Cargando…

Recognizing software names in biomedical literature using machine learning

Software tools now are essential to research and applications in the biomedical domain. However, existing software repositories are mainly built using manual curation, which is time-consuming and unscalable. This study took the initiative to manually annotate software names in 1,120 MEDLINE abstract...

Descripción completa

Detalles Bibliográficos
Autores principales:	Wei, Qiang, Zhang, Yaoyun, Amith, Muhammad, Lin, Rebecca, Lapeyrolerie, Jenay, Tao, Cui, Xu, Hua
Formato:	Online Artículo Texto
Lenguaje:	English
Publicado:	2019
Materias:	Article
Acceso en línea:	https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7334865/ https://www.ncbi.nlm.nih.gov/pubmed/31566474 http://dx.doi.org/10.1177/1460458219869490

Ejemplares similares

Expressing Biomedical Ontologies in Natural Language for Expert Evaluation
por: Amith, Muhammad, et al.
Publicado: (2017)

Recognizing Scientific Artifacts in Biomedical Literature
por: Groza, Tudor, et al.
Publicado: (2013)

A scalable machine-learning approach to recognize chemical names within large text databases
por: Wren, Jonathan D
Publicado: (2006)

Using Ontology Fingerprints to disambiguate gene name entities in the biomedical literature
por: Chen, Guocai, et al.
Publicado: (2015)

Lightweight predicate extraction for patient-level cancer information and ontology development
por: Amith, Muhammad, et al.
Publicado: (2017)

Machine Learning for Biomedical Literature Triage
por: Almeida, Hayda, et al.
Publicado: (2014)

Extract antibody and antigen names from biomedical literature
por: Dinh, Thuy Trang, et al.
Publicado: (2022)

A comparison of conditional random fields and structured support vector machines for chemical entity recognition in biomedical literature
por: Tang, Buzhou, et al.
Publicado: (2015)

Hierarchical shared transfer learning for biomedical named entity recognition
por: Chai, Zhaoying, et al.
Publicado: (2022)

Chemical named entity recognition in patents by domain knowledge and unsupervised feature learning
por: Zhang, Yaoyun, et al.
Publicado: (2016)

LINNAEUS: A species name identification system for biomedical literature
por: Gerner, Martin, et al.
Publicado: (2010)

CheNER: chemical named entity recognizer
por: Usié, Anabel, et al.
Publicado: (2014)

Transfer learning for biomedical named entity recognition with neural networks
por: Giorgi, John M, et al.
Publicado: (2018)

Deep learning with word embeddings improves biomedical named entity recognition
por: Habibi, Maryam, et al.
Publicado: (2017)

Multitask learning for biomedical named entity recognition with cross-sharing structure
por: Wang, Xi, et al.
Publicado: (2019)

Topics in machine learning for biomedical literature analysis and text retrieval
por: Islamaj Doğan, Rezarta, et al.
Publicado: (2011)

Topics in machine learning for biomedical literature analysis and text retrieval
por: Islamaj Doğan, Rezarta, et al.
Publicado: (2012)

Recognizing the value of software: a software citation guide
por: Katz, Daniel S., et al.
Publicado: (2021)

Ethnicity‐based name partitioning for author name disambiguation using supervised machine learning
por: Kim, Jinseok, et al.
Publicado: (2021)

Various criteria in the evaluation of biomedical named entity recognition
por: Tsai, Richard Tzong-Han, et al.
Publicado: (2006)

Machine learning approach to identify adverse events in scientific biomedical literature
por: Wewering, Sonja, et al.
Publicado: (2022)

Disease named entity recognition from biomedical literature using a novel convolutional neural network
por: Zhao, Zhehuan, et al.
Publicado: (2017)

Leveraging syntactic and semantic graph kernels to extract pharmacokinetic drug drug interactions from biomedical literature
por: Zhang, Yaoyun, et al.
Publicado: (2016)

A CRF-based system for recognizing chemical entity mentions (CEMs) in biomedical literature
por: Xu, Shuo, et al.
Publicado: (2015)

A neural network multi-task learning approach to biomedical named entity recognition
por: Crichton, Gamal, et al.
Publicado: (2017)

Analyzing transfer learning impact in biomedical cross-lingual named entity recognition and normalization
por: Rivera-Zavala, Renzo M., et al.
Publicado: (2021)

Ambiguity and variability of database and software names in bioinformatics
por: Duck, Geraint, et al.
Publicado: (2015)

Evaluating Word Representation Features in Biomedical Named Entity Recognition Tasks
por: Tang, Buzhou, et al.
Publicado: (2014)

Phenonizer: A Fine-Grained Phenotypic Named Entity Recognizer for Chinese Clinical Texts
por: Zou, Qunsheng, et al.
Publicado: (2022)

Distinguishing the species of biomedical named entities for term identification
por: Wang, Xinglong, et al.
Publicado: (2008)

Comparison of named entity recognition methodologies in biomedical documents
por: Song, Hye-Jeong, et al.
Publicado: (2018)

Towards reliable named entity recognition in the biomedical domain
por: Giorgi, John M, et al.
Publicado: (2020)

Improving biomedical named entity recognition with syntactic information
por: Tian, Yuanhe, et al.
Publicado: (2020)

Biomedical informatics with optimization and machine learning
por: Huang, Shuai, et al.
Publicado: (2017)

A Machine Learning and Deep Learning Approach for Recognizing Handwritten Digits
por: Sharma, Ayushi, et al.
Publicado: (2022)

Improving deep learning method for biomedical named entity recognition by using entity definition information
por: Xiong, Ying, et al.
Publicado: (2021)

Biomedical named entity recognition with the combined feature attention and fully-shared multi-task learning
por: Zhang, Zhiyu, et al.
Publicado: (2022)

Recognizing Emily and Latisha: Inconsistent Effects of Name Stereotypicality on the Other-Race Effect
por: Stelter, Marleen, et al.
Publicado: (2018)

We are not ready yet: limitations of state-of-the-art disease named entity recognizers
por: Kühnel, Lisa, et al.
Publicado: (2022)

Few-shot biomedical named entity recognition via knowledge-guided instance generation and prompt contrastive learning
por: Chen, Peng, et al.
Publicado: (2023)

Cannot write session to /tmp/vufind_sessions/sess_f1jk0jf5061mspggn69ffsvai6