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Related Concept Videos

RNA-seq03:21

RNA-seq

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RNA sequencing, or RNA-Seq, is a high-throughput sequencing technology used to study the transcriptome of a cell. Transcriptomics helps to interpret the functional elements of a genome and identify the molecular constituents of an organism. Additionally, it also helps in understanding the development of an organism and the occurrence of diseases. 
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RNA Structure01:23

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The basic structure of RNA consists of a five-carbon sugar and one of four nitrogenous bases. Although most RNA is single-stranded, it can form complex secondary and tertiary structures. Such structures play essential roles in the regulation of transcription and translation.
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RNA Structure01:19

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The basic structure of RNA consists of a string of ribonucleotides attached by phosphodiester bonds. Although most RNA is single-stranded, it can form complex secondary and tertiary structures. Such structures play essential roles in the regulation of transcription and translation.
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During most eukaryotic translation processes, the small 40S ribosome subunit scans an mRNA from its 5' end until it encounters the first start AUG codon. The large 60S ribosomal subunit then joins the smaller one to initiate protein synthesis. The location of the translation initiation is largely determined by the nucleotides near the start codon as there may be multiple translation initiation sites present on the mRNA.  Marilyn Kozak discovered that the sequence RCCAUGG (where R...
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Related Experiment Video

Updated: Mar 12, 2026

Identification of Circular RNAs using RNA Sequencing
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Identification of Circular RNAs using RNA Sequencing

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LMSCDA: A Secondary Structure Enhanced Language Model for Predicting CircRNA and Disease Associations.

Mian-Shuo Lu, Lei Wang, Meng-Meng Wei

    IEEE Journal of Biomedical and Health Informatics
    |March 10, 2026
    PubMed
    Summary
    This summary is machine-generated.

    This study introduces LMSCDA, a novel method using language models to predict circular RNA-disease associations (CDAs) by enhancing feature representation. The approach significantly improves accuracy, aiding disease diagnosis and treatment strategies.

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    Area of Science:

    • Bioinformatics
    • Computational Biology
    • Genomics

    Background:

    • Circular RNAs (circRNAs) are crucial non-coding RNAs involved in disease pathogenesis and treatment.
    • Accurate prediction of circRNA-disease associations (CDAs) is vital for diagnosis.
    • Existing computational methods often lack sophisticated representation of circRNA structures.

    Purpose of the Study:

    • To develop a novel computational method, LMSCDA, for predicting CDAs.
    • To enhance the representation of circRNAs and diseases using language models.
    • To improve the accuracy and reliability of CDA prediction.

    Main Methods:

    • Calculating circRNA secondary structures using chemical principles.
    • Employing hierarchical feature extraction with an attention mechanism for circRNA structure and semantic features.
    • Utilizing a biomedical language model for disease semantic feature encoding.
    • Integrating network-based behavioral features from circRNA-miRNA and circRNA-disease networks.

    Main Results:

    • LMSCDA achieved high performance with an AUC of 0.9877 and AUPR of 0.9881 in 5-fold cross-validation.
    • The method demonstrated competitive results compared to existing models.
    • A case study on breast cancer confirmed 19 of the top 20 predicted associations.
    • Identification of highly differentially expressed circRNAs in an independent clinical dataset.

    Conclusions:

    • LMSCDA offers a powerful and accurate approach for predicting circRNA-disease associations.
    • The enhanced feature representation significantly improves prediction performance.
    • LMSCDA has potential applications in disease diagnosis and identifying novel therapeutic targets.