MicroRNAs (miRNAs) are non-coding, short (21-23nt) regulators of protein-coding 
genes that are generally transcribed first into primary miRNA (pri-miR), 
followed by the generation of precursor miRNA (pre-miR). This finally leads to 
the production of the mature miRNA. A large amount of information is available 
on the pre- and mature miRNAs. However, very little is known about the pri-miRs, 
due to a lack of knowledge about their transcription start sites (TSSs). Based 
on the genomic loci, miRNAs can be categorized into two types --intragenic 
(intra-miR) and intergenic (inter-miR). While it is already an established fact 
that intra-miRs are commonly transcribed in conjunction with their host genes, 
the transcription machinery of inter-miRs is poorly understood. Although it is 
assumed that miRNA promoters are similar in structure to gene promoters, since 
both are transcribed by RNA polymerase II (Pol II), computational validations 
exhibit poor performance of gene promoter prediction methods on miRNAs. In this 
paper, we concentrate on the problem of TSS prediction for miRNAs. The present 
study begins with the identification of positive and negative promoter samples 
from recently published data stemming from RNA-sequencing studies. From these 
samples of experimentally validated miRNA TSSs, a number of standard sequence 
features are extracted. Furthermore, to account for potential footprints related 
to promoter regulation by CpG dinucleotide targeted DNA methylation, a number of 
novel features are defined. We develop a support vector machine (SVM) with RBF 
kernel for the prediction of miRNA TSSs trained on human miRNA promoters. A 
novel feature reduction technique based on archived multi-objective simulated 
annealing (AMOSA) identifies the final set of features. The resulting model 
trained on miRNA promoters shows improved performance over the one trained on 
protein-coding gene promoters in terms of classification accuracy, sensitivity 
and specificity. Results are also reported for a completely independent 
biologically validated test set. In a part of the investigation, the proposed 
approach is used to predict protein-coding gene TSSs. It shows a significantly 
improved performance when compared to previously published gene TSS prediction 
methods.