The era of big data and high-throughput genomic technology has enabled scientists to
have a clear view of plant genomic profiles. However, it has also led to a massive need for
computational tools and strategies to interpret this data. In this scenario of huge data inflow,
machine learning (ML) approaches are emerging to be the most promising for analysing
heterogeneous and unstructured biological datasets. Extending its application to healthcare and
agriculture, ML approaches are being useful for microRNA (miRNA) screening as well.
Identification of miRNAs is a crucial step towards understanding post-transcriptional gene
regulation and miRNA-related pathology. The use of ML tools is becoming indispensable in
analysing such data and identifying species-specific, non-conserved miRNA. However, these
techniques have their own benefits and lacunas. In this review, we will discuss the current scenario
and pitfalls of ML-based tools for plant miRNA identification and provide some insights into the
important features, the need for deep learning models and direction in which studies are needed.
Keywords: microRNA, dataset, algorithm, feature selection, plant genomics, machine learning.
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