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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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Gene expression can be regulated at almost every step from gene to protein. Transcription is the step that is most commonly regulated. This involves the binding of proteins to short regulatory sequences on the DNA. This association can either promote or inhibit the transcription of a gene associated with the respective sequence.
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In eukaryotes, transcription and translation are compartmentalized; an mRNA is first synthesized in the nucleus and then selectively transported to the cytoplasm for protein synthesis. Before transport, a pre-mRNA undergoes several steps of post-transcriptional modifications including splicing, 5' capping, and the addition of a poly-adenine tail. Various proteins bind to the pre-mRNA during these modifications. The mRNA transport takes place with the help of multiple proteins playing...
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The gene expression in cells is regulated at different stages: (i) transcription, (ii) RNA processing, (iii) RNA localization, and (iv) translation. Transcriptional regulation is mediated by regulatory proteins such as transcription factors, activators, or repressors—these control gene expression by initiating or inhibiting the transcription of genes. Once a precursor or pre-mRNA is produced, it undergoes post-transcriptional modification, including 5' capping, splicing, and the...
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A gene is a stretch of DNA that serves as the blueprint for functional RNAs and proteins. Since DNA is comprised  of nucleotides and proteins are comprised of amino acids, a mediator is required to convert the information encoded in DNA into proteins. This mediator is the messenger RNA (mRNA). mRNA copies the blueprint from DNA by a process called transcription. In eukaryotes, transcription occurs in the nucleus by complementary base-pairing with the DNA template. The mRNA is then...
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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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Large Language Models and Genomics for Summarizing the Role of microRNA in Regulating mRNA Expression.

Balu Bhasuran1, Sharanya Manoharan2, Oviya Ramalakshmi Iyyappan3

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This study evaluated machine learning, deep learning, and large language models for extracting microRNA-messenger RNA interactions from PubMed. PubMedBERT excelled, while Llama 2 demonstrated strong recall, highlighting potential for improved precision.

Keywords:
deep learninggenomicslarge language modelsmachine learningmiRNA–mRNA interactionsnatural language processing

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Area of Science:

  • Molecular Biology
  • Bioinformatics
  • Computational Biology

Background:

  • microRNA-messenger RNA (miRNA-mRNA) interactions are crucial for gene regulation, cellular processes, and disease pathogenesis.
  • Efficiently extracting these interactions from scientific literature is a significant challenge.

Purpose of the Study:

  • To evaluate and compare the performance of Machine Learning (ML), Deep Learning-based Transformer (DLT), and Large Language Models (LLMs) in extracting miRNA-mRNA interactions from PubMed.
  • To validate extracted interactions using genomic approaches.

Main Methods:

  • Annotation of a miRNA-mRNA Interaction Corpus (MMIC).
  • Evaluation of ML, DLT, and LLM models, including PubMedBERT and Llama 2, for miRNA-mRNA interaction extraction.
  • Genomic validation of extracted interactions.

Main Results:

  • PubMedBERT achieved the highest performance among ML, DLT, and LLM models, with precision, recall, and F-score of 0.783.
  • Llama 2 demonstrated superior recall (0.86-0.87) compared to ML and DLT models in zero-shot and three-shot experiments.
  • While Llama 2 shows promise, further improvements in precision and F-score are needed.

Conclusions:

  • LLMs, particularly Llama 2, show potential for advancing miRNA-mRNA interaction extraction from biomedical literature.
  • PubMedBERT currently offers the best performance for this task.
  • Further research is warranted to enhance the precision and F-score of LLMs for more comprehensive extraction.