Literatura académica sobre el tema "Genomic classification"
Crea una cita precisa en los estilos APA, MLA, Chicago, Harvard y otros
Consulte las listas temáticas de artículos, libros, tesis, actas de conferencias y otras fuentes académicas sobre el tema "Genomic classification".
Junto a cada fuente en la lista de referencias hay un botón "Agregar a la bibliografía". Pulsa este botón, y generaremos automáticamente la referencia bibliográfica para la obra elegida en el estilo de cita que necesites: APA, MLA, Harvard, Vancouver, Chicago, etc.
También puede descargar el texto completo de la publicación académica en formato pdf y leer en línea su resumen siempre que esté disponible en los metadatos.
Artículos de revistas sobre el tema "Genomic classification"
Murthy, H. N., S. C. Hiremath y A. N. Pyati. "Genomic Classification in Guizotia (Asteraceae)." CYTOLOGIA 60, n.º 1 (1995): 67–73. http://dx.doi.org/10.1508/cytologia.60.67.
Texto completoAkbani, Rehan, Kadir C. Akdemir, B. Arman Aksoy, Monique Albert, Adrian Ally, Samirkumar B. Amin, Harindra Arachchi et al. "Genomic Classification of Cutaneous Melanoma". Cell 161, n.º 7 (junio de 2015): 1681–96. http://dx.doi.org/10.1016/j.cell.2015.05.044.
Texto completoDoranga, Saroj, Rajeev Nepal y Pratigya Timsina. "Automated Classification of Genetic Mutations in Cancer using Machine Learning". Scientific Researches in Academia 1, n.º 1 (23 de noviembre de 2023): 108–23. http://dx.doi.org/10.3126/sra.v1i1.60140.
Texto completoFaillot, Simon, Thomas Foulonneau, Mario Néou, Stéphanie Espiard, Simon Garinet, Anna Vaczlavik, Anne Jouinot et al. "Genomic classification of benign adrenocortical lesions". Endocrine-Related Cancer 28, n.º 1 (enero de 2021): 79–95. http://dx.doi.org/10.1530/erc-20-0128.
Texto completoOrnella, L., P. Pérez, E. Tapia, J. M. González-Camacho, J. Burgueño, X. Zhang, S. Singh et al. "Genomic-enabled prediction with classification algorithms". Heredity 112, n.º 6 (15 de enero de 2014): 616–26. http://dx.doi.org/10.1038/hdy.2013.144.
Texto completoSpino, Marissa y Matija Snuderl. "Genomic Molecular Classification of CNS Malignancies". Advances In Anatomic Pathology 27, n.º 1 (enero de 2020): 44–50. http://dx.doi.org/10.1097/pap.0000000000000254.
Texto completoGraur, Dan, Yichen Zheng y Ricardo B. R. Azevedo. "An Evolutionary Classification of Genomic Function". Genome Biology and Evolution 7, n.º 3 (28 de enero de 2015): 642–45. http://dx.doi.org/10.1093/gbe/evv021.
Texto completoKundra, Ritika, Hongxin Zhang, Robert Sheridan, Sahussapont Joseph Sirintrapun, Avery Wang, Angelica Ochoa, Manda Wilson et al. "OncoTree: A Cancer Classification System for Precision Oncology". JCO Clinical Cancer Informatics, n.º 5 (marzo de 2021): 221–30. http://dx.doi.org/10.1200/cci.20.00108.
Texto completoKim, Jong-Won. "Diagnostic Classification and Genomic Analyses of Cancer". Laboratory Medicine Online 11, n.º 4 (1 de octubre de 2021): 223–29. http://dx.doi.org/10.47429/lmo.2021.11.4.223.
Texto completoJoly, Yann, Hilary Burton, Bartha Maria Knoppers, Ida Ngueng Feze, Tom Dent, Nora Pashayan, Susmita Chowdhury et al. "Life insurance: genomic stratification and risk classification". European Journal of Human Genetics 22, n.º 5 (16 de octubre de 2013): 575–79. http://dx.doi.org/10.1038/ejhg.2013.228.
Texto completoTesis sobre el tema "Genomic classification"
Pfaff, Florian [Verfasser]. "Expanding the virosphere : advanced genomic classification / Florian Pfaff". Greifswald : Universitätsbibliothek Greifswald, 2017. http://d-nb.info/114441251X/34.
Texto completoSonnhammer, Erik Leonard Laage. "Classification of protein domain families for genomic sequence analysis". Thesis, Open University, 1996. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.336799.
Texto completoStone, Thomas John. "Genomic classification and analysis of epilepsy-associated glioneuronal tumours". Thesis, University College London (University of London), 2017. http://discovery.ucl.ac.uk/10037593/.
Texto completoHua, Jianping. "Topics in genomic image processing". Texas A&M University, 2004. http://hdl.handle.net/1969.1/3244.
Texto completoSaluja, Sunil K. (Sunil Kumar) 1968. "A computational framework for the identification, cataloging, and classification of evolutionary conserved genomic DNA". Thesis, Massachusetts Institute of Technology, 2004. http://hdl.handle.net/1721.1/28590.
Texto completoIncludes bibliographical references (leaves 27-29).
Evolutionarily conserved genomic regions (ecores) are understudied, and yet comprise a very large percentage of the Human Genome. Highly conserved human-mouse non-coding ecores, for example, are more abundant within the Human Genome than those regions, which are currently estimated to encode for proteins. Subsets of these ecores also exhibit conservation that extends across several species. These genomic regions have managed to survive millions of years of evolution despite the fact that they do not appear to directly encode for proteins. The survival of these regions compels us to investigate their potential function. Development of a computational framework for the classification and clustering of these regions may be the first step in understanding their function. The need for a standardized framework is underscored by the explosive growth in the number of publicly available, fully sequenced genomes, and the diverse set of methodologies used to generate cross-species alignments. This project describes the design and implementation of a system for the identification, classification and cataloguing of ecores across multiple species. A key feature of this system is its ability to quickly incorporate new genomes and assemblies as they become available. Additionally, this system provides investigators with a feature rich user interface, which facilitates the retrieval of ecores based on a wide range of parameters. The system returns a dynamically annotated list of evolutionarily conserved regions, which is used as input to several classification schemes, aimed at identifying families of ecores that share similar features, including depth of evolutionary conservation, position relative to known genes, sequence similarity,
(cont.) and content of transcription factor binding sites. Families of ecores have already been retrieved by the system and clustered using this feature space, and are currently awaiting biological validation.
by Sunil K. Saluja.
S.M.
Sharma, Jason P. (Jason Poonam) 1979. "Classification performance of support vector machines on genomic data utilizing feature space selection techniques". Thesis, Massachusetts Institute of Technology, 2002. http://hdl.handle.net/1721.1/87830.
Texto completoStagni, Camilla. "Genomic analysis in cutaneous melanoma: a tool for predictive biomarker identification and molecular classification". Doctoral thesis, Università degli studi di Padova, 2017. http://hdl.handle.net/11577/3426683.
Texto completoProgetto 1: identificazione di signatures molecolari associate alla risposta al trattamento con inibitori del MAPK pathway. I melanomi portatori di una mutazione nel codone V600 del gene BRAF rispondono agli inibitori del MAPK pathway, ma l’efficacia a lungo termine di questa terapia è limitata dallo sviluppo di resistenza, talvolta immediata. In questo studio, abbiamo esaminato le alterazioni molecolari caratterizzanti la progressione del melanoma al fine di identificare fattori predittivi di risposta/resistenza ai MAPK-inibitori. Nello specifico, su una serie di campioni pretrattamento di pazienti affetti da melanoma, trattati con MAPK-inibitori, abbiamo valutato numero di copie del gene BRAF e percentuale di allele V600-mutato, delezione e mutazioni di PTEN, alterazioni del promotore di TERT, e ne abbiamo analizzato l’associazione con la risposta dei pazienti alla terapia. Inoltre, abbiamo determinato il copy number variation dell’intero genoma dei nostri campioni per individuare ulteriori aberrazioni non note potenzialmente associate con la risposta alla terapia. Abbiamo identificato un numero aumentato di copie (gain) del gene BRAF, spesso dovuto a polisomia del cromosoma 7, nel 65% dei pazienti; l’allele mutato è stato trovato in una percentuale compresa tra il 35% e il 65% nel 64% dei pazienti, inferiore al 35% nel 14% dei pazienti e superiore al 65% nel 23% dei pazienti. Dall’analisi di sopravvivenza, è risultato che i pazienti con BRAF diploide o una percentuale di allele mutato inferiore al 35% presentano un più alto rischio di progressione rispetto a coloro che presentano gain di BRAF (HR=2.86; 95% CI 1.29-6.35; p=0.01) o tra il 35% e il 65% di allele mutato (HR=4.54,CI 1.33-15.53; p=0.016), rispettivamente. L’analisi di PTEN ha rivelato la presenza di mutazioni nel 27% dei pazienti, localizzate a livello dei domini catalitico e C2 della proteina codificata; inoltre, il 42% dei casi valutati mostrava una delezione completa del gene, il 35% una delezione parziale, mentre nel 23% dei pazienti non è stata individuata alcuna aberrazione di PTEN. Da notare, delezioni di PTEN sono emerse sia nei casi di melanoma resistente alla terapia, che in quelli che avevano risposto a lungo. Il sequenziamento del promotore del gene TERT ha permesso l’identificazione di mutazioni nel 78% dei pazienti. Le mutazioni -124C>T e -146C>T mostravano la stessa frequenza (36%) nella nostra coorte, mentre la -138-139CC>TT è stata individuata solo nel 5% dei casi. Il 51% dei pazienti presentava inoltre lo SNP rs2853669, noto per contrastare l’effetto attivante delle mutazioni sull’espressione di TERT. Stratificando la coorte di pazienti mutati in base alla presenza/assenza del polimorfismo, i pazienti TERT mutati/SNP carriers mostravano un trend verso una migliore PFS (PFS mediana 11.5 mesi, 95% CI 3.12-19.88) rispetto ai TERT mutati/SNP non-carriers (PFS mediana 7 mesi, 95% CI 4.27-9.72). La mutazione -146C>T, inoltre, correlava con PFS più breve (PFS mediana 5.45 mesi, 95% CI 2.80-9.20) rispetto alla -124C>T (PFS mediana 15.2 mesi, 95% CI 5.57-). Dall’analisi del copy number variation (CNV) sull’intero genoma, le regioni chr3p24, chr3p21.2 e chr17p13.1 hanno mostrato pattern di alterazioni diverse in pazienti responsivi vs. non-responsivi alle terapie; risultano pertanto regioni di potenziale interesse per l’individuazione di nuovi geni coinvolti nella resistenza alla terapia. I nostri dati suggeriscono dunque che l’analisi quantitativa del gene BRAF e il sequenziamento del promotore di TERT costituiscono un utile strumento di selezione dei pazienti con maggiore probabilità di rispondere alla terapia con MAPK-inibitori, contrariamente alla valutazione dello status di PTEN. L’analisi genome-wide, invece, indica di approfondire lo studio dei cromosomi 3 e 17. Progetto 2: ricerca di marcatori biomolecolari per la classificazione del melanoma acrale. Il melanoma acrale lentigginoso è un raro sottotipo di melanoma cutaneo con specifiche caratteristiche morfologiche, epidemiologiche e genetiche. Poiché il genoma del melanoma acrale non è ancora stato pienamente caratterizzato, ne abbiamo analizzato il CNV per individuare quei caratteri genomici peculiari che lo differenziano dal melanoma non acrale. La nostra analisi genome-wide ha evidenziato una maggiore frequenza di delezioni della regione 16q24.2-16q24.3, gains meno frequenti nella regione 7q21.2-7q33, una più accentuata frammentazione genomica e numerosi isocromosomi come caratteri che distinguono il melanoma acrale dal non acrale. Abbiamo inoltre identificato amplificazioni focali nei geni TERT, CCND1, MDM2 e MITF, più rare nei non acrali, laddove interessavano altri geni, come BRAF e MITF. Delezioni focali sono state individuate soprattutto nei geni CDKN2A e PTEN in entrambi i sottotipi di melanoma, anche se più frequenti nei non acrali. I nostri dati, in accordo con il classificare il melanoma acrale come tipo distinto di melanoma, hanno consentito di delinearne alcune delle peculiarità genomiche, chiave per elucidarne anche la patogenesi.
McConechy, Melissa. "PPP2R1A mutations in gynaecologic cancers: functional characterization and use in the genomic classification of tumours". Thesis, University of British Columbia, 2015. http://hdl.handle.net/2429/52829.
Texto completoMedicine, Faculty of
Pathology and Laboratory Medicine, Department of
Graduate
Marisa, Laetitia. "Classification et caractérisation des cancers colorectaux par approches omiques". Thesis, Paris 6, 2015. http://www.theses.fr/2015PA066235/document.
Texto completoColon cancer (CC) is one of the most frequent and most deadly cancer in France and worldwide. Nearly half of patients die within 5 years after diagnosis. Clinical stage based on histological features and molecular classification based genomic instabilities (microsatellite instability (MSI), chromosomal instability (CIN) and hypermethylation of the promoters (ICPM)) are not sufficient to define homogeneous molecular entities and to predict recurrence effectively. To improve patient care, it is essential to better understand the diversity of the disease so that effective prognostic and predictive markers could be found. My PhD work has been focused on studying the diversity of CC at the molecular level through the use of omics approaches on a large cohort of tumor samples. It led to the establishment of a robust transcriptomic classification of these cancers, validated on independent data sets, and to a detailed characterization of each of the subtypes. Six subtypes have been defined and were associated with distinct clinicopathological characteristics and molecular alterations, specific enrichments of supervised gene expression signatures related to cell and lesions of origin, specific deregulated signaling pathways and distinct survival. The results of this work have been strengthened by a consensus classification defined by an international consortium working group in which I've been involved. These results confirm that colorectal cancer is an heterogeneous disease. They provide a renewed framework to develop prognostic signatures, discover new treatment targets, identify new therapeutic strategies and assess response to treatment in clinical trials
Pages, Mélanie. "Integrative genomic, epigenetic, radiologic and histological characterization of pediatric glioneuronal tumors". Thesis, Sorbonne Paris Cité, 2018. http://www.theses.fr/2018USPCB217.
Texto completoThe large-scale genomic studies performed recently has enabled the objective identification of numerous novel genomic alterations and highlighted that pediatric brain tumors often harbor quiet cancer genomes, with a single driver genomic alteration. This characteristic is of special interest in the current context of precision medicine development. Low-grade glioneuronal tumor group is highly heterogeneous and remains particularly challenging since it includes a broad spectrum of tumors, often poorly discriminated by their histopathological features and not completely molecularly characterized. We used targeted methods (IHC, FISH, targeted sequencing), and large scale genomic and epigenetic methodologies to perform an integrative analysis to further characterized papillary glioneuronal tumors (PGNT), midline gangliogliomas and dysembryoplastic neuroepithelial tumors (DNT). We demonstrated that PGNT is a distinct entity characterized by a PRKCA fusion. We highlighted that H3 K27M mutation can occur in association with BRAF V600E mutation in midline grade I glioneuronal tumors, showing that despite the presence of H3 K27M mutations, these cases should not be graded and treated as grade IV tumors because they have a better spontaneous outcome than classic diffuse midline H3 K27M-mutant glioma. The DNT study enable us 1) to specify that non-specific DNT corresponds to a clinico-histological tumor group encompassing diverse molecularly distinct entities and 2) to demonstrate that specific DNTs can be progressive tumors and harbored a distinct DNA methylation profile. Diagnosis and genomic profiling that can guide precision medicine require tissue acquisition by neurosurgical procedures that are often difficult or not possible. We validated a sample collection procedure and we developed methodologies to detect circulating tumor DNA (ctDNA) in CSF, plasma and urine to identify clinically relevant genomic alterations from a cohort of 235 pediatric patients with brain tumors. We optimized a method to process ctDNA and performed ultra-low pass whole genome sequencing (ULP-WGS) using unique molecular identifiers, confirming we can reliably construct sequencing libraries from CSF-, plasma- and urine-derived ctDNA. ULP-WGS has also been used to assess sequencing library quality, copy number variations (CNVs) and tumor fraction. The vast majority of samples undergoing ULPWGS exhibited no CNVs, consistent with either absence in the tumor or low levels of tumorderived cfDNA. To distinguish between these, we developed a hybrid capture sequencing panel allowing identification of specific mutations and fusions more common in pediatric brain tumors
Libros sobre el tema "Genomic classification"
Fenaux, Robert. The classification of Appendicularia (Tunicata): History and current state. [Monaco: Institut océanographique], 1993.
Buscar texto completoGenome clustering: From linguistic models to classification of genetic texts. Berlin: Springer, 2010.
Buscar texto completoMaes, Dominiek, Marina Sibila y Maria Pieters, eds. Mycoplasmas in swine. Wallingford: CABI, 2021. http://dx.doi.org/10.1079/9781789249941.0000.
Texto completo1963-, Feng Zhi y Long Ming, eds. Viral genomes: Diversity, properties, and parameters. Hauppauge, NY: Nova Science Publishers, 2009.
Buscar texto completoViruses and the environment. 2a ed. London ; New York: Chapman and Hall, 1995.
Buscar texto completo1954-, Heiner Monika y SpringerLink (Online service), eds. Computational Methods in Systems Biology: 10th International Conference, CMSB 2012, London, UK, October 3-5, 2012. Proceedings. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012.
Buscar texto completoJános, Varga y Samson Robert A, eds. Aspergillus in the genomic era. Wageningen, Netherlands: Wageningen Academic Publishers, 2008.
Buscar texto completoMatsui, Shigeyuki y Hisashi Noma. Estimation and Selection in High-Dimensional Genomic Studies: Multiple Testing, Gene Ranking, and Classification. Springer, 2020.
Buscar texto completoSherman, Mark E., Melissa A. Troester, Katherine A. Hoadley y William F. Anderson. Morphological and Molecular Classification of Human Cancer. Oxford University Press, 2017. http://dx.doi.org/10.1093/oso/9780190238667.003.0003.
Texto completoBolshoy, Alexander, Zeev Volkovich y Valery Kirzhner. Genome Clustering: From Linguistic Models to Classification of Genetic Texts. Springer, 2010.
Buscar texto completoCapítulos de libros sobre el tema "Genomic classification"
Braga-Neto, Ulisses M., Emre Arslan, Upamanyu Banerjee y Arghavan Bahadorinejad. "Bayesian Classification of Genomic Big Data". En Signal Processing and Machine Learning for Biomedical Big Data, 411–27. Boca Raton : Taylor & Francis, 2018.: CRC Press, 2018. http://dx.doi.org/10.1201/9781351061223-20.
Texto completoLa Rosa, Massimo, Antonino Fiannaca, Riccardo Rizzo y Alfonso Urso. "Genomic Sequence Classification Using Probabilistic Topic Modeling". En Computational Intelligence Methods for Bioinformatics and Biostatistics, 49–61. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-09042-9_4.
Texto completoLi, Jingyi Jessica y Xin Tong. "Genomic Applications of the Neyman–Pearson Classification Paradigm". En Big Data Analytics in Genomics, 145–67. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-41279-5_4.
Texto completoAhmad Dar, Mayasar y Deepmala Sharma. "Revisiting the Genomics and Genetic Codes Using Walsh-Hadamard Spectrum Analysis". En Proceedings of the Conference BioSangam 2022: Emerging Trends in Biotechnology (BIOSANGAM 2022), 106–13. Dordrecht: Atlantis Press International BV, 2022. http://dx.doi.org/10.2991/978-94-6463-020-6_11.
Texto completoBadescu, Dunarel, Abdoulaye Baniré Diallo y Vladimir Makarenkov. "Identification of Specific Genomic Regions Responsible for the Invasivity of Neisseria Meningitidis". En Studies in Classification, Data Analysis, and Knowledge Organization, 491–99. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-10745-0_53.
Texto completoMontesinos López, Osval Antonio, Abelardo Montesinos López y Jose Crossa. "Reproducing Kernel Hilbert Spaces Regression and Classification Methods". En Multivariate Statistical Machine Learning Methods for Genomic Prediction, 251–336. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-89010-0_8.
Texto completoStiglic, Gregor, Juan J. Rodriguez y Peter Kokol. "Rotation of Random Forests for Genomic and Proteomic Classification Problems". En Advances in Experimental Medicine and Biology, 211–21. New York, NY: Springer New York, 2011. http://dx.doi.org/10.1007/978-1-4419-7046-6_21.
Texto completoKaretla, Girija Rani, Daniel R. Catchpoole y Quang Vinh Nguyen. "Hybrid Framework for Genomic Data Classification Using Deep Learning: QDeep_SVM". En Algorithms for Intelligent Systems, 451–63. Singapore: Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-99-1620-7_36.
Texto completoPatel, Nisha B. y Paul A. Lawson. "The Strength of Chemotaxonomy." En Trends in the systematics of bacteria and fungi, 141–67. Wallingford: CABI, 2021. http://dx.doi.org/10.1079/9781789244984.0141.
Texto completoTahiri, Nadia y Aleksandr Koshkarov. "New Metrics for Classifying Phylogenetic Trees Using K-means and the Symmetric Difference Metric". En Studies in Classification, Data Analysis, and Knowledge Organization, 383–91. Cham: Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-09034-9_41.
Texto completoActas de conferencias sobre el tema "Genomic classification"
Akhtar, Mahmood, Eliathamby Ambikairajah y Julien Epps. "GMM-Based Classification of Genomic Sequences". En 2007 15th International Conference on Digital Signal Processing. IEEE, 2007. http://dx.doi.org/10.1109/icdsp.2007.4288529.
Texto completoWatson, Ian R., Chang-Jiun Wu, Lihua Zou, Jeffrey E. Gershenwald y Lynda Chin. "Abstract 2972: Genomic classification of cutaneous melanoma". En Proceedings: AACR 106th Annual Meeting 2015; April 18-22, 2015; Philadelphia, PA. American Association for Cancer Research, 2015. http://dx.doi.org/10.1158/1538-7445.am2015-2972.
Texto completoBowtell, David D. L. "Abstract IA02: Genomic classification of ovarian cancer." En Abstracts: AACR Special Conference: Advances in Ovarian Cancer Research: Exploiting Vulnerabilities; October 17-20, 2015; Orlando, FL. American Association for Cancer Research, 2016. http://dx.doi.org/10.1158/1557-3265.ovca15-ia02.
Texto completoAkhtar, Mahmood, Eliathamby Ambikairajah y Julien Epps. "Comprehensive autoregressive modeling for classification of genomic sequences". En 2007 6th International Conference on Information, Communications & Signal Processing. IEEE, 2007. http://dx.doi.org/10.1109/icics.2007.4449750.
Texto completoKing, Stuart, Yanni Sun, James Cole y Sakti Pramanik. "BLAST Tree: Fast Filtering for Genomic Sequence Classification". En 2010 IEEE International Conference on BioInformatics and BioEngineering. IEEE, 2010. http://dx.doi.org/10.1109/bibe.2010.74.
Texto completoPahadia, Mayank, Akash Srivastava, Divyang Srivastava y Nagamma Patil. "Classification of multi-genomic data using MapReduce paradigm". En 2015 International Conference on Computing, Communication & Automation (ICCCA). IEEE, 2015. http://dx.doi.org/10.1109/ccaa.2015.7148460.
Texto completoRuusuvuori, Pekka, Olli Yli-Harja, Chao Sima y Edward Dougherty. "Classification of quantized small sample data". En 2006 IEEE International Workshop on Genomic Signal Processing and Statistics. IEEE, 2006. http://dx.doi.org/10.1109/gensips.2006.353172.
Texto completoGadia, V. y G. Rosen. "A text-mining approach for classification of genomic fragments". En 2008 IEEE International Conference on Bioinformatics and Biomedcine Workshops. IEEE, 2008. http://dx.doi.org/10.1109/bibmw.2008.4686216.
Texto completoMahapatra, Aritra y Jayanta Mukherjee. "GenFooT: Genomic Footprint of mitochondrial sequence for Taxonomy classification". En 2020 IEEE International Conference on Bioinformatics and Biomedicine (BIBM). IEEE, 2020. http://dx.doi.org/10.1109/bibm49941.2020.9313475.
Texto completoGuarracino, M. R., C. Cifarelli, O. Seref y P. M. Pardalos. "A parallel classification method for genomic and proteomic problems". En 20th International Conference on Advanced Information Networking and Applications - Volume 1 (AINA'06). IEEE, 2006. http://dx.doi.org/10.1109/aina.2006.47.
Texto completoInformes sobre el tema "Genomic classification"
Kamalakaran, Sitharthan y Josh Dubnau. A Strategy to Rapidly Re-Sequence the NF1 Genomic Loci Using Microarrays and Bioinformatics for Molecular Classification of the Disease. Fort Belvoir, VA: Defense Technical Information Center, diciembre de 2006. http://dx.doi.org/10.21236/ada478099.
Texto completoMcCarthy, Noel, Eileen Taylor, Martin Maiden, Alison Cody, Melissa Jansen van Rensburg, Margaret Varga, Sophie Hedges et al. Enhanced molecular-based (MLST/whole genome) surveillance and source attribution of Campylobacter infections in the UK. Food Standards Agency, julio de 2021. http://dx.doi.org/10.46756/sci.fsa.ksj135.
Texto completoBurns, Malcom y Gavin Nixon. Literature review on analytical methods for the detection of precision bred products. Food Standards Agency, septiembre de 2023. http://dx.doi.org/10.46756/sci.fsa.ney927.
Texto completoAhlgren, Per, Tobias Jeppsson, Esa Stenberg y Erik Berg. A bibliometric analysis of battery research with the BATTERY 2030+ roadmap as point of departure. Uppsala universitet, 2022. http://dx.doi.org/10.33063/diva-473454.
Texto completo