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Related Concept Videos

MicroRNAs01:22

MicroRNAs

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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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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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A related convolutional neural network for cancer diagnosis using microRNA data classification.

Najmeh Sadat Jaddi1, Salwani Abdullah2, Say Leng Goh3

  • 1Faculty of Computer Engineering Iranian eUniversity Tehran Iran.

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|December 25, 2024
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This study introduces a novel method for cancer classification using a genetic algorithm-optimized convolutional neural network (CNN). The approach achieves high accuracy in identifying 29 cancer types from microRNA data.

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Area of Science:

  • Genomics
  • Bioinformatics
  • Computational Biology

Background:

  • MicroRNA (miRNA) expression profiles are crucial biomarkers for cancer classification.
  • Convolutional Neural Networks (CNNs) have demonstrated efficacy in various pattern recognition tasks.
  • Existing classification methods may face challenges with high-dimensional genomic data and computational efficiency.

Purpose of the Study:

  • To develop and evaluate a novel, computationally efficient method for cancer classification using miRNA data.
  • To enhance classification accuracy by employing a union of two CNNs optimized by a genetic algorithm.
  • To compare the proposed method's performance against established classifiers on a large-scale real-world dataset.

Main Methods:

  • A convolutional neural network (CNN)-based model optimized by a genetic algorithm was developed.
  • The method utilizes a union of two CNNs to leverage inter-network knowledge exchange.
  • The approach was tested on a microRNA dataset comprising genomic information from 8129 patients across 29 cancer types.

Main Results:

  • The proposed method achieved 100% classification accuracy in 24 out of 29 cancer types.
  • In seven specific cases, the method attained 100% accuracy, surpassing previously reported results.
  • Performance analysis demonstrated superior accuracy compared to 22 well-known classifiers and 77 previously reported classifiers.

Conclusions:

  • The developed CNN-based method offers a highly accurate and computationally efficient solution for cancer classification from miRNA data.
  • The union of CNNs and genetic algorithm optimization effectively enhances classification performance.
  • This approach holds significant potential for improving diagnostic accuracy in oncology.