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Automatic recognition of topic-classified relations between prostate cancer and genes using MEDLINE abstracts

Hong-Woo Chun, Yoshimasa Tsuruoka, Jin-Dong Kim, Rie Shiba, Naoki Nagata, Teruyoshi Hishiki, Jun'ichi Tsujii
2006 BMC Bioinformatics  
We collected prostate cancer-related abstracts from MEDLINE, and constructed an annotated corpus of gene and prostate cancer relations based on six topics by biologists.  ...  Automatic recognition of relations between a specific disease term and its relevant genes or protein terms is an important practice of bioinformatics.  ...  The full contents of the supplement are available online at http://www.biomedcentral.com/1471-2105/7?issue=S3.  ... 
doi:10.1186/1471-2105-7-s3-s4 pmid:17134477 pmcid:PMC1764448 fatcat:jg4tzqgmvrht3d7kbtgtspjcge

Biomedical text mining and its applications in cancer research

Fei Zhu, Preecha Patumcharoenpol, Cheng Zhang, Yang Yang, Jonathan Chan, Asawin Meechai, Wanwipa Vongsangnak, Bairong Shen
2013 Journal of Biomedical Informatics  
The immense body and rapid growth of biomedical text on cancer has led to the appearance of a large number of text mining techniques aimed at extracting novel knowledge from scientific text.  ...  Biomedical text mining on cancer research is computationally automatic and high-throughput in nature. However, it is error-prone due to the complexity of natural language processing.  ...  [91] developed classifiers for the automatic identification of schemes from abstracts to help cancer risk assessment.  ... 
doi:10.1016/j.jbi.2012.10.007 pmid:23159498 fatcat:xd7j77sbwfhklkat6tael64lbq

CERC: an interactive content extraction, recognition, and construction tool for clinical and biomedical text

Eva K Lee, Karan Uppal
2020 BMC Medical Informatics and Decision Making  
and extracting salient information from clinical and biomedical text.  ...  In this work, we develop an interactive content extraction, recognition, and construction system (CERC) that combines machine learning and visualization techniques with domain knowledge for highlighting  ...  , Chris Kwan, Eunho Kwon, Di Liu, Joe Malecki, Autumn Phillips, and Peijue Zhang, who helped with the initial usage and testing of the anonymized data generated from the customizable information extraction  ... 
doi:10.1186/s12911-020-01330-8 pmid:33323109 fatcat:neac7vl5fncibnx3kxcktyqmba

Target discovery from data mining approaches

Yongliang Yang, S. James Adelstein, Amin I. Kassis
2009 Drug Discovery Today  
Acknowledgements We wish to thank Dr Pavel Pospisil and Dr Lakshmanan K. Iyer for their helpful discussions in this project.  ...  First, an initial list of 15 genes (seed genes) that are well known to be related to prostate cancer was collected from a curated database, Online Mendelian Inheritance in Man (OMIM; also see Box 1).  ...  For the text mining approach, all abstracts related to the keywords 'HCV' and 'protein interactions' were retrieved and subjected to gene name recognition and human expert curation.  ... 
doi:10.1016/j.drudis.2008.12.005 pmid:19135549 fatcat:plsts2psuvclneuhgd5tlianwi

Target discovery from data mining approaches

Yongliang Yang, S. James Adelstein, Amin I. Kassis
2012 Drug Discovery Today  
Acknowledgements We wish to thank Dr Pavel Pospisil and Dr Lakshmanan K. Iyer for their helpful discussions in this project.  ...  First, an initial list of 15 genes (seed genes) that are well known to be related to prostate cancer was collected from a curated database, Online Mendelian Inheritance in Man (OMIM; also see Box 1).  ...  For the text mining approach, all abstracts related to the keywords 'HCV' and 'protein interactions' were retrieved and subjected to gene name recognition and human expert curation.  ... 
doi:10.1016/j.drudis.2011.12.006 pmid:22178890 fatcat:c7k3puzntbhcrmzjlw6uvgxhny

Automated recognition of malignancy mentions in biomedical literature

Yang Jin, Ryan T McDonald, Kevin Lerman, Mark A Mandel, Steven Carroll, Mark Y Liberman, Fernando C Pereira, Raymond S Winters, Peter S White
2006 BMC Bioinformatics  
Application of MTag to all MEDLINE abstracts yielded the identification of 580,002 unique and 9,153,340 overall mentions of malignancy.  ...  Previous efforts in biomedical text mining have focused primarily upon named entity recognition of well-defined molecular objects such as genes, but less work has been performed to identify disease-related  ...  of Medicine for access to MEDLINE; and Richard Wooster for corpus provision.  ... 
doi:10.1186/1471-2105-7-492 pmid:17090325 pmcid:PMC1657036 fatcat:no7r2il3ybh5ncwop5xhpez6ma

The Treasury Chest of Text Mining: Piling Available Resources for Powerful Biomedical Text Mining

Nícia Rosário-Ferreira, Catarina Marques-Pereira, Manuel Pires, Daniel Ramalhão, Nádia Pereira, Victor Guimarães, Vítor Santos Costa, Irina S. Moreira
2021 BioChem  
The array of documents that can be used ranges from scientific literature to patents or clinical data, and the biomedical concepts often include, despite not being limited to genes, proteins, drugs, and  ...  When applied to biomedical literature, text mining is named biomedical text mining and its specificity lies in both the type of analyzed documents and the language and concepts retrieved.  ...  Conflicts of Interest: The authors declare no conflict of interest.  ... 
doi:10.3390/biochem1020007 fatcat:qve6xgoxuvbwzpqz6q45s7wlly

Extracting Concepts for Precision Oncology from the Biomedical Literature

Nicholas Greenspan, Yuqi Si, Kirk Roberts
2021 AMIA Annual Symposium Proceedings  
This paper describes an initial dataset and automatic natural language processing (NLP) method for extracting concepts related to precision oncology from biomedical research articles.  ...  The best-performing model achieved a precision of 63.8%, a recall of 71.9%, and an F1 of 67.1.  ...  [14] developed a corpus and extracted relations between prostate cancer and genes from abstracts using a maximum entropy classifier.  ... 
pmid:34457142 pmcid:PMC8378653 fatcat:3zm7sb3rujaxjcotp3wwgmiv7y

Extracting Concepts for Precision Oncology from the Biomedical Literature [article]

Nicholas Greenspan and Yuqi Si and Kirk Roberts
2020 arXiv   pre-print
This paper describes an initial dataset and automatic natural language processing (NLP) method for extracting concepts related to precision oncology from biomedical research articles.  ...  The best-performing model achieved a precision of 63.8%, a recall of 71.9%, and an F1 of 67.1.  ...  [14] developed a corpus and extracted relations between prostate cancer and genes from abstracts using a maximum entropy classifier.  ... 
arXiv:2010.00074v1 fatcat:rgq6yymifrd7jdb4r7knzm2w74

Text mining of cancer-related information: Review of current status and future directions

Irena Spasić, Jacqueline Livsey, John A. Keane, Goran Nenadić
2014 International Journal of Medical Informatics  
The F-measure of NER ranges between 80% and 90%, while that of IE for simple tasks is in the high 90s.  ...  Methods: A review of the research on TM of cancer-related information was carried out.  ...  The NCI Thesaurus provides definitions and synonyms of nearly 10,000 cancers and related diseases, 8000 single agents and combination therapies and a range of other cancer-related topics.  ... 
doi:10.1016/j.ijmedinf.2014.06.009 pmid:25008281 fatcat:c3ifkrjacjgghdm6kz65flh434

Combining literature text mining with microarray data: advances for system biology modeling

A. Faro, D. Giordano, C. Spampinato
2011 Briefings in Bioinformatics  
At the same time, the publication of databases of biological information and of experimental datasets generated by high-throughput methods is in great expansion, and a wealth of annotated gene databases  ...  Under this scenario, this article reviews the knowledge discovery systems that fuse information from the literature, gathered by text mining, with microarray data for enriching the lists of down and upregulated  ...  An example of association derived with Anni 2.0. is: 'Gene KLK3 is bound to the prostate cancer, more specifically with malignant neoplasm of prostate'.  ... 
doi:10.1093/bib/bbr018 pmid:21677032 fatcat:lvtg7ea74ngb5h6i2ywss6cjru

Molecular profiling of thyroid cancer subtypes using large-scale text mining

Chengkun Wu, Jean-Marc Schwartz, Georg Brabant, Goran Nenadic
2014 BMC Medical Genomics  
Conclusions: Identification of key genes and pathways plays a central role in understanding the molecular biology of thyroid cancer.  ...  Identifying the most relevant genes and biological pathways reported in the thyroid cancer literature is vital for understanding of the disease and developing targeted therapeutics.  ...  For instance, a maximum entropy-based named entity recognizer and relation recognizer were applied to find relations between prostate cancer and genes [23] .  ... 
doi:10.1186/1755-8794-7-s3-s3 pmid:25521965 pmcid:PMC4290788 fatcat:ga2yy4owpfcmjbsuuly4tei2r4

Context-specific interaction networks from vector representation of words

Matteo Manica, Roland Mathis, Joris Cadow, María Rodríguez Martínez
2019 Nature Machine Intelligence  
Here we present INtERAcT, a novel approach to extract protein-protein interactions from a corpus of biomedical articles related to a broad range of scientific domains in a completely unsupervised way.  ...  INtERAcT exploits vector representation of words, computed on a corpus of domain specific knowledge, and implements a new metric that estimates an interaction score between two molecules in the space where  ...  The project leading to this application has received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement No 668858.  ... 
doi:10.1038/s42256-019-0036-1 fatcat:3p6cvpw3hfhtviuumdok36myde

The Clinical Role of SRSF1 Expression in Cancer: A Review of the Current Literature

Arturo Lo Giudice, Maria Giovanna Asmundo, Giuseppe Broggi, Sebastiano Cimino, Giuseppe Morgia, Ettore Di Trapani, Stefano Luzzago, Gennaro Musi, Matteo Ferro, Ottavio de Cobelli, Giorgio I. Russo
2022 Applied Sciences  
Methods: Our review is based on English articles published in the MEDLINE/PubMed medical library between 2000 and 2021.  ...  We retrieved articles that were specifically related to SRSF1 and cancers, and we excluded other reviews and meta-analyses.  ...  Conflicts of Interest: The authors declare no conflict of interest.  ... 
doi:10.3390/app12052268 fatcat:6wfcupr3xrhsti3alhrau6csne

Natural Language Processing methods and systems for biomedical ontology learning

Kaihong Liu, William R. Hogan, Rebecca S. Crowley
2011 Journal of Biomedical Informatics  
from free-text documents.  ...  One important requirement of domain ontologies is that they must achieve a high degree of coverage of the domain concepts and concept relationships.  ...  We thank the two anonymous reviewers of this whose insightful critiques and suggestions have helped us improve the quality and completeness of this review.  ... 
doi:10.1016/j.jbi.2010.07.006 pmid:20647054 pmcid:PMC2990796 fatcat:hnyc5k4iobdzpatclwihaiw4wu
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