| no |
title |
author |
magazine |
year |
volume |
issue |
page(s) |
type |
| 1 |
Aggregator: A machine learning approach to identifying MEDLINE articles that derive from the same underlying clinical trial
|
Shao, Weixiang
|
|
2015
|
74 |
C |
p. 65-70
6 p.
|
article
|
| 2 |
A guide for building biological pathways along with two case studies: hair and breast development
|
Trindade, Daniel
|
|
2015
|
74 |
C |
p. 16-35
20 p.
|
article
|
| 3 |
Application of text mining in the biomedical domain
|
Fleuren, Wilco W.M.
|
|
2015
|
74 |
C |
p. 97-106
10 p.
|
article
|
| 4 |
Assessment of curated phenotype mining in neuropsychiatric disorder literature
|
Fontaine, Jean-Fred
|
|
2015
|
74 |
C |
p. 90-96
7 p.
|
article
|
| 5 |
Cover 1
|
|
|
2015
|
74 |
C |
p. OFC-
1 p.
|
article
|
| 6 |
DISEASES: Text mining and data integration of disease–gene associations
|
Pletscher-Frankild, Sune
|
|
2015
|
74 |
C |
p. 83-89
7 p.
|
article
|
| 7 |
Editorial Board
|
|
|
2015
|
74 |
C |
p. IFC-
1 p.
|
article
|
| 8 |
How to learn about gene function: text-mining or ontologies?
|
Soldatos, Theodoros G.
|
|
2015
|
74 |
C |
p. 3-15
13 p.
|
article
|
| 9 |
Prediction of drug gene associations via ontological profile similarity with application to drug repositioning
|
Kissa, Maria
|
|
2015
|
74 |
C |
p. 71-82
12 p.
|
article
|
| 10 |
Protein–protein interaction predictions using text mining methods
|
Papanikolaou, Nikolas
|
|
2015
|
74 |
C |
p. 47-53
7 p.
|
article
|
| 11 |
Question answering for Biology
|
Neves, Mariana
|
|
2015
|
74 |
C |
p. 36-46
11 p.
|
article
|
| 12 |
Text as data: Using text-based features for proteins representation and for computational prediction of their characteristics
|
Shatkay, Hagit
|
|
2015
|
74 |
C |
p. 54-64
11 p.
|
article
|
| 13 |
Text mining of biomedical literature: Doing well, but we could be doing better
|
Andrade-Navarro, Miguel
|
|
2015
|
74 |
C |
p. 1-2
2 p.
|
article
|
Journal description