The majority of existing computational tools rely on sequence homology and/or 
structural similarity to identify novel microRNA (miRNA) genes. Recently 
supervised algorithms are utilized to address this problem, taking into account 
sequence, structure and comparative genomics information. In most of these 
studies miRNA gene predictions are rarely supported by experimental evidence and 
prediction accuracy remains uncertain. In this work we present a new 
computational tool (SSCprofiler) utilizing a probabilistic method based on 
Profile Hidden Markov Models to predict novel miRNA precursors. Via the 
simultaneous integration of biological features such as sequence, structure and 
conservation, SSCprofiler achieves a performance accuracy of 88.95% sensitivity 
and 84.16% specificity on a large set of human miRNA genes. The trained 
classifier is used to identify novel miRNA gene candidates located within 
cancer-associated genomic regions and rank the resulting predictions using 
expression information from a full genome tiling array. Finally, four of the top 
scoring predictions are verified experimentally using northern blot analysis. 
Our work combines both analytical and experimental techniques to show that 
SSCprofiler is a highly accurate tool which can be used to identify novel miRNA 
gene candidates in the human genome. SSCprofiler is freely available as a web 
service at http://www.imbb.forth.gr/SSCprofiler.html.