BACKGROUND: Though most of the transcripts are long non-coding RNAs (lncRNAs), 
little is known about their functions. lncRNAs usually function through 
interactions with proteins, which implies the importance of identifying the 
binding proteins of lncRNAs in understanding the molecular mechanisms underlying 
the functions of lncRNAs. Only a few approaches are available for predicting 
interactions between lncRNAs and proteins. In this study, we introduce a new 
method lncPro.
RESULTS: By encoding RNA and protein sequences into numeric vectors, we used 
matrix multiplication to score each RNA-protein pair. This score can be used to 
measure the interactions between an RNA-protein pair. This method effectively 
discriminates interacting and non-interacting RNA-protein pairs and predicts 
RNA-protein interactions within a given complex. Applying this method on all 
human proteins, we found that the long non-coding RNAs we collected tend to 
interact with nuclear proteins and RNA-binding proteins.
CONCLUSIONS: Compared with the existing approaches, our method shortens the time 
for training matrix and obtains optimal results based on the model being used. 
The ability of predicting the associations between lncRNAs and proteins has also 
been enhanced. Our method provides an idea on how to integrate different 
information into the prediction process.