Machine learning for small interfering RNAs: a concise review of recent developments

The advent of machine learning and its subsequent integration into small interfering RNA (siRNA) research heralds a new epoch in the field of RNA interference (RNAi). This review emphasizes the urgency and relevance of assimilating the plethora of contributions and advancements in this domain, particularly focusing on the period of 2019-2023. Given the rapid progression of deep learning technologies, our synthesis of recent research is paramount to staying apprised of the state-of-the-art methods being utilized. It not only offers a comprehensive insight into the confluence of machine learning and siRNA but also serves as a beacon, guiding future explorations in this intersectional research field. Our rigorous examination of studies promises a discerning perspective on the contemporary landscape of machine learning applications in siRNA design and function. This review is an effort to foster further discourse and propel academic inquiry in this multifaceted domain.

Automated summary

The integration of machine learning and small interfering RNA (siRNA) research has revolutionized the field of RNA interference (RNAi). This review emphasizes the importance of assimilating advancements in this domain from 2019 to 2023, given the rapid progress of deep learning technologies. It provides comprehensive insight into the confluence of machine learning and siRNA, guiding future explorations in this research field.