We have developed a sequence conservation-based artificial neural network 
predictor called NetDiseaseSNP which classifies nsSNPs as disease-causing or 
neutral. Our method uses the excellent alignment generation algorithm of SIFT to 
identify related sequences and a combination of 31 features assessing sequence 
conservation and the predicted surface accessibility to produce a single score 
which can be used to rank nsSNPs based on their potential to cause disease. 
NetDiseaseSNP classifies successfully disease-causing and neutral mutations. In 
addition, we show that NetDiseaseSNP discriminates cancer driver and passenger 
mutations satisfactorily. Our method outperforms other state-of-the-art methods 
on several disease/neutral datasets as well as on cancer driver/passenger 
mutation datasets and can thus be used to pinpoint and prioritize plausible 
disease candidates among nsSNPs for further investigation. NetDiseaseSNP is 
publicly available as an online tool as well as a web service: 
http://www.cbs.dtu.dk/services/NetDiseaseSNP.