In silico prediction of novel therapeutic targets using gene-disease association data.
Enrico FerreroIan DunhamPhilippe SanseauPublished in: Journal of translational medicine (2017)
Our in silico approach shows that data linking genes and diseases is sufficient to predict novel therapeutic targets effectively and confirms that this type of evidence is essential for formulating or strengthening hypotheses in the target discovery process. Ultimately, more rapid and automated target prioritisation holds the potential to reduce both the costs and the development times associated with bringing new medicines to patients.
Keyphrases
- end stage renal disease
- electronic health record
- genome wide
- molecular docking
- newly diagnosed
- ejection fraction
- high throughput
- chronic kidney disease
- small molecule
- big data
- deep learning
- peritoneal dialysis
- prognostic factors
- patient reported outcomes
- transcription factor
- risk assessment
- data analysis
- molecular dynamics simulations