Relevant bibliographies by topics / Automated Scientific Discovery

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Automated Scientific Discovery'

Author: Grafiati
Published: 4 June 2021
Last updated: 12 February 2022

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Journal articles on the topic "Automated Scientific Discovery"

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Hakuk, Yaron, and Yoram Reich. "Automated discovery of scientific concepts: Replicating three recent discoveries in mechanics." Advanced Engineering Informatics 44 (April 2020): 101080. http://dx.doi.org/10.1016/j.aei.2020.101080.

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Yang, Lusann, Joel A. Haber, Zan Armstrong, et al. "Discovery of complex oxides via automated experiments and data science." Proceedings of the National Academy of Sciences 118, no. 37 (2021): e2106042118. http://dx.doi.org/10.1073/pnas.2106042118.

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The quest to identify materials with tailored properties is increasingly expanding into high-order composition spaces, with a corresponding combinatorial explosion in the number of candidate materials. A key challenge is to discover regions in composition space where materials have novel properties. Traditional predictive models for material properties are not accurate enough to guide the search. Herein, we use high-throughput measurements of optical properties to identify novel regions in three-cation metal oxide composition spaces by identifying compositions whose optical trends cannot be ex
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Khan, Fakhri Alam, Sardar Hussain, Ivan Janciak, and Peter Brezany. "Towards Next Generation Provenance Systems for e-Science." International Journal of Information System Modeling and Design 2, no. 3 (2011): 24–48. http://dx.doi.org/10.4018/jismd.2011070102.

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e-Science helps scientists to automate scientific discovery processes and experiments, and promote collaboration across organizational boundaries and disciplines. These experiments involve data discovery, knowledge discovery, integration, linking, and analysis through different software tools and activities. Scientific workflow is one technique through which such activities and processes can be interlinked, automated, and ultimately shared amongst the collaborating scientists. Workflows are realized by the workflow enactment engine, which interprets the process definition and interacts with th
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Ieong, Pek U., Jesper Sørensen, Prasantha L. Vemu, et al. "Progress towards Automated Kepler Scientific Workflows for Computer-aided Drug Discovery and Molecular Simulations." Procedia Computer Science 29 (2014): 1745–55. http://dx.doi.org/10.1016/j.procs.2014.05.159.

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Ren, Kaijun, Jinjun Chen, Nong Xiao, and Junqiang Song. "Building Quick Service Query list (QSQL) to support automated service discovery for scientific workflow." Concurrency and Computation: Practice and Experience 21, no. 16 (2009): 2099–117. http://dx.doi.org/10.1002/cpe.1449.

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Choi, Byung-Kwon, Tajhal Dayaram, Neha Parikh, et al. "Literature-based automated discovery of tumor suppressor p53 phosphorylation and inhibition by NEK2." Proceedings of the National Academy of Sciences 115, no. 42 (2018): 10666–71. http://dx.doi.org/10.1073/pnas.1806643115.

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Scientific progress depends on formulating testable hypotheses informed by the literature. In many domains, however, this model is strained because the number of research papers exceeds human readability. Here, we developed computational assistance to analyze the biomedical literature by reading PubMed abstracts to suggest new hypotheses. The approach was tested experimentally on the tumor suppressor p53 by ranking its most likely kinases, based on all available abstracts. Many of the best-ranked kinases were found to bind and phosphorylate p53 (P value = 0.005), suggesting six likely p53 kina
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Thilakaratne, Menasha, Katrina Falkner, and Thushari Atapattu. "A systematic review on literature-based discovery workflow." PeerJ Computer Science 5 (November 18, 2019): e235. http://dx.doi.org/10.7717/peerj-cs.235.

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As scientific publication rates increase, knowledge acquisition and the research development process have become more complex and time-consuming. Literature-Based Discovery (LBD), supporting automated knowledge discovery, helps facilitate this process by eliciting novel knowledge by analysing existing scientific literature. This systematic review provides a comprehensive overview of the LBD workflow by answering nine research questions related to the major components of the LBD workflow (i.e., input, process, output, and evaluation). With regards to theinputcomponent, we discuss the data types
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Giza, Piotr. "Sign use and cognition in automated scientific discovery: are computers only special kinds of signs?" International Journal of General Systems 47, no. 3 (2017): 193–207. http://dx.doi.org/10.1080/03081079.2017.1414209.

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Hassan, Amr, and Christopher J. Fluke. "Scientific Visualization in Astronomy: Towards the Petascale Astronomy Era." Publications of the Astronomical Society of Australia 28, no. 2 (2011): 150–70. http://dx.doi.org/10.1071/as10031.

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AbstractAstronomy is entering a new era of discovery, coincident with the establishment of new facilities for observation and simulation that will routinely generate petabytes of data. While an increasing reliance on automated data analysis is anticipated, a critical role will remain for visualization-based knowledge discovery. We have investigated scientific visualization applications in astronomy through an examination of the literature published during the last two decades. We identify the two most active fields for progress — visualization of large-N particle data and spectral data cubes—d
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Karl, Jason W. "Mining location information from life- and earth-sciences studies to facilitate knowledge discovery." Journal of Librarianship and Information Science 51, no. 4 (2018): 1007–21. http://dx.doi.org/10.1177/0961000618759413.

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Location information in published studies represents an untapped resource for literature discovery, applicable to a range of domains. The ability to easily discover scientific articles from specific places, nearby locales, or similar (but geographically separate) areas worldwide is important for advancing science and addressing global sustainability challenges. However, the thematic and not geographic nature of current search tools makes location-based searches challenging and inefficient. Manually geolocating studies is labor intensive, and place-name recognition algorithms have performed poo
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Dissertations / Theses on the topic "Automated Scientific Discovery"

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Shea-Blymyer, Colin Russel. "Distinguishing Dynamical Kinds: An Approach for Automating Scientific Discovery." Thesis, Virginia Tech, 2019. http://hdl.handle.net/10919/101659.

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The automation of scientific discovery has been an active research topic for many years. The promise of a formalized approach to developing and testing scientific hypotheses has attracted researchers from the sciences, machine learning, and philosophy alike. Leveraging the concept of dynamical symmetries a new paradigm is proposed for the collection of scientific knowledge, and algorithms are presented for the development of EUGENE – an automated scientific discovery tool-set. These algorithms have direct applications in model validation, time series analysis, and system identification. Furthe
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Books on the topic "Automated Scientific Discovery"

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Bringsjord, Selmer, and Andrew Shilliday. Automated Scientific Discovery: Papers from the AAAI Fall Symposium. AAAI Press, 2009.

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Book chapters on the topic "Automated Scientific Discovery"

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Ray, Oliver. "Automated Abduction in Scientific Discovery." In Model-Based Reasoning in Science, Technology, and Medicine. Springer Berlin Heidelberg, 2007. http://dx.doi.org/10.1007/978-3-540-71986-1_5.

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Sterpetti, Fabio. "Mathematical Proofs and Scientific Discovery." In A Critical Reflection on Automated Science. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-25001-0_6.

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Pafilis, Evangelos, Heiko Horn, and Nigel P. Brown. "Automated Annotation of Scientific Documents: Increasing Access to Biological Knowledge." In Biological Knowledge Discovery Handbook. John Wiley & Sons, Inc., 2013. http://dx.doi.org/10.1002/9781118617151.ch38.

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Hajlaoui, Kafil, Pascal Cuxac, Jean-Charles Lamirel, and Claire François. "Enhancing Patent Expertise through Automatic Matching with Scientific Papers." In Discovery Science. Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-33492-4_24.

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Addis, Mark, Fernand Gobet, Peter C. R. Lane, and Peter D. Sozou. "Semi-Automatic Generation of Cognitive Science Theories." In Scientific Discovery in the Social Sciences. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-23769-1_10.

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Tamaddoni-Nezhad, Alireza, David Bohan, Ghazal Afroozi Milani, Alan Raybould, and Stephen Muggleton. "Human–Machine Scientific Discovery." In Human-Like Machine Intelligence. Oxford University Press, 2021. http://dx.doi.org/10.1093/oso/9780198862536.003.0015.

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Humanity is facing existential, societal challenges related to food security, ecosystem conservation, antimicrobial resistance, etc, and Artificial Intelligence (AI) is already playing an important role in tackling these new challenges. Most current AI approaches are limited when it comes to ‘knowledge transfer’ with humans, i.e. it is difficult to incorporate existing human knowledge and also the output knowledge is not human comprehensible. In this chapter we demonstrate how a combination of comprehensible machine learning, text-mining and domain knowledge could enhance human-machine collaboration for the purpose of automated scientific discovery where humans and computers jointly develop and evaluate scientific theories. As a case study, we describe a combination of logic-based machine learning (which included human-encoded ecological background knowledge) and text-mining from scientific publications (to verify machine-learned hypotheses) for the purpose of automated discovery of ecological interaction networks (food-webs) to detect change in agricultural ecosystems using the Farm Scale Evaluations (FSEs) of genetically modified herbicide-tolerant (GMHT) crops dataset. The results included novel food-web hypotheses, some confirmed by subsequent experimental studies (e.g. DNA analysis) and published in scientific journals. These machine-leaned food-webs were also used as the basis of a recent study revealing resilience of agro-ecosystems to changes in farming management using GMHT crops.
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Khan, Fakhri Alam, Sardar Hussain, Ivan Janciak, and Peter Brezany. "Towards Next Generation Provenance Systems for E-Science." In Frameworks for Developing Efficient Information Systems. IGI Global, 2013. http://dx.doi.org/10.4018/978-1-4666-4161-7.ch003.

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e-Science helps scientists to automate scientific discovery processes and experiments, and promote collaboration across organizational boundaries and disciplines. These experiments involve data discovery, knowledge discovery, integration, linking, and analysis through different software tools and activities. Scientific workflow is one technique through which such activities and processes can be interlinked, automated, and ultimately shared amongst the collaborating scientists. Workflows are realized by the workflow enactment engine, which interprets the process definition and interacts with the workflow participants. Since workflows are typically executed on a shared and distributed infrastructure, the information on the workflow activities, data processed, and results generated (also known as provenance), needs to be recorded in order to be reproduced and reused. A range of solutions and techniques have been suggested for the provenance of data collection and analysis; however, these are predominantly workflow enactment engine and domain dependent. This paper includes taxonomy of existing provenance techniques and a novel solution named VePS (The Vienna e-Science Provenance System) for e-Science provenance collection.
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Fredericks, Janet. "Persistence of Knowledge across Layered Architectures." In Collaborative Knowledge in Scientific Research Networks. IGI Global, 2015. http://dx.doi.org/10.4018/978-1-4666-6567-5.ch013.

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In this chapter, a model demonstrating methods for integration of semantic technologies within observational data services is described. Implementation of the model captures knowledge about data provenance where it is best understood and also enables its persistence across architectural layers through the use of standards-based technologies. Domain experts can build upon the semantic layer to create meaningful ontologies. Brokering services can utilize the ontologies for automated mediation of terms and translation between standards-based technologies. Research communities will be enabled to operate within their own framework, utilizing their familiar, specialized terminology and tools. The role of communities of practice is explored relating to knowledge management across layered architectures. Implementation of semantic technologies within Web-based data and brokering services will minimize the operational barriers to data discovery and access and provide mechanisms that enable the formation of collaborative environments that will facilitate repeatable, well-documented research.
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Verykios, Vassilios S. "Association Rule Hiding Methods." In Database Technologies. IGI Global, 2009. http://dx.doi.org/10.4018/978-1-60566-058-5.ch138.

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The enormous expansion of data collection and storage facilities has created an unprecedented increase in the need for data analysis and processing power. Data mining has long been the catalyst for automated and sophisticated data analysis and interrogation. Recent advances in data mining and knowledge discovery have generated controversial impact in both scientific and technological arenas. On the one hand, data mining is capable of analyzing vast amounts of information within a minimum amount of time, an analysis that has exceeded the expectations of even the most imaginative scientists of the last decade. On the other hand, the excessive processing power of intelligent algorithms which is brought with this new research area puts at risk sensitive and confidential information that resides in large and distributed data stores.
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Brezany, Peter, Ivan Janciak, and A. Min Tjoa. "Ontology-Based Construction of Grid Data Mining Workflows." In Data Warehousing and Mining. IGI Global, 2008. http://dx.doi.org/10.4018/978-1-59904-951-9.ch054.

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This chapter introduces an ontology-based framework for automated construction of complex interactive data mining workflows as a means of improving productivity of Grid-enabled data exploration systems. The authors first characterize existing manual and automated workflow composition approaches and then present their solution called GridMiner Assistant (GMA), which addresses the whole life cycle of the knowledge discovery process. GMA is specified in the OWL language and is being developed around a novel data mining ontology, which is based on concepts of industry standards like the Predictive Model Markup Language, Cross Industry Standard Process for Data Mining and Java Data Mining API. The ontology introduces basic data mining concepts like data mining elements, tasks, services, etc. In addition, conceptual and implementation architectures of the framework are presented and its application to an example taken from the medical domain is illustrated. The authors hope that the further research and development of this framework can lead to productivity improvements, which can have significant impact on many real-life spheres. For example, it can be a crucial factor in achievement of scientific discoveries, optimal treatment of patients, productive decision making, cutting costs, etc.
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Conference papers on the topic "Automated Scientific Discovery"

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Spangler, Scott, Angela D. Wilkins, Benjamin J. Bachman, et al. "Automated hypothesis generation based on mining scientific literature." In KDD '14: The 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. ACM, 2014. http://dx.doi.org/10.1145/2623330.2623667.

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Egan, Paul, Jonathan Cagan, Christian Schunn, Philip LeDuc, Jeffrey Moore, and Felix Chiu. "The D3 Science-to-Design Methodology: Automated and Cognitive-Based Processes for Discovering, Describing, and Designing Complex Nanomechanical Biosystems." In ASME 2015 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. American Society of Mechanical Engineers, 2015. http://dx.doi.org/10.1115/detc2015-47466.

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New opportunities in design often surface with scientific advances, however, the rapid pace of scientific findings in biological domains, and their complexity, may impede effective technological design. This paper addresses such challenges through weaving phases of scientific discovery, analytical description, and technological design in an integrative “d3 Methodology.” The method is implemented using human-guided automated processes developed with cognitive-based considerations. A case study of designing myosin bio-libraries is specifically investigated, and optimization results suggest that
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Reports on the topic "Automated Scientific Discovery"

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Osherson, Daniel, and Scott Weinstein. On the Automated Discovery of Scientific Theories. Defense Technical Information Center, 1992. http://dx.doi.org/10.21236/ada247046.

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Serbin, Shawn, Scott Giangrande, Chongai Kuang, Nathan Urban, and Line Pouchard. AI to Automate ModEx for Optimal Predictive Improvement and Scientific Discovery. Office of Scientific and Technical Information (OSTI), 2021. http://dx.doi.org/10.2172/1769662.

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