Clarion is a multi-label problem transformation method for identifying mRNA subcellular localizations

Bi, Yue; Li, Fuyi; Guo, Xudong; Wang, Zhikang; Pan, Tong; Guo, Yuming; Webb, Geoffrey I.; Yao, Jianhua; Jia, Cangzhi; Song, Jiangning

doi:10.1093/bib/bbac467

Cited by 18 publications

(10 citation statements)

References 47 publications

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“…System-wide studies of RNA subcellular localization (e.g., mRNA [177]) have also paved the way for a more comprehensive analysis of the cellular dynamics [178,179], as proteins are usually transcribed by RNA molecules. Moreover, except for RNA transcripts for protein, other RNAs, like long noncoding RNAs (lncRNAs), may also be involved in many biological functions [180].…”

Section: Future Directionsmentioning

confidence: 99%

A Review for Artificial Intelligence Based Protein Subcellular Localization

Xiao,

Zou,

Wang

et al. 2024

Biomolecules

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Proteins need to be located in appropriate spatiotemporal contexts to carry out their diverse biological functions. Mislocalized proteins may lead to a broad range of diseases, such as cancer and Alzheimer’s disease. Knowing where a target protein resides within a cell will give insights into tailored drug design for a disease. As the gold validation standard, the conventional wet lab uses fluorescent microscopy imaging, immunoelectron microscopy, and fluorescent biomarker tags for protein subcellular location identification. However, the booming era of proteomics and high-throughput sequencing generates tons of newly discovered proteins, making protein subcellular localization by wet-lab experiments a mission impossible. To tackle this concern, in the past decades, artificial intelligence (AI) and machine learning (ML), especially deep learning methods, have made significant progress in this research area. In this article, we review the latest advances in AI-based method development in three typical types of approaches, including sequence-based, knowledge-based, and image-based methods. We also elaborately discuss existing challenges and future directions in AI-based method development in this research field.

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Section: Future Directionsmentioning

confidence: 99%

A Review for Artificial Intelligence Based Protein Subcellular Localization

Xiao,

Zou,

Wang

et al. 2024

Biomolecules

View full text Add to dashboard Cite

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“…To verify this assertion, we investigated the impact of employing CTS selection on performance. Specifically, we applied three CTS selection criteria outlined in [24] and compared them against the scenario without CTS selection. These criteria included: (1) miRAW-6-1:10, necessitating a minimum of 6 base pairs within positions 1-10;…”

Section: Performance Evaluation Of Mimosamentioning

confidence: 99%

“…Typically, they support a limited array of seed match types, including 8mer, 7mer-m8, 7mer-A1, 6mer, and offset 6mer, all considered canonical sites. In contrast, deep learning approaches have shown a remarkable ability to automatically discern intricate data patterns compared to those reliant on feature engineering (23)(24)(25)(26). For instance, Lee et al introduced deepTarget, a model utilizing auto-encoders and stacked recurrent neural networks to analyze sequence interactions (27).…”

Section: Introductionmentioning

confidence: 99%

Advancing microRNA Target Site Prediction with Transformer and Base-Pairing Patterns

Bi,

Li,

Wang

et al. 2024

Preprint

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Micro RNAs (miRNAs) are short non-coding RNAs involved in various cellular processes, playing a crucial role in gene regulation. Identifying miRNA targets remains a central challenge and is pivotal for elucidating the complex gene regulatory networks. Traditional computational approaches have predominantly focused on identifying miRNA targets through perfect Watson-Crick base pairings within the seed region, referred to as canonical sites. However, emerging evidence suggests that perfect seed matches are not a prerequisite for miRNA-mediated regulation, underscoring the importance of also recognizing imperfect, or non-canonical, sites. To address this challenge, we propose Mimosa, a new computational approach that employs the Transformer framework to enhance the prediction of miRNA targets. Mimosa distinguishes itself by integrating contextual, positional, and base-pairing information to capture in-depth attributes, thereby improving its predictive capabilities. Its unique ability to identify non-canonical base-pairing patterns makes Mimosa a standout model, reducing the reliance on pre-selecting candidate targets. Mimosa achieves superior performance in gene-level predictions and also shows impressive performance in site-level predictions across various non-human species through extensive benchmarking tests. To facilitate research efforts in miRNA targeting, we have developed an easy-to-use web server for comprehensive end-to-end predictions, which is publicly available at http://monash.bioweb.cloud.edu.au/Mimosa/.

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“…Regarding the numerical features, we employed two encodings, k-mer and CKSNAP (k-spaced nucleic acid pairs), for extracting primary sequence characteristics [20,21]. These encodings have demonstrated their versatility in various bioinformatics tasks [13,22,32]. Specifically, the k-mer encoding calculates the frequency of k adjacent nucleic acids.…”

Section: Feature Engineeringmentioning

confidence: 99%

Advancing mRNA subcellular localization prediction with graph neural network and RNA structure

Li,

Bi,

Guo

et al. 2023

Preprint

Self Cite

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The asymmetrical distribution of expressed mRNAs tightly controls the precise synthesis of proteins within human cells. This non-uniform distribution, a cornerstone of developmental biology, plays a pivotal role in numerous cellular processes. To advance our comprehension of gene regulatory networks, it is essential to develop computational tools for accurately identifying the subcellular localizations of mRNAs. However, considering multi-localization phenomena remains limited in existing approaches, with none considering the influence of RNA’s secondary structure. In this study, we introduce ‘Allocator’, a deep learning-based model that seamlessly integrates both sequence-level and structure-level information, significantly enhancing the prediction of mRNA multi-localization. The Allocator models equip four efficient feature extractors, each designed to handle different inputs. Two are tailored for sequence-based inputs, incorporating multilayer perceptron and multi-head self-attention mechanisms. The other two are specialized in processing structure-based inputs, employing graph neural networks. Benchmarking results underscore Allocator’s superiority over state-of-the-art methods, showcasing its strength in revealing intricate localization associations. Furthermore, we have developed a user-friendly web server for Allocator, enhancing its accessibility. You can explore Allocator through our web server, available athttp://Allocator.unimelb-biotools.cloud.edu.au.

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Clarion is a multi-label problem transformation method for identifying mRNA subcellular localizations

Cited by 18 publications

References 47 publications

A Review for Artificial Intelligence Based Protein Subcellular Localization

A Review for Artificial Intelligence Based Protein Subcellular Localization

Advancing microRNA Target Site Prediction with Transformer and Base-Pairing Patterns

Advancing mRNA subcellular localization prediction with graph neural network and RNA structure

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