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lncRNA - Long Non-coding RNAs02:39

lncRNA - Long Non-coding RNAs

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In humans, more than 80% of the genome gets transcribed. However, only around 2% of the genome codes for proteins. The remaining part produces non-coding RNAs which includes ribosomal RNAs, transfer RNAs, telomerase RNAs, and regulatory RNAs, among other types. A large number of regulatory non-coding RNAs have been classified into two groups depending upon their length – small non-coding RNAs, such as microRNA, which are less than 200 nucleotides in length, and long non-coding RNA...
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MicroRNA (miRNA) are short, regulatory RNA transcribed from introns (non-coding regions of a gene) or intergenic regions (stretches of DNA present between genes). Several processing steps are required to form biologically active, mature miRNA. The initial transcript, called primary miRNA (pri-mRNA), base-pairs with itself, forming a stem-loop structure. Within the nucleus, an endonuclease enzyme, called Drosha, shortens the stem-loop structure into hairpin-shaped pre-miRNA. After the pre-miRNA...
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MicroRNA (miRNA) are short, regulatory RNA transcribed from introns—non-coding regions of a gene—or intergenic regions—stretches of DNA present between genes. Several processing steps are required to form biologically active, mature miRNA. The initial transcript, called primary miRNA (pri-mRNA), base-pairs with itself forming a stem-loop structure. Within the nucleus, an endonuclease enzyme, called Drosha, shortens the stem-loop structure into hairpin-shaped pre-miRNA. After...
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Related Experiment Video

Updated: Dec 6, 2025

mirMachine: A One-Stop Shop for Plant miRNA Annotation
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Predicting Cancer Types From miRNA Stem-loops Using Deep Learning.

Jean-Francois Laplante, Moulay A Akhloufi

    Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
    |October 6, 2020
    PubMed
    Summary
    This summary is machine-generated.

    This study introduces a deep neural network for pinpointing tumor locations using microRNA (miRNA) stem-loop expression data. The model achieved 96.9% accuracy in classifying tumors across 20 anatomical sites, aiding in early cancer diagnosis.

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    Area of Science:

    • Oncology
    • Bioinformatics
    • Computational Biology

    Background:

    • Cancer remains a significant medical challenge, emphasizing the need for advanced diagnostic tools.
    • Early cancer detection is crucial for effective treatment and improved patient outcomes.
    • Advances in genomic sequencing and understanding of nucleic acid roles fuel personalized medicine research.

    Purpose of the Study:

    • To develop a computational method for identifying the anatomical origin of tumors.
    • To leverage microRNA (miRNA) expression profiles for cancer site classification.

    Main Methods:

    • Utilized a deep neural network (DNN) classifier.
    • Trained the model on 27 The Cancer Genome Atlas (TCGA) miRNA stem-loop expression datasets.
    • Classified tumors into 20 distinct anatomical sites.

    Main Results:

    • Achieved a high classification accuracy of 96.9% for tumor site identification.
    • Demonstrated the efficacy of miRNA stem-loop expression data in cancer localization.
    • Validated the potential of the DNN model for clinical application.

    Conclusions:

    • miRNA stem-loop expression patterns contain significant information for determining tumor anatomical sites.
    • Deep learning models can accurately predict cancer locations, supporting precision oncology.
    • This approach offers a novel strategy for non-invasive cancer diagnosis and staging.