Literatura académica sobre el tema "GENOMIC LANGUAGE PROCESSING"
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Artículos de revistas sobre el tema "GENOMIC LANGUAGE PROCESSING"
Routhier, Etienne y Julien Mozziconacci. "Genomics enters the deep learning era". PeerJ 10 (24 de junio de 2022): e13613. http://dx.doi.org/10.7717/peerj.13613.
Texto completoKehl, Kenneth L., Wenxin Xu, Eva Lepisto, Haitham Elmarakeby, Michael J. Hassett, Eliezer M. Van Allen, Bruce E. Johnson y Deborah Schrag. "Natural Language Processing to Ascertain Cancer Outcomes From Medical Oncologist Notes". JCO Clinical Cancer Informatics, n.º 4 (septiembre de 2020): 680–90. http://dx.doi.org/10.1200/cci.20.00020.
Texto completoSchubert, Michael. "clustermq enables efficient parallelization of genomic analyses". Bioinformatics 35, n.º 21 (27 de mayo de 2019): 4493–95. http://dx.doi.org/10.1093/bioinformatics/btz284.
Texto completoLe Guen, Yann, François Leroy, Cathy Philippe, Jean-François Mangin, Ghislaine Dehaene-Lambertz y Vincent Frouin. "Enhancer Locus in ch14q23.1 Modulates Brain Asymmetric Temporal Regions Involved in Language Processing". Cerebral Cortex 30, n.º 10 (20 de mayo de 2020): 5322–32. http://dx.doi.org/10.1093/cercor/bhaa112.
Texto completoKonstantinidis, George, Adriane Chapman, Mark J. Weal, Ahmed Alzubaidi, Lisa M. Ballard y Anneke M. Lucassen. "The Need for Machine-Processable Agreements in Health Data Management". Algorithms 13, n.º 4 (7 de abril de 2020): 87. http://dx.doi.org/10.3390/a13040087.
Texto completoGuan, Meijian, Samuel Cho, Robin Petro, Wei Zhang, Boris Pasche y Umit Topaloglu. "Natural language processing and recurrent network models for identifying genomic mutation-associated cancer treatment change from patient progress notes". JAMIA Open 2, n.º 1 (3 de enero de 2019): 139–49. http://dx.doi.org/10.1093/jamiaopen/ooy061.
Texto completoMiyano, Satoru. "IL-3 Changing Cancer Genomics and Cancer Genomic Medicine by Artificial Intelligence and Large-Scale Data Analysis". Neuro-Oncology Advances 3, Supplement_6 (1 de diciembre de 2021): vi1. http://dx.doi.org/10.1093/noajnl/vdab159.002.
Texto completoGarzon, Max H., Kiran C. Bobba, Andrew Neel y Vinhthuy Phan. "DNA-Based Indexing". International Journal of Nanotechnology and Molecular Computation 2, n.º 3 (julio de 2010): 25–45. http://dx.doi.org/10.4018/jnmc.2010070102.
Texto completoKlein, Harry, Tali Mazor, Matthew Galvin, Jason Hansel, Emily Mallaber, Pavel Trukhanov, Joyce Yu et al. "Abstract 1067: MatchMiner: An open-source AI precision medicine trial matching platform". Cancer Research 83, n.º 7_Supplement (4 de abril de 2023): 1067. http://dx.doi.org/10.1158/1538-7445.am2023-1067.
Texto completoGraves, Jordan, Jacob Byerly, Eduardo Priego, Naren Makkapati, S. Vince Parish, Brenda Medellin y Monica Berrondo. "A Review of Deep Learning Methods for Antibodies". Antibodies 9, n.º 2 (28 de abril de 2020): 12. http://dx.doi.org/10.3390/antib9020012.
Texto completoTesis sobre el tema "GENOMIC LANGUAGE PROCESSING"
Dyremark, Johanna y Caroline Mayer. "Bedömning av elevuppsatser genom maskininlärning". Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-262041.
Texto completoToday, a large amount of a teacher’s workload is comprised of essay scoring and there is a large variability between teachers’ gradings. This report aims to examine what accuracy can be acceived with an automated essay scoring system for Swedish. Three following machine learning models for classification are trained and tested with 5-fold cross-validation on essays from Swedish national tests: Linear Discriminant Analysis, K-Nearest Neighbour and Random Forest. Essays are classified based on 31 language structure related attributes such as token-based length measures, similarity to texts with different formal levels and use of grammar. The results show a maximal quadratic weighted kappa value of 0.4829 and a grading identical to expert’s assessment in 57.53% of all tests. These results were achieved by a model based on Linear Discriminant Analysis and showed higher inter-rater reliability with expert grading than a local teacher. Despite an ongoing digitilization within the Swedish educational system, there are a number of obstacles preventing a complete automization of essay scoring such as users’ attitude, ethical issues and the current techniques difficulties in understanding semantics. Nevertheless, a partial integration of automatic essay scoring has potential to effectively identify essays suitable for double grading which can increase the consistency of large-scale tests to a low cost.
Akhurst, Timothy John. "The role of parallel computing in bioinformatics". Thesis, Rhodes University, 2005. http://eprints.ru.ac.za/162/.
Texto completoCHAKRABORTY, RAJKUMAR. "GENOMIC LANGUAGE PROCESSING USING MACHINE LEARNING". Thesis, 2023. http://dspace.dtu.ac.in:8080/jspui/handle/repository/20063.
Texto completoLibros sobre el tema "GENOMIC LANGUAGE PROCESSING"
Dwyer, Rex A. Genomic Perl: From bioinformatics basics to working code. Cambridge: Cambridge University Press, 2003.
Buscar texto completoIan, Korf, ed. UNIX and Perl to the rescue!: A field guide for the life sciences (and other data-rich pursuits). New York: Cambridge University Press, 2012.
Buscar texto completoGenomic Perl. Cambridge University Press, 2003.
Buscar texto completoDwyer, Rex A. Genomic Perl: From Bioinformatics Basics to Working Code. Cambridge University Press, 2002.
Buscar texto completoDwyer, Rex A. Genomic Perl: From Bioinformatics Basics to Working Code. Cambridge University Press, 2002.
Buscar texto completoDwyer, Rex A. Genomic Perl: From Bioinformatics Basics to Working Code. Cambridge University Press, 2002.
Buscar texto completoDwyer, Rex A. Genomic Perl: From Bioinformatics Basics to Working Code. Cambridge University Press, 2012.
Buscar texto completoAkalin, Altuna. Computational Genomics with R. Taylor & Francis Group, 2020.
Buscar texto completoAkalin, Altuna. Computational Genomics with R. Taylor & Francis Group, 2020.
Buscar texto completoAkalin, Altuna. Computational Genomics with R. Taylor & Francis Group, 2020.
Buscar texto completoCapítulos de libros sobre el tema "GENOMIC LANGUAGE PROCESSING"
Botsis, Taxiarchis, Joseph Murray, Alessandro Leal, Doreen Palsgrove, Wei Wang, James R. White, Victor E. Velculescu y Valsamo Anagnostou. "Natural Language Processing Approaches for Retrieval of Clinically Relevant Genomic Information in Cancer". En Studies in Health Technology and Informatics. IOS Press, 2022. http://dx.doi.org/10.3233/shti220735.
Texto completoYee, David P. y Tim Hunkapiller. "Overview: A System for Tracking and Managing the Results from Sequence Comparison Programs". En Pattern Discovery in Biomolecular Data. Oxford University Press, 1999. http://dx.doi.org/10.1093/oso/9780195119404.003.0017.
Texto completoLussier, Yves A. "Ontologies for natural language processing". En Encyclopedia of Genetics, Genomics, Proteomics and Bioinformatics. Chichester: John Wiley & Sons, Ltd, 2005. http://dx.doi.org/10.1002/047001153x.g408212.
Texto completoDar, Gowhar Mohiuddin, Ashok Sharma y Parveen Singh. "Deep Learning Models for Detection and Diagnosis of Alzheimer's Disease". En Advances in Medical Technologies and Clinical Practice, 140–49. IGI Global, 2021. http://dx.doi.org/10.4018/978-1-7998-7188-0.ch011.
Texto completoNagarajan, Srikantan S., Kamalini G. Ranasinghe y Keith A. Vossel. "Brain Imaging With Magnetoencephalography During Rest and During Speech and Language Processing". En Genomics, Circuits, and Pathways in Clinical Neuropsychiatry, 233–45. Elsevier, 2016. http://dx.doi.org/10.1016/b978-0-12-800105-9.00015-9.
Texto completoRaychaudhuri, Soumya. "Textual Profiles of Genes". En Computational Text Analysis. Oxford University Press, 2006. http://dx.doi.org/10.1093/oso/9780198567400.003.0010.
Texto completoRaychaudhuri, Soumya. "Finding Gene Names". En Computational Text Analysis. Oxford University Press, 2006. http://dx.doi.org/10.1093/oso/9780198567400.003.0016.
Texto completoActas de conferencias sobre el tema "GENOMIC LANGUAGE PROCESSING"
Cao, Jiarun, Niels Peek, Andrew Renehan y Sophia Ananiadou. "Gaussian Distributed Prototypical Network for Few-shot Genomic Variant Detection". En The 22nd Workshop on Biomedical Natural Language Processing and BioNLP Shared Tasks. Stroudsburg, PA, USA: Association for Computational Linguistics, 2023. http://dx.doi.org/10.18653/v1/2023.bionlp-1.2.
Texto completoDordiuk, Vladislav, Ekaterina Demicheva, Fernando Polanco Espino y Konstantin Ushenin. "Natural language processing for clusterization of genes according to their functions". En 2022 Ural-Siberian Conference on Computational Technologies in Cognitive Science, Genomics and Biomedicine (CSGB). IEEE, 2022. http://dx.doi.org/10.1109/csgb56354.2022.9865330.
Texto completoCahyawijaya, Samuel, Tiezheng Yu, Zihan Liu, Xiaopu Zhou, Tze Wing Tiffany Mak, Yuk Yu Nancy Ip y Pascale Fung. "SNP2Vec: Scalable Self-Supervised Pre-Training for Genome-Wide Association Study". En Proceedings of the 21st Workshop on Biomedical Language Processing. Stroudsburg, PA, USA: Association for Computational Linguistics, 2022. http://dx.doi.org/10.18653/v1/2022.bionlp-1.14.
Texto completoMansouri Ghiasi, Nika, Jisung Park, Harun Mustafa, Jeremie Kim, Ataberk Olgun, Arvid Gollwitzer, Damla Senol Cali et al. "GenStore: a high-performance in-storage processing system for genome sequence analysis". En ASPLOS '22: 27th ACM International Conference on Architectural Support for Programming Languages and Operating Systems. New York, NY, USA: ACM, 2022. http://dx.doi.org/10.1145/3503222.3507702.
Texto completoGrechkin, Maxim, Hoifung Poon y Bill Howe. "EZLearn: Exploiting Organic Supervision in Automated Data Annotation". En Twenty-Seventh International Joint Conference on Artificial Intelligence {IJCAI-18}. California: International Joint Conferences on Artificial Intelligence Organization, 2018. http://dx.doi.org/10.24963/ijcai.2018/568.
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