Knowledge Discovery in Life Science Literature

2006 ◽  
Keyword(s):  
Knowledge Discovery ◽  
Life Science ◽  
Science Literature

scholarly journals Looking through glass: Knowledge discovery from materials science literature using natural language processing

Patterns ◽  
2021 ◽  
pp. 100290
Author(s):  
Vineeth Venugopal ◽  
Sourav Sahoo ◽  
Mohd Zaki ◽  
Manish Agarwal ◽  
Nitya Nand Gosvami ◽  
...  
Keyword(s):  
Natural Language Processing ◽  
Natural Language ◽  
Knowledge Discovery ◽  
Language Processing ◽  
Materials Science ◽  
Science Literature

scholarly journals Towards Virtual Knowledge Broker services for semantic integration of life science literature and data sources

2013 ◽  
Vol 18 (9-10) ◽  
pp. 428-434 ◽  
Author(s):  
Ian Harrow ◽  
Wendy Filsell ◽  
Peter Woollard ◽  
Ian Dix ◽  
Michael Braxenthaler ◽  
...  
Keyword(s):  
Life Science ◽  
Semantic Integration ◽  
Knowledge Broker ◽  
Data Sources ◽  
Science Literature

Nurturing life-science knowledge discovery: managing multi-organisation networks

2012 ◽  
Vol 24 (2-3) ◽  
pp. 195-207 ◽  
Author(s):  
Lawrence Dooley ◽  
David Kirk ◽  
Kevin Philpott
Keyword(s):  
Knowledge Discovery ◽  
Life Science ◽  
Science Knowledge

scholarly journals Normalized MEDLINE distance in context-aware life science literature searches

2010 ◽  
Vol 15 (6) ◽  
pp. 709-715 ◽  
Author(s):  
Yan Wang ◽  
Cong Wang ◽  
Yi Zeng ◽  
Zhisheng Huang ◽  
Vassil Momtchev ◽  
...  
Keyword(s):  
Life Science ◽  
Science Literature ◽  
Context Aware ◽  
Literature Searches

scholarly journals Alignment of Biomedical Ontologies Using Life Science Literature

2006 ◽  
pp. 1-17 ◽  
Author(s):  
He Tan ◽  
Vaida Jakonienė ◽  
Patrick Lambrix ◽  
Johan Aberg ◽  
Nahid Shahmehri
Keyword(s):  
Life Science ◽  
Science Literature ◽  
Biomedical Ontologies

scholarly journals Bridging Data Management and Knowledge Discovery in the Life Sciences

2008 ◽  
Vol 2 (1) ◽  
pp. 28-36 ◽  
Author(s):  
Karl Kugler ◽  
Maria Mercedes Tejada ◽  
Christian Baumgartner ◽  
Bernhard Tilg ◽  
Armin Graber ◽  
...  
Keyword(s):  
Data Mining ◽  
Knowledge Discovery ◽  
Data Warehouse ◽  
Life Science ◽  
Life Sciences ◽  
Relevant Information ◽  
Biomedical Data ◽  
Science Data ◽  
New Knowledge ◽  
Discovery Algorithms

In this work we present an application for integrating and analyzing life science data using a biomedical data warehouse system and tools developed in-house enabling knowledge discovery tasks. Knowledge discovery is known as a process where different steps have to be coupled in order to solve a specified question. In order to create such a combination of steps, a data miner using our in-house developed knowledge discovery tool KD3 is able to assemble functional objects to a data mining workflow. The generated workflows can easily be used for ulterior purposes by only adding new data and parameterizing the functional objects in the process. Workflows guide the performance of data integration and aggregation tasks, which were defined and implemented using a public available open source tool. To prove the concept of our application, intelligent query models were designed and tested for the identification of genotype-phenotype correlations in Marfan Syndrome. It could be shown that by using our application, a data miner can easily develop new knowledge discovery algorithms that may later be used to retrieve medical relevant information by clinical researchers.

scholarly journals Complex networks reveal emergent interdisciplinary knowledge in Wikipedia

2021 ◽  
Vol 8 (1) ◽  
Author(s):  
Gustavo A. Schwartz
Keyword(s):  
Data Mining ◽  
Complex Networks ◽  
Knowledge Discovery ◽  
Scientific Activity ◽  
Collective Behaviour ◽  
New Method ◽  
Science Literature ◽  
Quantitative Studies ◽  
Interdisciplinary Knowledge ◽  
Human Capacity

AbstractIn the last 2 decades, a great amount of work has been done on data mining and knowledge discovery using complex networks. These works have provided insightful information about the structure and evolution of scientific activity, as well as important biomedical discoveries. However, interdisciplinary knowledge discovery, including disciplines other than science, is more complicated to implement because most of the available knowledge is not indexed. Here, a new method is presented for mining Wikipedia to unveil implicit interdisciplinary knowledge to map and understand how different disciplines (art, science, literature) are related to and interact with each other. Furthermore, the formalism of complex networks allows us to characterise both individual and collective behaviour of the different elements (people, ideas, works) within each discipline and among them. The results obtained agree with well-established interdisciplinary knowledge and show the ability of this method to boost quantitative studies. Note that relevant elements in different disciplines that rarely directly refer to each other may nonetheless have many implicit connections that impart them and their relationship with new meaning. Owing to the large number of available works and to the absence of cross-references among different disciplines, tracking these connections can be challenging. This approach aims to bridge this gap between the large amount of reported knowledge and the limited human capacity to find subtle connections and make sense of them.

scholarly journals Client-side integration of life science literature resources

2009 ◽  
Vol 25 (23) ◽  
pp. 3194-3196 ◽  
Author(s):  
R. Easty ◽  
N. Nikolov
Keyword(s):  
Life Science ◽  
Science Literature ◽  
Client Side
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