Biological relation extraction and query answering from MEDLINE abstracts using ontology-based text mining

2007 ◽  
Vol 61 (2) ◽  
pp. 228-262 ◽  
Author(s):  
Muhammad Abulaish ◽  
Lipika Dey
Keyword(s):  
Text Mining ◽  
Relation Extraction ◽  
Query Answering ◽  
Biological Relation

scholarly journals PLAN2L: a web tool for integrated text mining and literature-based bioentity relation extraction

2009 ◽  
Vol 37 (Web Server) ◽  
pp. W160-W165 ◽  
Author(s):  
M. Krallinger ◽  
C. Rodriguez-Penagos ◽  
A. Tendulkar ◽  
A. Valencia
Keyword(s):  
Text Mining ◽  
Relation Extraction ◽  
Web Tool

scholarly journals Text Mining for Building Biomedical Networks Using Cancer as a Case Study

Biomolecules ◽  
2021 ◽  
Vol 11 (10) ◽  
pp. 1430
Author(s):  
Sofia I. R. Conceição ◽  
Francisco M. Couto
Keyword(s):  
Text Mining ◽  
Biological Networks ◽  
Scientific Literature ◽  
Real Life ◽  
Relation Extraction ◽  
Cancer Disease ◽  
Network Building ◽  
Automatic Information ◽  
Notable Increase

In the assembly of biological networks it is important to provide reliable interactions in an effort to have the most possible accurate representation of real-life systems. Commonly, the data used to build a network comes from diverse high-throughput essays, however most of the interaction data is available through scientific literature. This has become a challenge with the notable increase in scientific literature being published, as it is hard for human curators to track all recent discoveries without using efficient tools to help them identify these interactions in an automatic way. This can be surpassed by using text mining approaches which are capable of extracting knowledge from scientific documents. One of the most important tasks in text mining for biological network building is relation extraction, which identifies relations between the entities of interest. Many interaction databases already use text mining systems, and the development of these tools will lead to more reliable networks, as well as the possibility to personalize the networks by selecting the desired relations. This review will focus on different approaches of automatic information extraction from biomedical text that can be used to enhance existing networks or create new ones, such as deep learning state-of-the-art approaches, focusing on cancer disease as a case-study.

scholarly journals A Knowledge-Driven Approach to Extract Disease-Related Biomarkers from the Literature

2014 ◽  
Vol 2014 ◽  
pp. 1-11 ◽  
Author(s):  
À. Bravo ◽  
M. Cases ◽  
N. Queralt-Rosinach ◽  
F. Sanz ◽  
L. I. Furlong
Keyword(s):  
Text Mining ◽  
Named Entity Recognition ◽  
Relation Extraction ◽  
Recognition System ◽  
Biomedical Literature ◽  
Entity Recognition ◽  
Scientific Publications ◽  
Positive Ratio ◽  
Related Information ◽  
Mesh Terms

The biomedical literature represents a rich source of biomarker information. However, both the size of literature databases and their lack of standardization hamper the automatic exploitation of the information contained in these resources. Text mining approaches have proven to be useful for the exploitation of information contained in the scientific publications. Here, we show that a knowledge-driven text mining approach can exploit a large literature database to extract a dataset of biomarkers related to diseases covering all therapeutic areas. Our methodology takes advantage of the annotation of MEDLINE publications pertaining to biomarkers with MeSH terms, narrowing the search to specific publications and, therefore, minimizing the false positive ratio. It is based on a dictionary-based named entity recognition system and a relation extraction module. The application of this methodology resulted in the identification of 131,012 disease-biomarker associations between 2,803 genes and 2,751 diseases, and represents a valuable knowledge base for those interested in disease-related biomarkers. Additionally, we present a bibliometric analysis of the journals reporting biomarker related information during the last 40 years.

scholarly journals miRTex: A Text Mining System for miRNA-Gene Relation Extraction

2015 ◽  
Vol 11 (9) ◽  
pp. e1004391 ◽  
Author(s):  
Gang Li ◽  
Karen E. Ross ◽  
Cecilia N. Arighi ◽  
Yifan Peng ◽  
Cathy H. Wu ◽  
...  
Keyword(s):  
Text Mining ◽  
Mirna Gene ◽  
Relation Extraction ◽  
Mining System ◽  
Text Mining System
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