nr |
titel |
auteur |
tijdschrift |
jaar |
jaarg. |
afl. |
pagina('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. |
artikel |
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. |
artikel |
3 |
Application of text mining in the biomedical domain
|
Fleuren, Wilco W.M. |
|
2015 |
74 |
C |
p. 97-106 10 p. |
artikel |
4 |
Assessment of curated phenotype mining in neuropsychiatric disorder literature
|
Fontaine, Jean-Fred |
|
2015 |
74 |
C |
p. 90-96 7 p. |
artikel |
5 |
Cover 1
|
|
|
2015 |
74 |
C |
p. OFC- 1 p. |
artikel |
6 |
DISEASES: Text mining and data integration of disease–gene associations
|
Pletscher-Frankild, Sune |
|
2015 |
74 |
C |
p. 83-89 7 p. |
artikel |
7 |
Editorial Board
|
|
|
2015 |
74 |
C |
p. IFC- 1 p. |
artikel |
8 |
How to learn about gene function: text-mining or ontologies?
|
Soldatos, Theodoros G. |
|
2015 |
74 |
C |
p. 3-15 13 p. |
artikel |
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. |
artikel |
10 |
Protein–protein interaction predictions using text mining methods
|
Papanikolaou, Nikolas |
|
2015 |
74 |
C |
p. 47-53 7 p. |
artikel |
11 |
Question answering for Biology
|
Neves, Mariana |
|
2015 |
74 |
C |
p. 36-46 11 p. |
artikel |
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. |
artikel |
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. |
artikel |