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Deep Learning-Driven Data Curation and Model Interpretation for Smart Manufacturing 认领 引用 被引量：8
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作者 Jianjing Zhang Robert X.Gao 《Chinese Journal of Mechanical Engineering》 SCIE EI CAS CSCD 2021年第3期52-72,共21页
Characterized by self-monitoring and agile adaptation to fast changing dynamics in complex production environments,smart manufacturing as envisioned under Industry 4.0 aims to improve the throughput and reliability of... Characterized by self-monitoring and agile adaptation to fast changing dynamics in complex production environments,smart manufacturing as envisioned under Industry 4.0 aims to improve the throughput and reliability of production beyond the state-of-the-art.While the widespread application of deep learning(DL)has opened up new opportunities to accomplish the goal,data quality and model interpretability have continued to present a roadblock for the widespread acceptance of DL for real-world applications.This has motivated research on two fronts:data curation,which aims to provide quality data as input for meaningful DL-based analysis,and model interpretation,which intends to reveal the physical reasoning underlying DL model outputs and promote trust from the users.This paper summarizes several key techniques in data curation where breakthroughs in data denoising,outlier detection,imputation,balancing,and semantic annotation have demonstrated the effectiveness in information extraction from noisy,incomplete,insufficient,and/or unannotated data.Also highlighted are model interpretation methods that address the“black-box”nature of DL towards model transparency. 展开更多
关键词 Deep learning Data curation Model interpretation
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Genetic architecture of key traits for Prunus crop improvement:an overview of 25 years of curated genomic and breeding data 认领 引用
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作者 Michael Itam Sook Jung +5 位作者 Ping Zheng Taein Lee Chun-Huai Cheng Katheryn Buble Dorrie Main Ksenija Gasic 《Horticulture Research》 SCIE CSCD 2025年第8期361-373,共13页
The extensive accumulation of genetic,genomic,expression,and breeding data on Prunus species often results in valuable information being lost or difficult to access for breeding purposes.We report a recent effort to i... The extensive accumulation of genetic,genomic,expression,and breeding data on Prunus species often results in valuable information being lost or difficult to access for breeding purposes.We report a recent effort to increase curation on Prunus data in the Genome Database for Rosaceae(GDR,rosaceae.org)and a case study that explores 25 years of curated data(from 1998 to 2023)to uncover the genetic architecture of key traits in Prunus species,provide actionable insights for breeding,and encourage the use of shared molecular data across Prunus species.The curated data includes 177 genetic maps,primarily for almond(19),apricot(21),peach(52),and sweet cherry(46).A total of 28971 trait-associated loci were reported,with 72.4% derived from genome-wide association studies,18.7% from quantitative trait loci(QTL),and 8.9% from Mendelian trait loci.Notably,76.4% of these loci are associated with morphological and quality traits,reflecting breeders'focus on consumer preferences.We identified 16 potential QTL hotspots linked to key traits such as morphology,phenology,fruit quality,and disease resistance.Additionally,we identified 17 high-priority syntenic regions among peach,sweet cherry,and almond.The colocalized markers and genes within the QTL hotspots and syntenic regions offer a valuable resource for tool development for Prunus breeding,especially for complex polyploid genomes and lesser studied species with limited genetic and genomic data. 展开更多
关键词 Prunus breeding fruit quality curated data genome database genetic architecture genomic data morphological traits
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The China National GeneBank Sequence Archive(CNSA)2024 update 认领 引用 被引量：1
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作者 Weiwen Wang Cong Tan +25 位作者 Ling Li Xia Li Lei Zhang Xiaoqiang Li Jieyu Wang Ziyi He Tao Yang Kailong Ma Qingjiang Hu Wenzhen Yang Zhiyong Li Mingwen Zhang Wensi Du Fan Yang Zhicheng Xu Xizheng Ma Jiawei Tong Jia Cai Cong Hua Fengzhen Chen Lijin You Liang Li Wenjun Zeng Bo Wang Xun Xu Xiaofeng Wei 《Horticulture Research》 SCIE CSCD 2025年第5期237-246,共10页
The China National GeneBank Sequence Archive(CNSA)is an open and freely accessible curated data repository built for archiving,sharing,and reutilizing of multiomics data.The remarkable advancement in sequencing techno... The China National GeneBank Sequence Archive(CNSA)is an open and freely accessible curated data repository built for archiving,sharing,and reutilizing of multiomics data.The remarkable advancement in sequencing technologies has triggered a paradigm shift in life science research.However,it also poses tremendous challenges for the research community in data management and reusability.With the dramatic advance of sequencing technologies like spatial transcriptome sequencing,it brings an unprecedented explosion in sequence data and new requirements for data archiving.CNSA was established in 2017 as one of the fundamental infrastructures to offer multiomics data archiving for the worldwide research community.Here,we present the state-of-the-art enhancements of CNSA encompassing the dramatical increase of varied types of data,the latest features and services implemented in CNSA as well as consistent efforts supporting global cooperation in biodiversity preservation and utilization.CNSA provides public archiving and open-sharing services for sequencing data and relevant metadata including genome,transcriptome,metabolism,and proteome from single-cell(also spatial resolved)level to individual and population level,as well as further analyzed results.As of 2024,CNSA has archived>16.3 petabytes of data and provided the data curation,preservation,and open-share service for>1581 publications from>560 institutions.It plays a pivotal role in supporting global scientific projects such as the 10000 Plant Genomes Project.So far,CNSA has been recommended by various academic publishers such as Cell,Elsevier,and Oxford University Press.CNSA is accessible at http://gffzz984746bd41ee48d7sbxfx6w9cxcoq60op.ffgz.tsg.suse.edu.cn/cnsa/. 展开更多
关键词 spatial transcriptome sequencingit China National Genebank Sequence Archive cnsa spatial transcriptome sequencing reusability sequencing technologies curated data repository data management
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LigExtract:Large-scale Automated Identification of Ligands from Protein Structures in the Protein Data Bank 认领 引用 被引量：1
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作者 Natalia Aniceto Nuno Martinho +1 位作者 Ismael Rufino Rita CGuedes 《Genomics, Proteomics & Bioinformatics》 SCIE CAS CSCD 2025年第4期201-215,共15页
The Protein Data Bank(PDB)is an ever-growing database of three-dimensional macromolecular structures that has become a crucial resource for the drug discovery process.Exploring complexed proteins and accessing their a... The Protein Data Bank(PDB)is an ever-growing database of three-dimensional macromolecular structures that has become a crucial resource for the drug discovery process.Exploring complexed proteins and accessing their associated ligands are essential for researchers to understand biological processes and design new compounds of pharmaceutical interest.However,currently available tools for large-scale ligand identification fail to address many of the more complex ways in which ligands are stored and represented in PDB structures.Therefore,a new tool called LigExtract was specifically developed for the large-scale processing of PDB structures and the identification of their ligands.This is a fully opensource tool available to the scientific community,designed to provide end-to-end processing.Users simply provide a list of UniProt IDs,and LigExtract returns a list of ligands,their individual PDB files,a PDB file of the protein chains interacting with the ligand,and a series of log files.These logs record the decisions made during the ligand extraction process and flag additional scenarios that might have to be considered during any follow-up use of the processed files(e.g.,ligands covalently bound to the protein).LigExtract is freely available on GitHub(http://gffzz188fe103f8f1460asbxfx6w9cxcoq60op.ffgz.tsg.suse.edu.cn/comp-medchem/LigExtract). 展开更多
关键词 Ligand identification Protein Data Bank mmCIF Data curation Ligand clustering
On the collection and integration of SARS-CoV-2 genome data 认领 引用 被引量：2
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作者 Lina Ma Wei Zhao +4 位作者 Tianhao Huang Enhui Jin Gangao Wu Wenming Zhao Yiming Bao 《Biosafety and Health》 CAS CSCD 2023年第4期204-210,共7页
Genome data of severe acute respiratory syndrome coronavirus 2(SARS-CoV-2)is essential for virus diagnosis,vaccine development,and variant surveillance.To archive and integrate worldwide SARS-CoV-2 genome data,a serie... Genome data of severe acute respiratory syndrome coronavirus 2(SARS-CoV-2)is essential for virus diagnosis,vaccine development,and variant surveillance.To archive and integrate worldwide SARS-CoV-2 genome data,a series of resources have been constructed,serving as a fundamental infrastructure for SARS-CoV-2 research,pandemic prevention and control,and coronavirus disease 2019(COVID-19)therapy.Here we present an over-view of extant SARS-CoV-2 resources that are devoted to genome data deposition and integration.We review deposition resources in data accessibility,metadata standardization,data curation and annotation;review integrative resources in data source,de-redundancy processing,data curation and quality assessment,and variant annotation.Moreover,we address issues that impede SARS-CoV-2 genome data integration,including low-complexity,inconsistency and absence of isolate name,sequence inconsistency,asynchronous update of genome data,and mismatched metadata.We finally provide insights into data standardization consensus and data submission guidelines,to promote SARS-CoV-2 genome data sharing and integration. 展开更多
关键词 SARS-CoV-2 resource Genome data Data deposition Data integration Data curation
Implementation of the FAIR Data Principles for Exploratory Biomarker Data from Clinical Trials 认领 引用 被引量：2
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作者 Alexander Arefolov Laura Adam +15 位作者 Shoshana Brown Yelena Budovskaya Cong Chen Diya Das Chen Farhy Rebecca Ferguson Hongmei Huang Kimberly Kanigel Christina Lu Oksana Polesskaya Tracy Staton Rajeev Tajhya Maryann Whitley Jee-Yeon Wong Xiangpei Zeng Mark McCreary 《Data Intelligence》 EI 2021年第4期630-661,共32页
The FAIR data guiding principles have been recently developed and widely adopted to improve the Findability,Accessibility,Interoperability,and Reuse of digital assets in the face of an exponential increase of data vol... The FAIR data guiding principles have been recently developed and widely adopted to improve the Findability,Accessibility,Interoperability,and Reuse of digital assets in the face of an exponential increase of data volume and complexity.The FAIR data principles have been formulated on a general level and the technological implementation of these principles remains up to the industries and organizations working on maximizing the value of their data.Here,we describe the data management and curation methodologies and best practices developed for FAIRification of clinical exploratory biomarker data collected from over 250 clinical studies.We discuss the data curation effort involved,the resulting output,and the business and scientific impact of our work.Finally,we propose prospective planning for FAIR data to optimize data management efforts and maximize data value. 展开更多
关键词 Data management Data curation Clinical studies FAIR data Biomarkers Data model Metadata Data standards Data governance
Biological Databases for Human Research 认领 引用 被引量：6
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作者 Dong Zou Lina Ma +1 位作者 Jun Yu Zhang Zhang 《Genomics, Proteomics & Bioinformatics》 SCIE CAS CSCD 2015年第1期55-63,共9页
The completion of the Human Genome Project lays a foundation for systematically studying the human genome from evolutionary history to precision medicine against diseases.With the explosive growth of biological data, ... The completion of the Human Genome Project lays a foundation for systematically studying the human genome from evolutionary history to precision medicine against diseases.With the explosive growth of biological data, there is an increasing number of biological databases that have been developed in aid of human-related research. Here we present a collection of humanrelated biological databases and provide a mini-review by classifying them into different categories according to their data types. As human-related databases continue to grow not only in count but also in volume, challenges are ahead in big data storage, processing, exchange and curation. 展开更多
关键词 Human Database Big data Database category Curation
Special Issue for "Spatial Multiomics" 认领 引用
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《Genomics, Proteomics & Bioinformatics》 SCIE CAS CSCD 2025年第3期I0008-I0008,共1页
The journal Genomics,Proteomics&Bioinformatics(GPB)is interested in submissions across all areas of life science,biology,and biomedicine,focusing on large data acquisition,analysis,and curation.
关键词 proteomics spatial multiomics large data acquisition data curation data analysis large data acquisitionanalysisand bioinformatics genomics
How to(Easily)Extend the FAIRness of Existing Repositories 认领 引用 被引量：8
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作者 Mark Hahnel Dan Valen 《Data Intelligence》 EI 2020年第1期192-198,314,共7页
Data repository infrastructures for academics have appeared in waves since the dawn of Web technology.These waves are driven by changes in societal needs,archiving needs and the development of cloud computing resource... Data repository infrastructures for academics have appeared in waves since the dawn of Web technology.These waves are driven by changes in societal needs,archiving needs and the development of cloud computing resources.As such,the data repository landscape has many flavors when it comes to sustainability models,target audiences and feature sets.One thing that links all data repositories is a desire to make the content they host reusable,building on the core principles of cataloging content for economical and research speed efficiency.The FAIR principles are a common goal for all repository infrastructures to aim for.No matter what discipline or infrastructure,the goal of reusable content,for both humans and machines,is a common one.This is the first time that repositories can work toward a common goal that ultimately lends itself to interoperability.The idea that research can move further and faster as we un-silo these fantastic resources is an achievable one.This paper investigates the steps that existing repositories need to take in order to remain useful and relevant in a FAIR research world. 展开更多
关键词 FAIR data Metadata Interoperability Repositories Data curation
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