Big data and deep learning for RNA biology.

The exponential growth of big data in RNA biology (RB) has led to the development of deep learning (DL) models that have driven crucial discoveries. As constantly evidenced by DL studies in other fields, the successful implementation of DL in RB depends heavily on the effective utilization of large-scale datasets from public databases. In achieving this goal, data encoding methods, learning algorithms, and techniques that align well with biological domain knowledge have played pivotal roles. In this review, we provide guiding principles for applying these DL concepts to various problems in RB by demonstrating successful examples and associated methodologies. We also discuss the remaining challenges in developing DL models for RB and suggest strategies to overcome these challenges. Overall, this review aims to illuminate the compelling potential of DL for RB and ways to apply this powerful technology to investigate the intriguing biology of RNA more effectively.

Determinants of Functional MicroRNA Targeting.

MicroRNAs (miRNAs) play cardinal roles in regulating biological pathways and processes, resulting in significant physiological effects. To understand the complex regulatory network of miRNAs, previous studies have utilized massivescale datasets of miRNA targeting and attempted to computationally predict the functional targets of miRNAs. Many miRNA target prediction tools have been developed and are widely used by scientists from various fields of biology and medicine. Most of these tools consider seed pairing between miRNAs and their mRNA targets and additionally consider other determinants to improve prediction accuracy. However, these tools exhibit limited prediction accuracy and high false positive rates. The utilization of additional determinants, such as RNA modifications and RNA-binding protein binding sites, may further improve miRNA target prediction. In this review, we discuss the determinants of functional miRNA targeting that are currently used in miRNA target prediction and the potentially predictive but unappreciated determinants that may improve prediction accuracy.

IMA: Identifying disease-related genes using MeSH terms and association rules.

Genes play an important role in several diseases. Hence, in biology, identifying relationships between diseases and genes is important for the analysis of diseases, because mutated or dysregulated genes play an important role in pathogenesis. Here, we propose a method to identify disease-related genes using MeSH terms and association rules. We identified genes by analyzing the MeSH terms and extracted information on gene-gene interactions based on association rules. By integrating the extracted interactions, we constructed gene-gene networks and identified disease-related genes. We applied the proposed method to study five cancers, including prostate, lung, breast, stomach, and colorectal cancer, and demonstrated that the proposed method is more useful for identifying disease-related and candidate disease-related genes than previously published methods. In this study, we identified 20 genes for each disease. Among them, we presented 34 important candidate genes with evidence that supports the relationship of the candidate genes with diseases.