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Explaining data using causal Bayesian networks

Jaime
<span title="2020-11-01">2020</span> <i title="Zenodo"> Zenodo </i> &nbsp;
a research avenue for studying pairwise identification of causal relations inspired by graphical causality criteria.  ...  We introduce Causal Bayesian Networks as a formalism for representing and explaining probabilistic causal relations, review the state of the art on learning Causal Bayesian Networks and suggest and illustrate  ...  I also thank the anonymous reviewers for the NL4XAI for kindly providing constructive feedback to improve the paper.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.5281/zenodo.5897993">doi:10.5281/zenodo.5897993</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/tbp4abjmyvcbjhqa7wyomupgjq">fatcat:tbp4abjmyvcbjhqa7wyomupgjq</a> </span>
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Guest editorial: special issue on causal discovery

Jiuyong Li, Kun Zhang, Elias Bareinboim, Lin Liu
<span title="">2017</span> <i title="Springer Nature"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/7xomsiq24ffadenkh4qzgc75tm" style="color: black;">International Journal of Data Science and Analytics</a> </i> &nbsp;
In the invited paper, "Introduction to the foundations of causal discovery," Frederick Eberhardt, keynote speaker of the KDD workshop, presents an introduction to various graphical causal models and their  ...  Estimating the underlying causal structures from observational data is one of the central challenges of causal discovery.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1007/s41060-016-0041-y">doi:10.1007/s41060-016-0041-y</a> <a target="_blank" rel="external noopener" href="https://dblp.org/rec/journals/ijdsa/LiZBL17.html">dblp:journals/ijdsa/LiZBL17</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/5vr33zggmbctxghz7tqcoj5lda">fatcat:5vr33zggmbctxghz7tqcoj5lda</a> </span>
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Statistical Causal Inference in the Era of Interventional Neuroimaging [article]

Manjari Narayan
<span title="2020-06-26">2020</span> <i title="figshare"> Figshare </i> &nbsp;
bridge the chasm between probabilistic graphical models and their causal counterparts.Citation: Manjari Narayan.  ...  Probabilistic graphical models are popularly used to model such interactions where the nodes denote neural variables and edges denote some form of statistical dependence between these regions.  ...  Ancestral Graphs Causal Discovery: Big Picture Causal Discovery Causal Inference Statistical Inference Causal Model Probabilistic Model Data Review Nandy & Maathuis, 2015 Methodology  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.6084/m9.figshare.12576182">doi:10.6084/m9.figshare.12576182</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/5o2zscscy5ffloqioewgfs7abu">fatcat:5o2zscscy5ffloqioewgfs7abu</a> </span>
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Simulations evaluating resampling methods for causal discovery: ensemble performance and calibration [article]

Erich Kummerfeld, Alexander Rix
<span title="2019-10-04">2019</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
One of the major hurdles preventing the field of causal discovery from having a larger impact is that it is difficult to determine when the output of a causal discovery method can be trusted in a real-world  ...  Causal discovery can be a powerful tool for investigating causality when a system can be observed but is inaccessible to experiments in practice.  ...  Acknowledgment The authors would like to thank the Center for Causal Discovery for developing and supporting the open source Tetrad software package.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/1910.02047v1">arXiv:1910.02047v1</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/7ckgl2im7ra43kezucen6l2obq">fatcat:7ckgl2im7ra43kezucen6l2obq</a> </span>
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Handling hybrid and missing data in constraint-based causal discovery to study the etiology of ADHD

Elena Sokolova, Daniel von Rhein, Jilly Naaijen, Perry Groot, Tom Claassen, Jan Buitelaar, Tom Heskes
<span title="2016-12-02">2016</span> <i title="Springer Nature"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/7xomsiq24ffadenkh4qzgc75tm" style="color: black;">International Journal of Data Science and Analytics</a> </i> &nbsp;
We demonstrate the validity of our approach for causal discovery on simulated data as well as on two real-world data sets from a monetary incentive delay task and a reversal learning task.  ...  Causal discovery is an increasingly important method for data analysis in the field of medical research.  ...  In this paper, we propose to transfer the ideas of structure learning for undirected graphical models to causal discovery.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1007/s41060-016-0034-x">doi:10.1007/s41060-016-0034-x</a> <a target="_blank" rel="external noopener" href="https://www.ncbi.nlm.nih.gov/pubmed/28691055">pmid:28691055</a> <a target="_blank" rel="external noopener" href="https://pubmed.ncbi.nlm.nih.gov/PMC5479362/">pmcid:PMC5479362</a> <a target="_blank" rel="external noopener" href="https://dblp.org/rec/journals/ijdsa/SokolovaRNGCBH17.html">dblp:journals/ijdsa/SokolovaRNGCBH17</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/537xfzy6wjg3nolmdpmt32n33u">fatcat:537xfzy6wjg3nolmdpmt32n33u</a> </span>
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Graphical Causal Models and Imputing Missing Data: A Preliminary Study [chapter]

Rui Jorge Almeida, Greetje Adriaans, Yuliya Shapovalova
<span title="">2020</span> <i title="Springer International Publishing"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/jyopc6cf5ze5vipjlm4aztcffi" style="color: black;">Communications in Computer and Information Science</a> </i> &nbsp;
Our goal is to use the information from a complete dataset in the form of graphical causal models to impute missing values in an incomplete dataset.  ...  In this paper, we investigate if graphical causal models can be used to impute missing values and derive additional information on the uncertainty of the imputed values.  ...  In this paper we investigate if graphical causal models can be used to impute missing values. Causal discovery aims to learn the causal relations between variables of a system of interest from data.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1007/978-3-030-50146-4_36">doi:10.1007/978-3-030-50146-4_36</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/4um7fifgdvgsbfpoeef3pg2dmy">fatcat:4um7fifgdvgsbfpoeef3pg2dmy</a> </span>
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Proof and Uncertainty in Causal Claims

Martine Jayne Barons, Rachel L Wilkerson
<span title="2018-06-07">2018</span> <i title="University of Warwick"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/qulghh3mxja5ficmgelovg4dme" style="color: black;">Exchanges</a> </i> &nbsp;
The difficulty of drawing causal conclusions from observational data has prompted developments in new methodologies, most notably in the area of graphical models.  ...  From Hume to Granger, and Rubin to Pearl the history of science is full of examples of scientists testing new theories in an effort to uncover causal mechanisms.  ...  Cowell and Smith (2014) develop causal discovery techniques to find the best fitting CEG from data.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.31273/eirj.v5i2.238">doi:10.31273/eirj.v5i2.238</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/xjzeiclvzjgpvjfenjpxf2th5a">fatcat:xjzeiclvzjgpvjfenjpxf2th5a</a> </span>
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Review of Causal Discovery Methods Based on Graphical Models

Clark Glymour, Kun Zhang, Peter Spirtes
<span title="2019-06-04">2019</span> <i title="Frontiers Media SA"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/r7trx2kj6je5jhtaoy3rztibgy" style="color: black;">Frontiers in Genetics</a> </i> &nbsp;
It is then necessary to discover causal relations by analyzing statistical properties of purely observational data, which is known as causal discovery or causal structure search.  ...  This paper aims to give a introduction to and a brief review of the computational methods for causal discovery that were developed in the past three decades, including constraint-based and score-based  ...  AUTHOR CONTRIBUTIONS All authors listed have made a substantial, direct and intellectual contribution to the work, and approved it for publication.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.3389/fgene.2019.00524">doi:10.3389/fgene.2019.00524</a> <a target="_blank" rel="external noopener" href="https://www.ncbi.nlm.nih.gov/pubmed/31214249">pmid:31214249</a> <a target="_blank" rel="external noopener" href="https://pubmed.ncbi.nlm.nih.gov/PMC6558187/">pmcid:PMC6558187</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/q2ruix27tjcc3b4oze4y7223by">fatcat:q2ruix27tjcc3b4oze4y7223by</a> </span>
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An Empirical Study of Encoding Schemes and Search Strategies in Discovering Causal Networks [chapter]

Honghua Dai, Gang Li, Yiqing Tu
<span title="">2002</span> <i title="Springer Berlin Heidelberg"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/2w3awgokqne6te4nvlofavy5a4" style="color: black;">Lecture Notes in Computer Science</a> </i> &nbsp;
Efficiently inducing precise causal models accurately reflecting given data sets is the ultimate goal of causal discovery.  ...  The algorithm proposed by Wallace et al. [10] has demonstrated its ability in discovering Linear Causal Models from data.  ...  As Graphical Model can often be plausibly understood as describing causal relations, the automatic construction of Graphical Model is usually referred as Causal Discovery.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1007/3-540-36755-1_5">doi:10.1007/3-540-36755-1_5</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/4s3uyri3rngdngqqfzfgh6vh3e">fatcat:4s3uyri3rngdngqqfzfgh6vh3e</a> </span>
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The Center for causal discovery of biomedical knowledge from Big Data

Gregory F. Cooper, Ivet Bahar, Michael J. Becich, Panayiotis V. Benos, Jeremy Berg, Jeremy U. Espino, Clark Glymour, Rebecca Crowley Jacobson, Michelle Kienholz, Adrian V. Lee, Xinghua Lu, Richard Scheines
<span title="2015-07-02">2015</span> <i title="Oxford University Press (OUP)"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/aapnwtybrvghlc35hfimbtdlom" style="color: black;">JAMIA Journal of the American Medical Informatics Association</a> </i> &nbsp;
to Knowledge initiative (www.bd2k.nih.gov).  ...  Key Personnel FUNDING Research reported in this publication was supported by grant U54HG008540 awarded by the National Human Genome Research Institute through funds provided by the trans-NIH Big Data  ...  of causal modeling and discovery (CMD) algorithms and tools designed to help address causal discovery in biomedicine from big data. scientists involved in algorithm and software development, meet biweekly  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1093/jamia/ocv059">doi:10.1093/jamia/ocv059</a> <a target="_blank" rel="external noopener" href="https://www.ncbi.nlm.nih.gov/pubmed/26138794">pmid:26138794</a> <a target="_blank" rel="external noopener" href="https://pubmed.ncbi.nlm.nih.gov/PMC5009908/">pmcid:PMC5009908</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/cpu3vju33vd6beky4y6ku2nnjy">fatcat:cpu3vju33vd6beky4y6ku2nnjy</a> </span>
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Causal Discovery from Medical Data: Dealing with Missing Values and a Mixture of Discrete and Continuous Data [chapter]

Elena Sokolova, Perry Groot, Tom Claassen, Daniel von Rhein, Jan Buitelaar, Tom Heskes
<span title="">2015</span> <i title="Springer International Publishing"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/2w3awgokqne6te4nvlofavy5a4" style="color: black;">Lecture Notes in Computer Science</a> </i> &nbsp;
We demonstrate the validity of our approach for causal discovery for empiric data from a monetary incentive delay task.  ...  Causal discovery is an increasingly popular method for data analysis in the field of medical research.  ...  Faraone), NWO Large Investment Grant (1750102007010 to Jan Buitelaar), NWO Brain & Cognition grants (056-13-015 and 433-09-242 to Jan Buitelaar), and grants from Radboud University Nijmegen Medical Center  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1007/978-3-319-19551-3_23">doi:10.1007/978-3-319-19551-3_23</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/nc2uvhoe5jecbefyn3di3kzxx4">fatcat:nc2uvhoe5jecbefyn3di3kzxx4</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20170808204918/http://www.cs.ru.nl/~perry/publications/2015/AIME/sokolova2015AIME.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/c7/76/c776151b4c077f15fce783a3fa3c07a4fd0f2719.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1007/978-3-319-19551-3_23"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="external alternate icon"></i> springer.com </button> </a>

Causal Discovery for Manufacturing Domains [article]

Katerina Marazopoulou, Rumi Ghosh, Prasanth Lade, David Jensen
<span title="2016-06-13">2016</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
Specifically, the goal of this work is to learn interpretable causal models from observational data produced by manufacturing lines.  ...  order to improve causal discovery.  ...  We then provide a short introduction to graphical models and causal discovery algorithms.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/1605.04056v2">arXiv:1605.04056v2</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/kqef7cwlqnh6fafjpivgq5jlwu">fatcat:kqef7cwlqnh6fafjpivgq5jlwu</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20200911184328/https://arxiv.org/pdf/1605.04056v2.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/00/40/0040de0b558532d9f965bf5f927cd61c086760a7.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/1605.04056v2" title="arxiv.org access"> <button class="ui compact blue labeled icon button serp-button"> <i class="file alternate outline icon"></i> arxiv.org </button> </a>

GenC: A Fast Tool for Applications Involving Belief Revision

Aaron Hunter, John Agapeyev
<span title="">2020</span> <i title="International Joint Conferences on Artificial Intelligence Organization"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/vfwwmrihanevtjbbkti2kc3nke" style="color: black;">Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence</a> </i> &nbsp;
One important theoretical model of belief revision is the well-known AGM approach.  ...  GenC uses an AllSAT solver and parallel processing to solve revision problems at a rate much faster than existing systems.  ...  One typical example would be causal discovery from observational data.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.24963/ijcai.2020/725">doi:10.24963/ijcai.2020/725</a> <a target="_blank" rel="external noopener" href="https://dblp.org/rec/conf/ijcai/Gong20.html">dblp:conf/ijcai/Gong20</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/5jobqbfnfrh55cn5wwdngw7rdu">fatcat:5jobqbfnfrh55cn5wwdngw7rdu</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20201104004457/https://www.ijcai.org/Proceedings/2020/0725.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/8a/e8/8ae8b2c24050663ed4b0878001646351cd7a0a56.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.24963/ijcai.2020/725"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="external alternate icon"></i> Publisher / doi.org </button> </a>

Discovering Brain Mechanisms Using Network Analysis and Causal Modeling

Matteo Colombo, Naftali Weinberger
<span title="2017-10-25">2017</span> <i title="Springer Nature"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/rplregtfrvbuppbjjbn5xwapiy" style="color: black;">Minds and Machines</a> </i> &nbsp;
Specifically, we examine two quantitative strategies currently used for causal discovery from functional neuroimaging data: dynamic causal modeling and probabilistic graphical modeling.  ...  Little attention has been paid, however, to the use of network analysis and causal modeling techniques for mechanism discovery.  ...  In Sect. 4, we turn our attention to two contemporary quantitative approaches to causal discovery from functional neuroimaging data: dynamic causal modeling and probabilistic graphical modeling.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1007/s11023-017-9447-0">doi:10.1007/s11023-017-9447-0</a> <a target="_blank" rel="external noopener" href="https://www.ncbi.nlm.nih.gov/pubmed/30996522">pmid:30996522</a> <a target="_blank" rel="external noopener" href="https://pubmed.ncbi.nlm.nih.gov/PMC6438494/">pmcid:PMC6438494</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/azvodzrabrcavjac4qqmj32yay">fatcat:azvodzrabrcavjac4qqmj32yay</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20180727234713/https://link.springer.com/content/pdf/10.1007%2Fs11023-017-9447-0.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/90/8c/908cc88c776253c0eec605b5b8b0d99db4c05554.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1007/s11023-017-9447-0"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="external alternate icon"></i> springer.com </button> </a> <a target="_blank" rel="external noopener" href="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6438494" title="pubmed link"> <button class="ui compact blue labeled icon button serp-button"> <i class="file alternate outline icon"></i> pubmed.gov </button> </a>

CausalMGM: an interactive web-based causal discovery tool

Xiaoyu Ge, Vineet K Raghu, Panos K Chrysanthis, Panayiotis V Benos
<span title="2020-05-11">2020</span> <i title="Oxford University Press"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/hfp6p6inqbdexbsu4r7usndpte" style="color: black;">Nucleic Acids Research</a> </i> &nbsp;
Causal discovery methods were developed to infer such cause-and-effect relationships from observational data.  ...  Here, we present CausalMGM (http://causalmgm.org/), the first web-based causal discovery tool that enables researchers to find cause-and-effect relationships from observational data.  ...  ACKNOWLEDGEMENTS We would like to thank Daniel Petrov for his help and insightful comments in the development of CausalMGM web server.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1093/nar/gkaa350">doi:10.1093/nar/gkaa350</a> <a target="_blank" rel="external noopener" href="https://www.ncbi.nlm.nih.gov/pubmed/32392295">pmid:32392295</a> <a target="_blank" rel="external noopener" href="https://pubmed.ncbi.nlm.nih.gov/PMC7319538/">pmcid:PMC7319538</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/wyvody2albfptioh3ughcw7ubu">fatcat:wyvody2albfptioh3ughcw7ubu</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20200513075229/https://watermark.silverchair.com/gkaa350.pdf?token=AQECAHi208BE49Ooan9kkhW_Ercy7Dm3ZL_9Cf3qfKAc485ysgAAAoQwggKABgkqhkiG9w0BBwagggJxMIICbQIBADCCAmYGCSqGSIb3DQEHATAeBglghkgBZQMEAS4wEQQMo_UbTolnlnFDJyukAgEQgIICN2qsPa9hhImCSg-zH5LI3nmgEK1jp3s7jxfw-0f9OUa02EAbMy3G6fokHo9jF2IxhKnHKx6nYp9lbUfgdnxddTYY9GxU5lTgJuSYPqILiXo3ZFFGKE_odLRnvg-MJtLCZo5zOJL0XuRp6jlbbO4m7P_8SLCkdnVLh1cOX4SkmSNH688MTcsMfIhveT29OTHgVa1sFu4YpIrAjE_ezEHSQGDyjQOhb8x4rbuI1omuAKQqhmKdZ8hbOXqQw3d9Jzr4aumCiX3cia1JvERJb-UrvjO83XJ-xip8eIkN4O-5b4XQF4LuimsDZ1cEzXRwiDA-0Np4va1K2RJpijnWE0d6bcPerGmTQmz0pxnvREc0uf19LiazgVxNJwHPCoicqW237gxu-4jc-4NcOfQBPNXqWctLRiTXXXlWhUlHLNKNg11r5xASRz3DSjSqlm_F8bwMTokB1TgIg4OgrU1yWe1IQ7ClZmCTzzg8J-WqtU_PXXoJk25v3ULPrMNQlqaLcnItVH-THjsQdTkOIEA0zucB4Mm_CqfZCB0IM5KNg3EcoQ7xn6zmeTDPnrbcKRD7Kb2hg11nL7C9jgJhy7ZgzrT3WMlrxcuMrG0ubLDwP_H5N_5N0qG9mbsdIQwtWIPez-SJYanWuvQZXdZLsMe5wjIqOoXnffrb8kwT8WO8nQct7Qy6D6GQWwRh0Q3FwYAy-bD29p1bRPprq2JOKPPKdTKOFfzNakR7YfJmlBiY43vt9pUyxoRXMMidIA" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1093/nar/gkaa350"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="unlock alternate icon" style="background-color: #fb971f;"></i> oup.com </button> </a> <a target="_blank" rel="external noopener" href="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7319538" title="pubmed link"> <button class="ui compact blue labeled icon button serp-button"> <i class="file alternate outline icon"></i> pubmed.gov </button> </a>
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