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AI and ML methods for RNA subcellular localization reviewed

By PulseAugur Editorial · [1 sources] ·

This paper reviews the latest advancements in AI and machine learning approaches for predicting the subcellular localization of various RNA types. It covers sequence-based, image-based, and hybrid methodologies, highlighting their potential to accelerate RNA research and guide disease treatments. The review also discusses challenges such as data scarcity and the lack of benchmarks in AI/ML applications for RNA localization. AI

IMPACT Provides a comprehensive overview of AI/ML applications in RNA research, potentially guiding future development and disease treatment strategies.

RANK_REASON The item is a review paper published on arXiv. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.AI →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

AI and ML methods for RNA subcellular localization reviewed

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The item is a review paper published on arXiv. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.AI TIER_1 English(EN) · Cece Zhang, Xuehuan Zhu, Nick Peterson, Jieqiong Wang, Shibiao Wan ·

    A Comprehensive Review on RNA Subcellular Localization Prediction

    arXiv:2504.17162v2 Announce Type: replace-cross Abstract: The subcellular localization of RNAs, including long non-coding RNAs (lncRNAs), messenger RNAs (mRNAs), microRNAs (miRNAs) and other smaller RNAs, plays a critical role in determining their biological functions. For instan…

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