miRLocator applies machine learning algorithms to accurately predict the localization of most likely miRNAs within their pre-miRNAs. One major strength of miRLocator is the fact that the machine learning-based miRNA prediction model can be automatically trained using a set of miRNAs of particular interest, with informative features extracted from miRNA-miRNA* duplexes and the optimized ratio between positive and negative samples.

Cui, H., Zhai, J., Ma, C. (2015). miRLocator: Machine Learning-Based Prediction of Mature MicroRNAs within Plant Pre-miRNA Sequences PLOS ONE 10(11), e0142753. https://dx.doi.org/10.1371/journal.pone.0142753

Zhang, T., Ju, L., Zhai, J., Song, Y., Song, J., Ma, C. (2019). Plant MicroRNAs, Methods and Protocols Methods in molecular biology (Clifton, N.J.) 1932(), 89-97. https://dx.doi.org/10.1007/978-1-4939-9042-9_6

Chuang Ma (马闯)
Professor, Doctoral Supervisor

My research interests include artificial intelligence, abiotic stress and plant breeding.