Artificial attributes in analyzing biomedical databases

Author(s):  
Zsolt Csizmadia ◽  
Peter Hammer ◽  
Béla Vizvári
Keyword(s):  
Author(s):  
V. Maojo ◽  
M. García-Remesal ◽  
H. Billhardt ◽  
J. Crespo ◽  
F. Martín-Sánchez ◽  
...  
Keyword(s):  

2016 ◽  
Vol 2016 ◽  
pp. 1-11
Author(s):  
Sung-Pil Choi

In order to achieve relevant scholarly information from the biomedical databases, researchers generally use technical terms as queries such as proteins, genes, diseases, and other biomedical descriptors. However, the technical terms have limits as query terms because there are so many indirect and conceptual expressions denoting them in scientific literatures. Combinatorial weighting schemes are proposed as an initial approach to this problem, which utilize various indexing and weighting methods and their combinations. In the experiments based on the proposed system and previously constructed evaluation collection, this approach showed promising results in that one could continually locate new relevant expressions by combining the proposed weighting schemes. Furthermore, it could be ascertained that the most outperforming binary combinations of the weighting schemes, showing the inherent traits of the weighting schemes, could be complementary to each other and it is possible to find hidden relevant documents based on the proposed methods.


Author(s):  
Ruitao Zhang ◽  
Huirong Shi ◽  
Fang Ren ◽  
Wei Feng ◽  
Yuan Cao ◽  
...  

Abstract Background Downregulation of microRNA-338-3p (miR-338-3p) was detected in many malignant tumors, which indicated miR-338-3p might serve as a role of antioncogene in those cancers. The present study aimed to explore the roles of miR-338-3p in the growth and metastasis of ovarian cancer cells and elaborate the underlying possible molecular mechanism. Methods Multiply biomedical databases query and KEGG pathway enrichment assay were used to infilter possible target genes and downstream pathways regulated by miR-338-3p. Overexpression miR-338-3p lentiviral vectors were transfected into ovarian cancer OVCAR-3 and OVCAR-8 cells, cell proliferation, migration and invasion were analyzed by MTT, colony formation, transwell, Matrigel assay and xenograft mouse model. One 3′-untranslated regions (UTRs) binding target gene of miR-338-3p, MACC1 (MET transcriptional regulator MACC1), and its regulated gene MET and downstream signaling pathway activities were examined by western blot. Results Biomedical databases query indicated that miR-338-3p could target MACC1 gene and regulate Met, downstream Wnt/Catenin beta and MEK/ERK pathways. Rescue of miR-338-3p could inhibit the proliferation, migration and invasion of ovarian cancer cells, and suppress the growth and metastasis of xenograft tumor. Restoration of miR-338-3p could attenuate MACC1 and Met overexpression induced growth, epithelial to mesenchymal transition (EMT) and activities of Wnt/Catenin beta and MEK/ERK signaling in vitro and in vivo. Conclusions The present data indicated that restoration of miR-338-3p could suppress the growth and metastasis of ovarian cancer cells, which might due to the inhibition of proliferation and EMT induced by MACC1, Met and its downstream Wnt/Catenin beta and MEK/ERK signaling pathways.


BMJ ◽  
2020 ◽  
pp. m4265
Author(s):  
Andrea Manca ◽  
Lucia Cugusi ◽  
Andrea Cortegiani ◽  
Giulia Ingoglia ◽  
David Moher ◽  
...  

2012 ◽  
Vol 5 (1) ◽  
pp. 604 ◽  
Author(s):  
Mizuki Morita ◽  
Yoshinobu Igarashi ◽  
Maori Ito ◽  
Yi-An Chen ◽  
Chioko Nagao ◽  
...  

Author(s):  
Linda S Murphy ◽  
Sibylle Reinsch ◽  
Wadie I Najm ◽  
Vivian M Dickerson ◽  
Michael A Seffinger ◽  
...  

2020 ◽  
Author(s):  
Eduardo Rosado ◽  
Miguel Garcia-Remesal Sr ◽  
Sergio Paraiso-Medina Sr ◽  
Alejandro Pazos Sr ◽  
Victor Maojo Sr

BACKGROUND Currently, existing biomedical literature repositories do not commonly provide users with specific means to locate and remotely access biomedical databases. OBJECTIVE To address this issue we developed BiDI (Biomedical Database Inventory), a repository linking to biomedical databases automatically extracted from the scientific literature. BiDI provides an index of data resources and a path to access them in a seamless manner. METHODS We designed an ensemble of Deep Learning methods to extract database mentions. To train the system we annotated a set of 1,242 articles that included mentions to database publications. Such a dataset was used along with transfer learning techniques to train an ensemble of deep learning NLP models based on the task of database publication detection. RESULTS The system obtained an f1-score of 0.929 on database detection, showing high precision and recall values. Applying this model to the PubMed and PubMed Central databases we identified over 10,000 unique databases. The ensemble also extracts the web links to the reported databases, discarding the irrelevant links. For the extraction of web links the model achieved a cross-validated f1-score of 0.908. We show two use cases, related to “omics” and the COVID-19 pandemia. CONCLUSIONS BiDI enables the access of biomedical resources over the Internet and facilitates data-driven research and other scientific initiatives. The repository is available at (http://gib.fi.upm.es/bidi/) and will be regularly updated with an automatic text processing pipeline. The approach can be reused to create repositories of different types (biomedical and others).


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