images,consistent with biological interpretation.While using substantial SNPs is crucial,incorporating additional,unrelated SNPs results in performance degradation for simple NN architectures that cannot easily identify the most important inputs.The proposed GenoDrawing method is a practical framework for exploring genomic Prediction in fruit tree phenotyping,particularly beneficial for small to medium breeding companies to predict economically substantial heritable traits.Although GenoDrawing has limitations,it sets the groundwork for future research in image Prediction from genomic markers.Future studies should focus on using stronger models for image reproduction,SNP information extraction,and dataset balance in terms of phenotypes for more precise outcomes.

关键词： Auto Drawing Prediction

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Delta.EPI:a probabilistic voting-based enhancer-promoter interaction Prediction platform

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Journal of Genetics and Genomics 2023年第7期50卷 519-527页

作者： Yuyang Zhang Haoyu Wang Jing Liu Junlin Li Qing Zhang Bixia Tang Zhihua Zhang CAS Key Laboratory of Genome Sciences and Information Beijing Institute of GenomicsChinese Academy of Sciencesand China National Center for BioinformationBeijing 100101China College of Life Sciences University of Chinese Academy of SciencesBeijing 100049China School of Artificial Intelligence University of Chinese Academy of SciencesBeijing 100049China

Enhancer promoter interaction(EPI)involves most of gene transcriptional regulation in the high eukaryotes.Predicting the EPIs from given genomic loci or DNA sequences is not a trivial task.The benchmarking work so far for EPI predictors is more or less empirical and lacks quantitative model-based comparisons,posing challenges for molecular biologists to obtain reliable EPI Predictions.Here,we present an EPI Prediction platform,namely Delta.EPI.Based on a statistic model of the data integration,Delta.EPI is capable of comprehensively assessing the Predictions from four state-of-the-art EPI predictors.Equipped with a userfriendly interface and visualization platform,Delta.EPI presents the sorted results with the confidence of EPI relevance,which may guide the molecular biologists who lack the pre-knowledge of the algorithms of EPI Prediction.Last,we showcase the utility of Delta.EPI with a case study.Delta.EPI provides a powerful tool to fuel the gene regulation and 3D genome studies by ease-to-access EPI Predictions.Delta.EPI can be freely accessed at https://***/deltaEPI/.

关键词： Enhancer-promoter interaction Hi-C Prediction Benchmark Web server

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Definition,Prediction,prevention and management of patients with severe ischemic stroke and large infarction

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Chinese Medical Journal 2023年第24期136卷 2912-2922页

作者： Xing Hua Ming Liu Simiao Wu Department of Neurology West China HospitalSichuan UniversityChengduSichuan 610041China Center of Cerebrovascular Diseases West China HospitalSichuan UniversityChengduSichuan 610041China

Severe ischemic stroke carries a high rate of disability and death.The severity of stroke is often assessed by the degree of neurological deficits or the extent of brain infarct,defined as severe stroke and large infarction,respectively.Critically severe stroke is a life-threatening condition that requires neurocritical care or neurosurgical intervention,which includes stroke with malignant brain edema,a leading cause of death during the acute phase,and stroke with severe complications of other vital systems.Early Prediction of high-risk patients with critically severe stroke would inform early prevention and treatment to interrupt the malignant course to fatal status.Selected patients with severe stroke could benefit from intravenous thrombolysis and endovascular treatment in improving functional outcome.There is insufficient evidence to inform dual antiplatelet therapy and the timing of anticoagulation initiation after severe stroke.Decompressive hemicraniectomy(DHC)Prediction of malignant brain edema,optimal indications for acute reperfusion therapies and neurosurgery,and the individualized management of complications and secondary prevention.We present an evidence-based review for severe ischemic stroke,with the aims of proposing operational definitions,emphasizing the importance of early Prediction and prevention of the evolution to critically severe status,summarizing specialized treatment for severe stroke,and proposing directions for future research.

关键词： Severe stroke Large infarction Malignant brain edema Critically severe stroke Definition Prediction Prevention Management

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Development,validation and comparison of multivariable risk scores for Prediction of total stroke and stroke types in Chinese adults:a prospective study of 0.5 million adults

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Stroke & Vascular Neurology 2022年第4期7卷 328-336,I0023-I0045页

作者： Matthew Chun Robert Clarke Tingting Zhu David Clifton Derrick A Bennett Yiping Chen Yu Guo Pei Pei Jun Lv Canqing Yu Ling Yang Liming Li Zhengming Chen Benjamin J Cairns 无 Clinical Trial Service Unit and Epidemiological Studies Nuffield Department of Population HealthUniversity of OxfordOxfordUK Department of Engineering Science University of OxfordOxfordUK Department of Biomedical Engineering Oxford-Suzhou Centre for Advanced ResearchSuzhouChina Medical Research Council Health Research Unit Nuffield Department of Population HealthUniversity of OxfordOxfordUK CKB Project Department Fuwai Hospital Chinese Academy of Medical SciencesNational Center for Cardiovascular DiseasesBeijingChina CKB Project Department Chinese Academy of Medical SciencesBeijingChina Department of Epidemiology and Biostatistics School of Public HealthPeking UniversityBeijingChina Department of Epidemiology Peking University Center for Public Health and Epidemic Preparedness and ResponseBeijingChina On behalf of the China Kadoorie Biobank Collaborative Group

Background and purpose Low-income and middle-income countries have the greatest stroke burden,yet remain understudied.This study compared the utility of Framingham versus novel risk scores for Prediction of total stroke and stroke types in Chinese adults.Methods China Kadoorie Biobank(CKB)is a prospective study of 512726 adults,aged 30-79 years,recruited from 10 areas in China in 2004-2008.By 1 January 2018,43234 incident first stroke cases(36310 ischaemic stroke(IS);8865 haemorrhagic stroke(HS))were recorded in 503842 participants with no history of stroke at baseline.We compared the predictive utility of the Framingham Stroke Risk Profile(FSRP)with novel CKB stroke risk scores and included recalibration,refitting,stratifying by study area and addition of other risk factors.Discrimination was assessed using area under the receiver operating characteristic curve(AUC)and calibration was assessed using Greenwood-Nam-D’Agostinoχ^(2) statistics.Results Incidence of total stroke varied fivefold by area in China.The FSRP had good discrimination for total stroke(AUC(95%CI);men:0.78(0.77 to 0.79),women:0.77(95%CI 0.76 to 0.78)),but poor calibration(χ^(2);men:1,825,women:3,053),substantially underestimating absolute risks.Recalibration reducedχ^(2) by>80%,but did not improve discrimination.Refitting the FSRP did not materially improve discrimination,but further improved calibration.Stratification by area improved discrimination(AUC;men:0.82(0.82 to 0.83);women:0.82(0.82 to 0.83)),but not calibration.Adding other risk factors yielded modest,but statistically significant,improvements in the AUCs.The findings for IS and HS were similar to those for total stroke.Conclusions The FSRP reliably differentiated Chinese adults with incident stroke,but substantially underestimated the absolute risks of stroke.Novel local risk Prediction equations that took account of differences in stroke incidence within China enhanced risk Prediction of total stroke and major stroke pathological types.

关键词： Prediction income absolute

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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 production.In 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 assays.Analysis of this curated dataset showed large pathogen diversity covering 176 different families.The accuracy in linear BCE Prediction was found to strongly vary with different features,while all sequencederived and structural features were informative.To 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 dataset.Through 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 hierarchy.The Net BCE is freely available at https://***/bsml320/Net BCE.

关键词： Prediction network value

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Modeling and Prediction of tribological properties of copper/aluminum-graphite self-lubricating composites using machine learning algorithms

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Friction 2024年第6期12卷 1322-1340页

作者： Huifeng NING Faqiang CHEN Yunfeng SU Hongbin LI Hengzhong FAN Junjie SONG Yongsheng ZHANG Litian HU School of Electrical and Mechanical Engineering Lanzhou University of TechnologyLanzhou 730050China State Key Laboratory of Solid Lubrication Lanzhou Institute of Chemical PhysicsChinese Academy of SciencesLanzhou 730000China Center of Materials Science and Optoelectronics Engineering University of Chinese Academy of SciencesBeijing 100049China

The tribological properties of self-lubricating composites are influenced by many variables and complex mechanisms.Data-driven methods,including machine learning(ML)algorithms,can yield a better comprehensive understanding of complex problems under the influence of multiple parameters,typically for how tribological performances and material properties correlate.Correlation of friction coefficients and wear rates of copper/aluminum-graphite(Cu/Al-graphite)self-lubricating composites with their inherent material properties(composition,lubricant content,particle size,processing process,and interfacial bonding strength)and the variables related to the testing method(normal load,sliding speed,and sliding distance)were analyzed using traditional approaches,followed by modeling and Prediction of tribological properties through five different ML algorithms,namely support vector machine(SVM),K-Nearest neighbor(KNN),random forest(RF),eXtreme gradient boosting(XGBoost),and least-squares boosting(LSBoost),based on the tribology experimental data.Results demonstrated that ML models could satisfactorily predict friction coefficient and wear rate from the material properties and testing method variables data.Herein,the LSBoost model based on the integrated learning algorithm presented the best Prediction performance for friction coefficients and wear rates,with R2 of 0.9219 and 0.9243,respectively.Feature importance analysis also revealed that the content of graphite and the hardness of the matrix have the greatest influence on the friction coefficients,and the normal load,the content of graphite,and the hardness of the matrix influence the wear rates the most.

关键词： self-lubricating composites machine learning(ML) tribological properties Prediction

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Development of a molecular feature-based survival Prediction model of ovarian cancer using the deep neural network

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Genes & Diseases 2023年第4期10卷 1190-1193页

作者： Tingyuan Lang Muyao Yang Yunqiu Xia Jingshu Liu Yunzhe Li Lingling Yang Chenxi Cui Yunran Hu Yang Luo Dongling Zou Lei Zhou Zhou Fu Qi Zhou Department of Gynecologic Oncology Chongqing University Cancer Hospital&Chongqing Cancer Institute&Chongqing Cancer HospitalChongqing 400030China Reproductive Medicine Center The First Affiliated Hospital of Chongqing Medical UniversityChongqing 400042China Bioengineering College of Chongqing University Chongqing 400044China Department of Dermatology Ministry of Education Key Laboratory of Child Development and DisordersNational Clinical Research Center for Child Health and DisordersChina International Science and Technology Cooperation Base of Child Development and Critical Disorders Chongqing Engineering Research Center of Stem Cell TherapyChildren’s Hospital of Chongqing Medical UniversityChongqingChina Department of Gynaecology and Obstetrics Reproductive Medicine CenterThe Second Affiliated HospitalChongqing Medical UniversityChongqing 400030China School of Medicine Chongqing UniversityChongqing 400044China Department of Pharmacy Affiliated Drum Tower Hospital of Nanjing University Medical SchoolNanjing 210008China School of Basic Medicine and Clinical Pharmacy China Pharmaceutical UniversityNanjing 210009Jiangsu ProvinceChina Center of Smart Laboratory and Molecular Medicine School of MedicineChongqing UniversityChongqing 400044China Centre for Eye and Vision Research School of OptometryThe Hong Kong Polytechnic UniversityHong Kong SARChina Centre for Eye and Vision Research Department of Applied Biology and Chemical TechnologyThe Hong Kong Polytechnic UniversityHong Kong SARChina Centre for Eye and Vision Research Research Centre for SHARP Vision(RCSV)The Hong Kong Polytechnic UniversityHong Kong SARChina Department of Respiratory Children’s Hospital of Chongqing Medical University&National Clinical Research Center for Child Health and Disorders&Ministry of Education Key Laboratory of Child Development and DisordersChongqing 400044China

Ovarian cancer(OC)is one of the most lethal gynecologic cancer worldwide,and survival Prediction is meaningful for personalized treatment.^(1)The survival outcome of cancer patients mainly depended on the malignancy of the primary tumor which is tightly linked with the expression profile of the molecular features.^(2)Therefore,in this study,we developed a molecular feature-based survival Prediction model of OC using a deep neural network(DNN).

关键词： cancer Prediction neural

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Improved Prediction of pile bending moment and deflection due to adjacent braced excavation

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Frontiers of Structural and Civil Engineering 2023年第11期17卷 1739-1759页

作者： Chana PHUTTHANANON Pornkasem JONGPRADIST Duangkamol SIRIRAK Prateep LUEPRASERT Pitthaya JAMSAWANG Construction Innovations and Future Infrastructures Research Center Department of Civil EngineeringFaculty of EngineeringKing Mongkut's University of Technology ThonburiBangkok 10140Thailand Department of Civil Engineering School of EngineeringKing Mongkut's Institute of Technology LadkrabangBangkok 10520Thailand Soil Engineering Research Center Department of Civil EngineeringKing Mongkut's University of Technology North BangkokBangkok10800Thailand

Deep excavations in dense urban areas have caused damage to nearby existing structures in numerous past construction cases.Proper assessment is crucial in the initial design stages.This study develops equations to predict the existing pile bending moment and deflection produced by adjacent braced excavations.Influential parameters(i.e.,the excavation geometry,diaphragm wall thickness,pile geometry,strength and small-strain stiffness of the soil,and soft clay thickness)were considered and employed in the developed equations.It is practically unfeasible to obtain measurement data;hence,artificial data for the bending moment and deflection of existing piles were produced from well-calibrated numerical analyses of hypothetical cases,using the three-dimensional finite element method.The developed equations were established through a multiple linear regression analysis of the artificial data,using the transformation technique.In addition,the three-dimensional nature of the excavation work was characterized by considering the excavation corner effect,using the plane strain ratio parameter.The estimation results of the developed equations can provide satisfactory pile bending moment and deflection data and are more accurate than those found in previous studies.

关键词： pile responses excavation Prediction deflection bending moments

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Comparing COVID-19 Case Prediction Between ARIMA Model and Compartment Model—China,December 2019–April 2020

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China CDC weekly 2022年第52期4卷 1185-1188,I0006-I0010页

作者： Bangguo Qi Nankun Liu Shicheng Yu Feng Tan Chinese Center for Disease Control and Prevention BeijingChina.

Introduction:To compare the performance between the compartment model and the autoregressive integrated moving average(ARIMA)model that were applied to the Prediction of new infections during the coronavirus disease 2019(COVID-19)epidemic.Methods:The compartment model and the ARIMA model were established based on the daily cases of new infection reported in China from December 2,2019 to April 8,2020.The goodness of fit of the two models was compared using the coefficient of determination(R2).Results:The compartment model predicts that the number of new cases without a cordon sanitaire,i.e.,a restriction of mobility to prevent spread of disease,will increase exponentially over 10 days starting from January 23,2020,while the ARIMA model shows a linear increase.The calculated R2 of the two models without cordon sanitaire were 0.990 and 0.981.The Prediction results of the ARIMA model after February 2,2020 have a large deviation.The R2 of complete transmission process fit of the epidemic for the 2 models were 0.964 and 0.933,respectively.Discussion:The two models fit well at different stages of the epidemic.The Predictions of compartment model were more in line with highly contagious transmission characteristics of COVID-19.The accuracy of recent historical data had a large impact on the Predictions of the ARIMA model as compared to those of the compartment model.&Joint first authors.

关键词： ARIMA Prediction starting

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Source identification and Prediction of nitrogen and phosphorus pollution of Lake Taihu by an ensemble machine learning technique

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Frontiers of Environmental Science & Engineering 2023年第5期17卷 27-40页

作者： Yirong Hu Wenjie Du Cheng Yang Yang Wang Tianyin Huang Xiaoyi Xu Wenwei Li CAS Key Laboratory of Urban Pollutant Conversion Department of Environmental Science and EngineeringUniversity of Science and Technology of ChinaHefei 230026China School of Software Engineering University of Science and Technology of ChinaHefei 230026China USTC-CityU Joint Advanced Research Center Suzhou Institute for Advanced Research of USTCSuzhou 215123China National and Local Joint Engineering Laboratory for Municipal Sewage Resource Utilization Technology Suzhou University of Science and TechnologySuzhou 215009China

Effective control of lake eutrophication necessitates a full understanding of the complicated nitrogen and phosphorus pollution sources,for which mathematical modeling is commonly adopted.In contrast to the conventional knowledge-based models that usually perform poorly due to insufficient knowledge of pollutant geochemical cycling,we employed an ensemble machine learning(ML)model to identify the key nitrogen and phosphorus sources of lakes.Six ML models were developed based on 13 years of historical data of Lake Taihu’s water quality,environmental input,and meteorological conditions,among which the XGBoost model stood out as the best model for total nitrogen(TN)and total phosphorus(TP)Prediction.The results suggest that the lake TN is mainly affected by the endogenous load and inflow river water quality,while the lake TP is predominantly from endogenous sources.The Prediction of the lake TN and TP concentration changes in response to these key feature variations suggests that endogenous source control is a highly desirable option for lake eutrophication control.Finally,one-month-ahead Prediction of lake TN and TP concentrations(R2 of 0.85 and 0.95,respectively)was achieved based on this model with sliding time window lengths of 9 and 6 months,respectively.Our work demonstrates the great potential of using ensemble ML models for lake pollution source tracking and Prediction,which may provide valuable references for early warning and rational control of lake eutrophication.

关键词： Eutrophication Machine learning Water quality Nutrients Prediction

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