# Classification of Precursor MicroRNAs from Different Species Based on K-mer Distance Features
## 1 Background  
Dysregulation of gene expression is a hallmark of many diseases. MicroRNAs (miRNAs) are also expressed, and their differential regulation has been explored in many studies aimed at finding disease markers. MicroRNAs are post-transcriptional regulators that modify protein expression. Their production starts with expression as primary miRNAs (pri-miRNAs) followed by processing by [PERSON] in the nucleus [1]. This processing leads to precursor miRNAs (pre-miRNAs) that are exported into the cytosol. Mature miRNAs are single-stranded RNA sequences of 18-24 nt in length, which are incorporated as the recognition element into RISC. They are produced from precursor miRNAs via Dicer processing. Once incorporated into RISC, the loaded miRSC complex can act on its target messenger RNAs, with the mature miRNA providing targeting specificity. Although microRNAs have been found in large parts of the phylogenetic tree, the molecular pathways of plants and animals may have evolved independently [2]. However, both pathways share the general processing from pri-miRNA to mature miRNA with the production of pre-miRNA and the final incorporation into a protein complex involvedin translational silencing. While plants are eukaryotes and can be expected to have an miRNA pathway [3], finding miRNAs in viruses may be surprising, but it is efficient for them to encode a small RNA with a potentially large effect [4]. It is important to note that miRNAs are only functional if they are coexpressed with their targets [5]. When both miRNA and target mRNA are present, modulation of the targets' protein expression can occur [6]. MicroRNAs are not expressed at all times and in all tissues, and some may only be expressed in response to cellular stresses. Therefore, it is not possible to determine all miRNAs, their targets, and their interactions experimentally. Hence, computational approaches for miRNA prediction are important, and many such approaches are available [7; 8; 9]. A large part of the tools are based on machine learning (ML). With a few notable exceptions [10; 11; 12], two-class classification is the basis for ML-based miRNA prediction tools. Although it has been found that the complete procedure is essential for ML model establishment [13], one important factor is that negative data used in model establishment comes without any quality guarantee. Positive data, while also containing questionable examples [14; 15; 16], is generally of higher quality because positive interactions can be queried in the lab. In contrast, it would be difficult to do so for negative examples. MicroRNAs and their targets are collected in databases. Examples of such databases are miRarBase [17], TarBase [18], and MirGeneDB [15], which generally depend on miRBase [19], which is the primary collection of all miRNAs. To be able to apply machine learning, it is essential to represent pre-miRNAs in a vector space. Therefore, feature extraction and the types of features used are crucial for model performance [20]. An abundance of features for encoding miRNAs and their secondary structure have been proposed [21]. Since miRNA genesis is a multistep process involving several protein complexes, the structural features of pre-miRNAs are essential [22]. [PERSON] et al. [23] evaluated some miRNA features experimentally using biochemical methods to discover precursor structures and compared their findings to predictive approaches. They found some differences between methods, which were more pronounced for the overall predicted precursor structure but did not strongly affect the stability of its termini. Almost all published features have been implemented by [PERSON] et al. [20] and miRNAfe [24]. Features can be differentiated into sequence-based, thermodynamic, probabilistic, structural, or a mixture thereof. All features can further be normalized, e.g., using another feature. Typically, features such as stem length or the number of stems are used for normalization.