scholarly journals ScanBious: Survey for Obesity Genes Using PubMed Abstracts and DisGeNET

2021 ◽  
Vol 11 (4) ◽  
pp. 246
Author(s):  
Svetlana Tarbeeva ◽  
Ekaterina Lyamtseva ◽  
Andrey Lisitsa ◽  
Anna Kozlova ◽  
Elena Ponomarenko ◽  
...  
Keyword(s):  
Text Mining ◽  
Personalized Medicine ◽  
Intercellular Signaling ◽  
Web Tool ◽  
Medical Subject Heading ◽  
Physiological Conditions ◽  
Key Concepts ◽  
General Network ◽  
Obesity Genes ◽  
Automatic Text

We used automatic text-mining of PubMed abstracts of papers related to obesity, with the aim of revealing that the information used in abstracts reflects the current understanding and key concepts of this widely explored problem. We compared expert data from DisGeNET to the results of an automated MeSH (Medical Subject Heading) search, which was performed by the ScanBious web tool. The analysis provided an overview of the obesity field, highlighting major trends such as physiological conditions, age, and diet, as well as key well-studied genes, such as adiponectin and its receptor. By intersecting the DisGeNET knowledge with the ScanBious results, we deciphered four clusters of obesity-related genes. An initial set of 100+ thousand abstracts and 622 genes was reduced to 19 genes, distributed among just a few groups: heredity, inflammation, intercellular signaling, and cancer. Rapid profiling of articles could drive personalized medicine: if the disease signs of a particular person were superimposed on a general network, then it would be possible to understand which are non-specific (observed in cohorts and, therefore, most likely have known treatment solutions) and which are less investigated, and probably represent a personalized case.

Automatic text-mining as an unbiased approach to uncover molecular associations between periodontitis and coronary artery disease

Biomarkers ◽  
2021 ◽  
pp. 1-22
Author(s):  
Fábio Trindade ◽  
Luís Perpétuo ◽  
Rita Ferreira ◽  
Adelino Leite-Moreira ◽  
Inês Falcão-Pires ◽  
...  
Keyword(s):  
Coronary Artery Disease ◽  
Coronary Artery ◽  
Text Mining ◽  
Molecular Associations ◽  
Artery Disease ◽  
Automatic Text

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

Identifying Disease Genes Based on Functional Annotation and Text Mining

2013 ◽  
pp. 48-57
Author(s):  
Fang Yuan ◽  
Mingliang Li ◽  
Jing Li
Keyword(s):  
Text Mining ◽  
Functional Annotation ◽  
Target Gene ◽  
Disease Genes ◽  
Web Tool ◽  
Related Function ◽  
Disease Phenotypes ◽  
Pathogenic Genes ◽  
Omim Database ◽  
Detailed Function

The identification of disease genes from candidated regions is one of the most important tasks in bioinformatics research. Most approaches based on function annotations cannot be used to identify genes for diseases without any known pathogenic genes or related function annotations. The authors have built a new web tool, DGHunter, to predict genes associated with these diseases which lack detailed function annotations. Its performance was tested with a set of 1506 genes involved in 1147 disease phenotypes derived from the morbid map table in the OMIM database. The results show that, on average, the target gene was in the top 13.60% of the ranked lists of candidates, and the target gene was in the top 5% with a 40.70% chance. DGHunter can identify disease genes effectively for those diseases lacking sufficient function annotations.

scholarly journals Manually structured digital abstracts: A scaffold for automatic text mining

FEBS Letters ◽  
2008 ◽  
Vol 582 (8) ◽  
pp. 1170-1170 ◽  
Author(s):  
Michael Seringhaus ◽  
Mark Gerstein
Keyword(s):  
Text Mining ◽  
Automatic Text

scholarly journals KinderMiner Web: a simple web tool for ranking pairwise associations in biomedical applications

F1000Research ◽  
2020 ◽  
Vol 9 ◽  
pp. 832
Author(s):  
Finn Kuusisto ◽  
Daniel Ng ◽  
John Steill ◽  
Ian Ross ◽  
Miron Livny ◽  
...  
Keyword(s):  
Text Mining ◽  
Web Application ◽  
Biomedical Applications ◽  
Analysis Tool ◽  
Trial And Error ◽  
Web Tool ◽  
Interactive Analysis ◽  
Term List ◽  
Public Resource ◽  
The Web

Many important scientific discoveries require lengthy experimental processes of trial and error and could benefit from intelligent prioritization based on deep domain understanding. While exponential growth in the scientific literature makes it difficult to keep current in even a single domain, that same rapid growth in literature also presents an opportunity for automated extraction of knowledge via text mining. We have developed a web application implementation of the KinderMiner algorithm for proposing ranked associations between a list of target terms and a key phrase. Any key phrase and target term list can be used for biomedical inquiry. We built the web application around a text index derived from PubMed. It is the first publicly available implementation of the algorithm, is fast and easy to use, and includes an interactive analysis tool. The KinderMiner web application is a public resource offering scientists a cohesive summary of what is currently known about a particular topic within the literature, and helping them to prioritize experiments around that topic. It performs comparably or better to similar state-of-the-art text mining tools, is more flexible, and can be applied to any biomedical topic of interest. It is also continually improving with quarterly updates to the underlying text index and through response to suggestions from the community. The web application is available at https://www.kinderminer.org.

Integration of Automatic Text Mining and Genomic and Proteomic Analysis to Unravel Prostate Cancer Biomarkers

2022 ◽  
Author(s):  
Tânia Lima ◽  
Rita Ferreira ◽  
Marina Freitas ◽  
Rui Henrique ◽  
Rui Vitorino ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Text Mining ◽  
Proteomic Analysis ◽  
Cancer Biomarkers ◽  
Automatic Text

scholarly journals KinderMiner Web: a simple web tool for ranking pairwise associations in biomedical applications

F1000Research ◽  
2021 ◽  
Vol 9 ◽  
pp. 832
Author(s):  
Finn Kuusisto ◽  
Daniel Ng ◽  
John Steill ◽  
Ian Ross ◽  
Miron Livny ◽  
...  
Keyword(s):  
Text Mining ◽  
Web Application ◽  
Biomedical Applications ◽  
Analysis Tool ◽  
Trial And Error ◽  
Web Tool ◽  
Interactive Analysis ◽  
Term List ◽  
Public Resource ◽  
The Web

Many important scientific discoveries require lengthy experimental processes of trial and error and could benefit from intelligent prioritization based on deep domain understanding. While exponential growth in the scientific literature makes it difficult to keep current in even a single domain, that same rapid growth in literature also presents an opportunity for automated extraction of knowledge via text mining. We have developed a web application implementation of the KinderMiner algorithm for proposing ranked associations between a list of target terms and a key phrase. Any key phrase and target term list can be used for biomedical inquiry. We built the web application around a text index derived from PubMed. It is the first publicly available implementation of the algorithm, is fast and easy to use, and includes an interactive analysis tool. The KinderMiner web application is a public resource offering scientists a cohesive summary of what is currently known about a particular topic within the literature, and helping them to prioritize experiments around that topic. It performs comparably or better to similar state-of-the-art text mining tools, is more flexible, and can be applied to any biomedical topic of interest. It is also continually improving with quarterly updates to the underlying text index and through response to suggestions from the community. The web application is available at https://www.kinderminer.org.

Identifying Disease Genes Based on Functional Annotation and Text Mining

2011 ◽  
Vol 3 (1) ◽  
pp. 45-54
Author(s):  
Fang Yuan ◽  
Mingliang Li ◽  
Jing Li
Keyword(s):  
Text Mining ◽  
Functional Annotation ◽  
Target Gene ◽  
Disease Genes ◽  
Web Tool ◽  
Related Function ◽  
Disease Phenotypes ◽  
Pathogenic Genes ◽  
Omim Database ◽  
Detailed Function

The identification of disease genes from candidated regions is one of the most important tasks in bioinformatics research. Most approaches based on function annotations cannot be used to identify genes for diseases without any known pathogenic genes or related function annotations. The authors have built a new web tool, DGHunter, to predict genes associated with these diseases which lack detailed function annotations. Its performance was tested with a set of 1506 genes involved in 1147 disease phenotypes derived from the morbid map table in the OMIM database. The results show that, on average, the target gene was in the top 13.60% of the ranked lists of candidates, and the target gene was in the top 5% with a 40.70% chance. DGHunter can identify disease genes effectively for those diseases lacking sufficient function annotations.

Personalized Medicine, Pharmacogenomics, and the Practice of Psychiatry: On the Threshold of Predictive Therapeutics in Psychopharmacology?

CNS Spectrums ◽  
2008 ◽  
Vol 13 (2) ◽  
pp. 115-118 ◽  
Author(s):  
Stephen M. Stahl
Keyword(s):  
Personalized Medicine ◽  
Key Concepts ◽  
Selection Of

Personalizing medicine by understanding the unique properties of each patient's genome has the potential of predicting what drug to prescribe for that individual. This approach has already proven useful for several drugs in medicine and promises to become a strategy for selection of therapeutics in psychiatry soon. Understanding some of the key concepts, strategies, and advances in the field of pharmacogenomics can set the stage for adapting emerging findings to the practice of psychopharmacology.

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