Projects per year
Abstract
Background
The scientific literature contains a wealth of information from different fields on potential disease mechanisms. However, identifying and prioritizing mechanisms for further analytical evaluation presents enormous challenges in terms of the quantity and diversity of published research. The application of data mining approaches to the literature offers the potential to identify and prioritize mechanisms for more focused and detailed analysis.
Methods
Here we present MELODI, a literature mining platform that can identify mechanistic pathways between any two biomedical concepts.
Results
Two case studies demonstrate the potential uses of MELODI and how it can generate hypotheses for further investigation. First, an analysis of ETS-related gene ERG and prostate cancer derives the intermediate transcription factor SP1, recently confirmed to be physically interacting with ERG. Second, examining the relationship between a new potential risk factor for pancreatic cancer identifies possible mechanistic insights which can be studied in vitro.
Conclusions
We have demonstrated the possible applications of MELODI, including two case studies. MELODI has been implemented as a Python/Django web application, and is freely available to use at [www.melodi.biocompute.org.uk].
The scientific literature contains a wealth of information from different fields on potential disease mechanisms. However, identifying and prioritizing mechanisms for further analytical evaluation presents enormous challenges in terms of the quantity and diversity of published research. The application of data mining approaches to the literature offers the potential to identify and prioritize mechanisms for more focused and detailed analysis.
Methods
Here we present MELODI, a literature mining platform that can identify mechanistic pathways between any two biomedical concepts.
Results
Two case studies demonstrate the potential uses of MELODI and how it can generate hypotheses for further investigation. First, an analysis of ETS-related gene ERG and prostate cancer derives the intermediate transcription factor SP1, recently confirmed to be physically interacting with ERG. Second, examining the relationship between a new potential risk factor for pancreatic cancer identifies possible mechanistic insights which can be studied in vitro.
Conclusions
We have demonstrated the possible applications of MELODI, including two case studies. MELODI has been implemented as a Python/Django web application, and is freely available to use at [www.melodi.biocompute.org.uk].
| Original language | English |
|---|---|
| Pages (from-to) | 369–379 |
| Number of pages | 11 |
| Journal | International Journal of Epidemiology |
| Volume | 47 |
| Issue number | 2 |
| Early online date | 12 Jan 2018 |
| DOIs | |
| Publication status | Published - Apr 2018 |
Research Groups and Themes
- ICEP
Access to Document
Handle.net
Full-text PDF (accepted author manuscript)
Accepted author manuscript, 490 KB
Embargoed Document
Fingerprint
Dive into the research topics of 'MELODI: Mining Enriched Literature Objects to Derive Intermediates'. Together they form a unique fingerprint.Projects
- 1 Finished
-
IEU Theme 2
Flach, P. A. (Principal Investigator), Gaunt, T. R. (Principal Investigator) & Gaunt, T. R. (Principal Investigator)
1/06/13 → 31/03/18
Project: Research
Profiles
-
Professor Tom R Gaunt
- Bristol Medical School (PHS) - Professor of Health and Biomedical Informatics and MRC Investigator
- Bristol Population Health Science Institute
- MRC Integrative Epidemiology Unit - Programme lead
Person: Academic , Member
-
Dr Emma E Vincent
- Bristol Medical School (THS) - Associate Professor in Molecular Metabolism
- School of Cellular and Molecular Medicine - Research Fellow
- Bristol Population Health Science Institute
- Cancer
Person: Academic , Member