000			04172nam a22004095i 4500
999			_c396463 _d396463
001			396463
003			ES-MaUEC
005			20230727151548.0
006			a||||fo|||| 00| 0
007			cr nn 008mamaa
008			220617s2022 xxu| o |||| 0|eng d
020			_a9781071623053
024	7		_a10.1007/978-1-0716-2305-3 _2doi
040			_aES-MaUEC _bspa _cES-MaUEC _dES-MaUEC
050		4	_aQA76.9 .D34 _b2022 EB
245	0	0	_aBiomedical Text Mining _cedited by Kalpana Raja
250			_a1st edition 2022
264		1	_aNew York, NY _bSpringer International Publising _c2022
300			_a1 recurso en línea (XI, 321 páginas) _b79 ilustraciones, 76 ilustraciones a color
336			_atexto _btxt _2rdacontent
337			_aelectrónico _bc _2rdamedia
338			_arecurso electrónico _bcr _2rdacarrier
347			_aarchivo de texto _bPDF
490	0		_aMethods in Molecular Biology _x1940-6029 _v2496
505	0		_aBiomedical literature mining and its components -- Text mining protocol to retrieve significant drug-gene interactions from PubMed abstracts -- A hybrid protocol for finding novel gene targets for various diseases using microarray expression data analysis and text mining -- Finding gene associations by text mining and annotating it with Gene Ontology -- Biomedical literature mining for repurposing laboratory tests -- A simple computational approach to identify potential drugs for multiple sclerosis and cognitive disorders from expert curated resources -- Combining literature mining and machine learning for predicting biomedical discoveries -- A Text Mining Protocol for Mining Biological Pathways and Regulatory Networks from Biomedical Literature -- Text mining and machine learning protocol for extracting human related protein phosphorylation information from PubMed -- A text mining and machine learning protocol for extracting post translational modifications of proteins from PubMed: A special focus on glycosylation, acetylation, methylation, hydroxylation, and ubiquitination -- A hybrid protocol for identifying comorbidity-based potential drugs for COVID-19 using biomedical literature mining, network analysis, and deep learning -- BioBERT and Similar Approaches for Relation Extraction -- A text mining protocol for predicting drug-drug interaction and adverse drug reactions from PubMed articles -- A text mining protocol for extracting drug-drug interaction and adverse drug reactions specific to patient population, pharmacokinetics, pharmacodynamics, and disease -- Extracting significant comorbid diseases from MeSH index of PubMed -- Integration of transcriptomic data and metabolomic data using biomedical literature mining and pathway analysis.
520			_aThis volume details step-by-step instructions on biomedical literature mining protocols. Chapters guide readers through various topics such as, disease comorbidity, literature-based discovery, protocols to combine literature mining, machine learning for predicting biomedical discoveries, and uncovering unknown public knowledge by combining two pieces of information from different sets of PubMed articles. Additional chapters discuss the importance of data science to understand outbreaks such as COVID-19. Written in the format of the highly successful Methods in Molecular Biology series, each chapter includes an introduction to the topic, includes tips on troubleshooting and known pitfalls, and step-by-step, readily reproducible protocols. Authoritative and cutting-edge, Biomedical Text Mining aims to be a useful practical guide to researches to help further their studies. .
988			_aSpringer_Protocols_2022
650		7	_2embne _9141180 _aProceso de datos
650		7	_2embne _9160489 _aBioinformática
776	0	8	_iPrinted edition: _z9781071623046
776	0	8	_iPrinted edition: _z9781071623060
776	0	8	_iPrinted edition: _z9781071623077
856	4	0	_uhttps://go.openathens.net/redirector/universidadeuropea.es?url=https://doi.org/10.1007/978-1-0716-2305-3 _zAcceso a este recurso digital (usuarios Universidad Europea de Madrid)
942			_2lcc _cLE
998			_b07/2023 _dz _eu _zSI