@article{Raden_Wallach_Miladi-Struc_machi_learn-2021,
author = {Raden, Martin and Wallach, Thomas and Miladi, Milad and 
          Zhai, Yuanyuan and Kruger, Christina and Mossmann, Zoe J. 
          and Dembny, Paul and Backofen, Rolf and Lehnardt, Seija},
title = {Structure-aware machine learning identifies {microRNAs} 
         operating as {Toll}-like receptor 7/8 ligands},
journal = {RNA Biol},
year = {2021},
doi = {10.1080/15476286.2021.1940697},
volume = {18},
user = {backofen},
pmid = {34241565},
pages = {268-277},
number = {sup1},
issn = {1547-6286},
abstract = {MicroRNAs (miRNAs) can serve as activation signals for 
            membrane receptors, a recently discovered function that is 
            independent of the miRNAs' conventional role in 
            post-transcriptional gene regulation. Here, we introduce a 
            machine learning approach, BrainDead, to identify 
            oligonucleotides that act as ligands for single-stranded 
            RNA-detecting Toll-like receptors (TLR)7/8, thereby 
            triggering an immune response. BrainDead was trained on 
            activation data obtained from in vitro experiments on murine 
            microglia, incorporating sequence and intra-molecular 
            structure, as well as inter-molecular homo-dimerization 
            potential of candidate RNAs. The method was applied to 
            analyse all known human miRNAs regarding their potential to 
            induce TLR7/8 signalling and microglia activation. We 
            validated the predicted functional activity of subsets of 
            high- and low-scoring miRNAs experimentally, of which a 
            selection has been linked to Alzheimer's disease. High 
            agreement between predictions and experiments confirms the 
            robustness and power of BrainDead. The results provide new 
            insight into the mechanisms of how miRNAs act as TLR 
            ligands. Eventually, BrainDead implements a generic machine 
            learning methodology for learning and predicting the 
            functions of short RNAs in any context.}
}