about
Mining semantic networks of bioinformatics e-resources from the literaturebioNerDS: exploring bioinformatics' database and software use through literature miningCombining knowledge- and data-driven methods for de-identification of clinical narrativesA Survey of Bioinformatics Database and Software Usage through Mining the LiteratureThe BioHub Knowledge Base: Ontology and Repository for Sustainable BiosourcingAmbiguity and variability of database and software names in bioinformaticsData shopping in an open marketplace: Introducing the Ontogrator web application for marking up data using ontologies and browsing using facetsTowards semi-automated curation: using text mining to recreate the HIV-1, human protein interaction database.BioContext: an integrated text mining system for large-scale extraction and contextualization of biomolecular events.Mining protein function from text using term-based support vector machinesIdentification of transcription factor contexts in literature using machine learning approaches.LINNAEUS: a species name identification system for biomedical literature.#WhyWeTweetMH: Understanding Why People Use Twitter to Discuss Mental Health Problems.Mining characteristics of epidemiological studies from Medline: a case study in obesityBiomedical semantics: the hub for biomedical research 2.0.The GNAT library for local and remote gene mention normalizationpubmed2ensembl: a resource for mining the biological literature on genesMedication information extraction with linguistic pattern matching and semantic rulesThe pain interactome: connecting pain-specific protein interactions.Molecular profiling of thyroid cancer subtypes using large-scale text mining.Disentangling the multigenic and pleiotropic nature of molecular functionConstructing a molecular interaction network for thyroid cancer via large-scale text mining of gene and pathway eventsModelling and extraction of variability in free-text medication prescriptions from an anonymised primary care electronic medical record research database.Term identification in the biomedical literature.Decision support systems for clinical radiological practice -- towards the next generationLearning to identify Protected Health Information by integrating knowledge- and data-driven algorithms: A case study on psychiatric evaluation notes.Bias in the reporting of sex and age in biomedical research on mouse modelsCataloging the biomedical world of pain through semi-automated curation of molecular interactions.Combining rules and machine learning for extraction of temporal expressions and events from clinical narratives.Using local lexicalized rules to identify heart disease risk factors in clinical notesA Case Study on Sepsis Using PubMed and Deep Learning for Ontology Learning.Assigning roles to protein mentions: the case of transcription factors.A cascaded approach to normalising gene mentions in biomedical literature.Terminology-driven mining of biomedical literature.Terminology-driven literature mining and knowledge acquisition in biomedicine.Topic categorisation of statements in suicide notes with integrated rules and machine learning.A text mining approach to the prediction of disease status from clinical discharge summaries.Literature mining solutions for life science research.PathNER: a tool for systematic identification of biological pathway mentions in the literature.Extracting patterns of database and software usage from the bioinformatics literature.
P50
Q22001267-E7406C0F-DABA-4F95-B46E-FF3BDF5EACA8Q28292796-B3C3D33D-52D2-45FF-8E04-796CA0453B13Q28598184-78B43B23-7161-4964-ADE9-A30E6EE11180Q28600864-E61E6798-C650-4150-B718-2FE8B7F3F6AAQ28601932-6621CC77-1D8D-4C2A-B552-EF45CD8D8227Q28647795-550B5EA9-4E53-481C-B81A-C1F714D3642FQ30485045-D8245F1E-ED75-4F07-BDAD-30A37BFE3CAAQ30485447-A19F3420-8554-47E7-B846-5AB6CA0C4CDAQ30485537-E160F01C-7489-4514-9751-B8E425102BFBQ33217260-2856FF1F-EB3A-4365-A72C-22B8EB6ADF26Q33329671-29350F1D-7D21-4611-9F55-192860C6471AQ33530716-91C29956-EA6C-42FC-A04D-DF94CB738AAEQ33586903-6AB9CA6A-E70F-47E7-9610-DB3A137AE0D7Q33775540-A3F21491-B7AF-41DB-918F-15D25028D6AEQ33955809-72330B31-7B12-4333-9629-8BB912114B2CQ33980984-63ECFF5E-B6A7-4F01-8914-C056A1F8A47DQ34042833-BCF91D29-0856-4095-A932-604AF434E6B6Q34371796-FAA076AA-C154-4007-818B-DECFCBA92FFFQ34585717-E66FB694-2B14-4426-BA0C-F8E2FB552FE9Q34914970-90A4AB8C-A80B-4B36-9D38-2A7B2E185F92Q35871930-91F95771-0714-47DA-98E3-3A8A2521C3E6Q35872093-1F929BEE-1FF8-4D3F-A33D-9B9C54983DCDQ35918557-83D2A3E9-ADC8-42DB-A83C-FD7CD599EFD4Q35948124-E0C82833-70DA-4AFA-9D99-D09AA12C8574Q36324860-DC1C721A-F42C-4039-B5DF-99A724159DE2Q36397886-7131EA95-BFE4-43E3-853D-4A6262715E27Q36768049-16DDEC5C-5B8A-453C-A34E-16A53C544015Q36871758-E770DBEC-5C9A-4856-AD69-F5F510777579Q37129531-D83FCFD6-2FCC-448A-9226-0672733AB8B9Q37153425-7D3D0C49-95D6-47DD-A297-2E6BDB30D63BQ38377802-7CE1B4EE-AFE7-49A7-8A65-2B471BC95093Q38382459-0977F017-A765-4EB3-8B4F-06730D5EDD88Q38391956-F9677730-725D-40DE-897D-74F0E814771FQ38428124-DDCAE54D-43F5-471F-8348-E613EA7FCF4EQ38453519-62F4BAD9-B2C8-454A-835B-D011D28D987AQ38466244-1140FC53-730F-4313-A104-E81847FA5A56Q41828001-4DCCC10B-605B-41D2-A66B-4FC8931B6F32Q42010241-9F072F43-A342-4A0D-8D20-2AB592E6D210Q42844532-3DA70B8C-86D8-47B6-BCB3-FF0E923E4EB4Q42848331-FC3CAF1D-4D9A-46C9-AA9C-74698C59DDBE
P50
description
hulumtues
@sq
onderzoeker
@nl
researcher
@en
հետազոտող
@hy
name
Goran Nenadic
@ast
Goran Nenadic
@en
Goran Nenadic
@es
Goran Nenadic
@nl
Goran Nenadic
@sl
type
label
Goran Nenadic
@ast
Goran Nenadic
@en
Goran Nenadic
@es
Goran Nenadic
@nl
Goran Nenadic
@sl
prefLabel
Goran Nenadic
@ast
Goran Nenadic
@en
Goran Nenadic
@es
Goran Nenadic
@nl
Goran Nenadic
@sl
P106
P1153
6603053052
P21
P31
P496
0000-0003-0795-5363