Relevant bibliographies by topics / Sequence data

Academic literature on the topic 'Sequence data'

Author: Grafiati
Published: 4 June 2021
Last updated: 2 February 2022

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Journal articles on the topic "Sequence data"

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HARRIS, T. J. R. "Sequence data." Nature 355, no. 6361 (February 1992): 581. http://dx.doi.org/10.1038/355581c0.

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KENNARD, OLGA. "Sequence data." Nature 314, no. 6011 (April 1985): 492. http://dx.doi.org/10.1038/314492c0.

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Song, Bosheng, Zimeng Li, Xuan Lin, Jianmin Wang, Tian Wang, and Xiangzheng Fu. "Pretraining model for biological sequence data." Briefings in Functional Genomics 20, no. 3 (May 2021): 181–95. http://dx.doi.org/10.1093/bfgp/elab025.

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Abstract With the development of high-throughput sequencing technology, biological sequence data reflecting life information becomes increasingly accessible. Particularly on the background of the COVID-19 pandemic, biological sequence data play an important role in detecting diseases, analyzing the mechanism and discovering specific drugs. In recent years, pretraining models that have emerged in natural language processing have attracted widespread attention in many research fields not only to decrease training cost but also to improve performance on downstream tasks. Pretraining models are used for embedding biological sequence and extracting feature from large biological sequence corpus to comprehensively understand the biological sequence data. In this survey, we provide a broad review on pretraining models for biological sequence data. Moreover, we first introduce biological sequences and corresponding datasets, including brief description and accessible link. Subsequently, we systematically summarize popular pretraining models for biological sequences based on four categories: CNN, word2vec, LSTM and Transformer. Then, we present some applications with proposed pretraining models on downstream tasks to explain the role of pretraining models. Next, we provide a novel pretraining scheme for protein sequences and a multitask benchmark for protein pretraining models. Finally, we discuss the challenges and future directions in pretraining models for biological sequences.
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Baxter, Catherine. "Sequence data wanted!" Nature Reviews Genetics 4, no. 3 (March 2003): 164. http://dx.doi.org/10.1038/nrg1040.

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Biemann, Torsten, and Deepak K. Datta. "Analyzing Sequence Data." Organizational Research Methods 17, no. 1 (September 5, 2013): 51–76. http://dx.doi.org/10.1177/1094428113499408.

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Schwager, Sabine, Dennis Rünger, Robert Gaschler, and Peter Frensch. "Data-driven sequence learning or search: What are the prerequisites for the generation of explicit sequence knowledge?" Advances in Cognitive Psychology 8, no. 2 (June 28, 2012): 132–43. http://dx.doi.org/10.5709/acp-0110-4.

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Biswas, Abhishek, David T. Gauthier, Desh Ranjan, and Mohammad Zubair. "ISQuest: finding insertion sequences in prokaryotic sequence fragment data." Bioinformatics 31, no. 21 (June 27, 2015): 3406–12. http://dx.doi.org/10.1093/bioinformatics/btv388.

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OH, SEUNG-JOON, and JAE-YEARN KIM. "A SEQUENCE-ELEMENT-BASED HIERARCHICAL CLUSTERING ALGORITHM FOR CATEGORICAL SEQUENCE DATA." International Journal of Information Technology & Decision Making 04, no. 01 (March 2005): 81–96. http://dx.doi.org/10.1142/s0219622005001398.

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Recently, there has been enormous growth in the amount of commercial and scientific data, such as protein sequences, retail transactions, and web-logs. Such datasets consist of sequence data that have an inherent sequential nature. However, few existing clustering algorithms consider sequentiality. In this paper, we study how to cluster these sequence datasets. We propose a new similarity measure to compute the similarity between two sequences. In the proposed measure, subsets of a sequence are considered, and the more identical subsets there are, the more similar the two sequences. In addition, we propose a hierarchical clustering algorithm and an efficient method for measuring similarity. Using a splice dataset and synthetic datasets, we show that the quality of clusters generated by our proposed approach is better than that of clusters produced by traditional clustering algorithms.
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Hontzeas, Nikos, and Bernard R. Glick. "Manipulating DNA sequence data." Biotechnology Advances 19, no. 4 (July 2001): 319–20. http://dx.doi.org/10.1016/s0734-9750(01)00057-x.

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Holmes, Edward C., Andrew J. Leigh Brown, and Peter Simmonds. "Sequence data as evidence." Nature 364, no. 6440 (August 1993): 766. http://dx.doi.org/10.1038/364766b0.

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Dissertations / Theses on the topic "Sequence data"

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Chui, Chun-kit, and 崔俊傑. "OLAP on sequence data." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2010. http://hub.hku.hk/bib/B45823996.

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Pray, Keith A. "Apriori Sets And Sequences: Mining Association Rules from Time Sequence Attributes." Link to electronic thesis, 2004. http://www.wpi.edu/Pubs/ETD/Available/etd-0506104-150831/.

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Thesis (M.S.) -- Worcester Polytechnic Institute.
Keywords: mining complex data; temporal association rules; computer system performance; stock market analysis; sleep disorder data. Includes bibliographical references (p. 79-85).
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Brine, A. "Direct sequence data transmission systems." Thesis, University of Kent, 1987. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.379274.

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Zhang, Minghua, and 張明華. "Sequence mining algorithms." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2004. http://hub.hku.hk/bib/B44570119.

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Ibeh, Neke. "Inferring Viral Dynamics from Sequence Data." Thesis, Université d'Ottawa / University of Ottawa, 2016. http://hdl.handle.net/10393/35317.

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One of the primary objectives of infectious disease research is uncovering the direct link that exists between viral population dynamics and molecular evolution. For RNA viruses in particular, evolution occurs at such a rapid pace that epidemiological processes become ingrained into gene sequences. Conceptually, this link is easy to make: as RNA viruses spread throughout a population, they evolve with each new host infection. However, developing a quantitative understanding of this connection is difficult. Thus, the emerging discipline of phylodynamics is centered on reconciling epidemiology and phylogenetics using genetic analysis. Here, we present two research studies that draw on phylodynamic principles in order to characterize the progression and evolution of the Ebola virus and the human immunodefficiency virus (HIV). In the first study, the interplay between selection and epistasis in the Ebola virus genome is elucidated through the ancestral reconstruction of a critical region in the Ebola virus glycoprotein. Hence, we provide a novel mechanistic account of the structural changes that led up to the 2014 Ebola virus outbreak. The second study applies an approximate Bayesian computation (ABC) approach to the inference of epidemiological parameters. First, we demonstrate the accuracy of this approach with simulated data. Then, we infer the dynamics of the Swiss HIV-1 epidemic, illustrating the applicability of this statistical method to the public health sector. Altogether, this thesis unravels some of the complex dynamics that shape epidemic progression, and provides potential avenues for facilitating viral surveillance efforts.
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Parsons, Jeremy David. "Computer analysis of molecular sequences." Thesis, University of Cambridge, 1993. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.282922.

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Hamby, Stephen Edward. "Data mining techniques for protein sequence analysis." Thesis, University of Nottingham, 2010. http://eprints.nottingham.ac.uk/11498/.

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This thesis concerns two areas of bioinformatics related by their role in protein structure and function: protein structure prediction and post translational modification of proteins. The dihedral angles Ψ and Φ are predicted using support vector regression. For the prediction of Ψ dihedral angles the addition of structural information is examined and the normalisation of Ψ and Φ dihedral angles is examined. An application of the dihedral angles is investigated. The relationship between dihedral angles and three bond J couplings determined from NMR experiments is described by the Karplus equation. We investigate the determination of the correct solution of the Karplus equation using predicted Φ dihedral angles. Glycosylation is an important post translational modification of proteins involved in many different facets of biology. The work here investigates the prediction of N-linked and O-linked glycosylation sites using the random forest machine learning algorithm and pairwise patterns in the data. This methodology produces more accurate results when compared to state of the art prediction methods. The black box nature of random forest is addressed by using the trepan algorithm to generate a decision tree with comprehensible rules that represents the decision making process of random forest. The prediction of our program GPP does not distinguish between glycans at a given glycosylation site. We use farthest first clustering, with the idea of classifying each glycosylation site by the sugar linking the glycan to protein. This thesis demonstrates the prediction of protein backbone torsion angles and improves the current state of the art for the prediction of glycosylation sites. It also investigates potential applications and the interpretation of these methods.
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Chung, Jimmy Hok Leung. "Application of sequence prediction to data compression." Thesis, Manchester Metropolitan University, 2000. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.322411.

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Maydt, Jochen. "Analysis of recombination in molecular sequence data." Aachen Shaker, 2008. http://d-nb.info/993318045/04.

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Layton, Martin Ian. "Augmented statistical models for classifying sequence data." Thesis, University of Cambridge, 2007. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.613094.

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Books on the topic "Sequence data"

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Swindell, Simon R., ed. Sequence Data Analysis Guidebook. Totowa, NJ: Humana Press, 1997. http://dx.doi.org/10.1385/0896033589.

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Griffin, Annette M., and Hugh G. Griffin. Computer Analysis of Sequence Data. Totowa, NJ: Humana Press, 1994. http://dx.doi.org/10.1385/0896032760.

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Griffin, Annette M., and Hugh G. Griffin. Computer Analysis of Sequence Data, Part I. New Jersey: Humana Press, 1994. http://dx.doi.org/10.1385/0896032469.

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1960-, Morishita Shinichi, ed. Large-scale genome sequence processing. London: Imperial College Press, 2006.

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Ophir, Frieder, and Martino Robert L, eds. High performance computational methods for biological sequence analysis. Boston: Kluwer Academic Publishers, 1996.

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Sequence analysis in molecular biology: Treasure trove or trivial pursuit. San Diego: Academic Press, 1987.

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Kumar, Pradeep. Pattern discovery using sequence data mining: Applications and studies. Hershey, PA: Information Science Reference, 2012.

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Gromiha, M. Michael. Protein bioinformatics: From sequence to function. Amsterdam: Academic Press/Elsevier, 2010.

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Gore, Neil M. An interface and analysis software tool for image sequence data. Manchester: UMIST, 1997.

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author, Tuimala Jarno, Somervuo Panu author, Huss Mikael author, and Wong Garry author, eds. RNA-seq data analysis: A practical approach. Boca Raton: CRC Press, Taylor & Francis Group, 2015.

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Book chapters on the topic "Sequence data"

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Martin, Eric, Samuel Kaski, Fei Zheng, Geoffrey I. Webb, Xiaojin Zhu, Ion Muslea, Kai Ming Ting, et al. "Sequence Data." In Encyclopedia of Machine Learning, 902. Boston, MA: Springer US, 2011. http://dx.doi.org/10.1007/978-0-387-30164-8_753.

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Parker, Steven R. "Sequence Navigator." In Sequence Data Analysis Guidebook, 145–54. Totowa, NJ: Humana Press, 1997. http://dx.doi.org/10.1385/0-89603-358-9:145.

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Manhart, James R., and Jeff H. Rettig. "Gene Sequence Data." In Caryophyllales, 235–46. Berlin, Heidelberg: Springer Berlin Heidelberg, 1994. http://dx.doi.org/10.1007/978-3-642-78220-6_10.

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O’Sullivan, Christopher, Benjamin Busby, and Ilene Karsch Mizrachi. "Managing Sequence Data." In Methods in Molecular Biology, 79–106. New York, NY: Springer New York, 2016. http://dx.doi.org/10.1007/978-1-4939-6622-6_4.

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Mizrachi, Ilene Karsch. "Managing Sequence Data." In Bioinformatics, 3–27. Totowa, NJ: Humana Press, 2008. http://dx.doi.org/10.1007/978-1-60327-159-2_1.

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Parker, Steven R. "AutoAssembler Sequence Assembly Software." In Sequence Data Analysis Guidebook, 107–17. Totowa, NJ: Humana Press, 1997. http://dx.doi.org/10.1385/0-89603-358-9:107.

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Eisen, Jonathan A. "The Genetic Data Environment." In Sequence Data Analysis Guidebook, 13–38. Totowa, NJ: Humana Press, 1997. http://dx.doi.org/10.1385/0-89603-358-9:13.

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Jennings, W. Bryan. "Developing DNA Sequence Loci." In Phylogenomic Data Acquisition, 195–219. Boca Raton: Taylor & Francis, 2017.: CRC Press, 2016. http://dx.doi.org/10.1201/9781315181431-8.

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Clewley, Jonathan P., and Catherine Arnold. "MEGALIGN." In Sequence Data Analysis Guidebook, 119–29. Totowa, NJ: Humana Press, 1997. http://dx.doi.org/10.1385/0-89603-358-9:119.

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Taylor, Phil. "GeneJockeyII." In Sequence Data Analysis Guidebook, 131–36. Totowa, NJ: Humana Press, 1997. http://dx.doi.org/10.1385/0-89603-358-9:131.

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Conference papers on the topic "Sequence data"

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"PREDICTED RELATIVE METABOLOMIC TURNOVER - Predicting Changes in the Environmental Metabolome from the Metagenome." In Metagenomic Sequence Data Analysis. SciTePress - Science and and Technology Publications, 2011. http://dx.doi.org/10.5220/0003314803370345.

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"AUTOMATIC ANNOTATION OF BACTERIAL COMMUNITY SEQUENCES AND APPLICATION TO INFECTIONS DIAGNOSTIC." In Metagenomic Sequence Data Analysis. SciTePress - Science and and Technology Publications, 2011. http://dx.doi.org/10.5220/0003333703460353.

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"INFRASTRUCTURE FOR METAGENOME DATA MANAGEMENT AND ANALYSIS." In Metagenomic Sequence Data Analysis. SciTePress - Science and and Technology Publications, 2011. http://dx.doi.org/10.5220/0003333803570362.

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"PROPOSAL FOR OPEN DISCUSSION - Informatics Challenges for Next Generation Sequencing Metagenomics Experiments." In Metagenomic Sequence Data Analysis. SciTePress - Science and and Technology Publications, 2011. http://dx.doi.org/10.5220/0003334203630366.

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"ANNOTATING UniProt METAGENOMIC AND ENVIRONMENTAL SEQUENCES IN UniMES." In Metagenomic Sequence Data Analysis. SciTePress - Science and and Technology Publications, 2011. http://dx.doi.org/10.5220/0003350803670368.

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Zhuang, Zhongfang, Xiangnan Kong, Rundensteiner Elke, Jihane Zouaoui, and Aditya Arora. "Attributed Sequence Embedding." In 2019 IEEE International Conference on Big Data (Big Data). IEEE, 2019. http://dx.doi.org/10.1109/bigdata47090.2019.9006481.

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Aalipour, Ghodrat, Pranav Kumar, Santosh Aditham, Trung Nguyen, and Aditya Sood. "Applications of Sequence to Sequence Models for Technical Support Automation." In 2018 IEEE International Conference on Big Data (Big Data). IEEE, 2018. http://dx.doi.org/10.1109/bigdata.2018.8622395.

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Joshi, Akshat, Kinal Mehta, Neha Gupta, and Varun Kannadi Valloli. "Data Generation Using Sequence-to-Sequence." In 2018 IEEE Recent Advances in Intelligent Computational Systems (RAICS). IEEE, 2018. http://dx.doi.org/10.1109/raics.2018.8635052.

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Muralidhar, Nikhil, Sathappan Muthiah, Kiyoshi Nakayama, Ratnesh Sharma, and Naren Ramakrishnan. "Multivariate Long-Term State Forecasting in Cyber-Physical Systems: A Sequence to Sequence Approach." In 2019 IEEE International Conference on Big Data (Big Data). IEEE, 2019. http://dx.doi.org/10.1109/bigdata47090.2019.9005511.

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Rallapalli, Swati, Liang Ma, Mudhakar Srivatsa, Ananthram Swami, Heesung Kwon, Graham Bent, and Christopher Simpkin. "SENSE: Semantically Enhanced Node Sequence Embedding." In 2019 IEEE International Conference on Big Data (Big Data). IEEE, 2019. http://dx.doi.org/10.1109/bigdata47090.2019.9006591.

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Reports on the topic "Sequence data"

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Pindzola, M. S., D. C. Griffin, C. Bottcher, S. M. Younger, and H. T. Hunter. Electron-impact ionization data for the Fe isonuclear sequence. Office of Scientific and Technical Information (OSTI), November 1987. http://dx.doi.org/10.2172/5706600.

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Alagarsamy, Jeyashree, Ramya Seetharaman, Chandramouli Sivanandham, Karthickeyan Chella Krishnan, and Ross Overbeek. Connecting Sequence Data to Virulence Factors in Streptococcus Genomes. Office of Scientific and Technical Information (OSTI), March 2014. http://dx.doi.org/10.2172/1171190.

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Pindzola, M. S., D. C. Griffin, G. C. Bottcher, M. J. Buie, and D. C. Gregory. Electron-impact ionization data for the nickel isonuclear sequence. Office of Scientific and Technical Information (OSTI), March 1990. http://dx.doi.org/10.2172/6849742.

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Faber-Hammond, Joshua, and Kim Brown. Data From: Anchored Pseudo-De Novo Assembly of Human Genomes Identifies Extensive Sequence Variation from Unmapped Sequence Reads. Portland State University, 2015. http://dx.doi.org/10.15760/data.1.

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Li, Peter Wei-Der. Sequence modelling and an extensible data model for genomic database. Office of Scientific and Technical Information (OSTI), January 1992. http://dx.doi.org/10.2172/7045141.

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Sexton, Francis W. A Quick and Simple Database for Comparing Toxin Sequence Data. Fort Belvoir, VA: Defense Technical Information Center, April 1990. http://dx.doi.org/10.21236/ada221309.

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Li, Peter Wei-Der. Sequence modelling and an extensible data model for genomic database. Office of Scientific and Technical Information (OSTI), January 1992. http://dx.doi.org/10.2172/10159439.

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Liberty, Lee, T. Mikesell, and Spencer Wilbur. Data Analysis of the 2020 Central Idaho Mainshock-Aftershock Sequence. Office of Scientific and Technical Information (OSTI), July 2021. http://dx.doi.org/10.2172/1813740.

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Safta, Cosmin, Kookjin Lee, and Jaideep Ray. Predictive Skill of Deep Learning Models Trained on Limited Sequence Data. Office of Scientific and Technical Information (OSTI), October 2020. http://dx.doi.org/10.2172/1688570.

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Gray, H. L., Wayne A. Woodward, and Suojin Wang. Testing the Consistency of Soviet Data Using a Sequence of Hypothesis Tests. Fort Belvoir, VA: Defense Technical Information Center, September 1990. http://dx.doi.org/10.21236/ada241711.

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