Abstract
Although disease prediction by microRNAs (miRNAs) based on mathematical data science has been statistically validated, no disease prediction can output etiology and therapeutic targets. Also, in silico network analysis using miRNA panels indicated therapeutic target proteins, but lacked data for statistical validation, resulting in a large number of putative targets. If data bioscience does not follow the laws of physics, just as it does in the Metaverse world, disease predictions cannot become reality. Therefore, the use of miRNA panels as diagnostic tools will require new algorithms to compensate for these shortcomings. So, we tried to completely fix the bug using miRNA entangling target sorting/quantum miRNA language plus artificial intelligence (METS/MIRAI).
Applaud, my friends, the comedy is over.—van Beethoven L
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Fujii, Y.R. (2023). Etiology Analysis for Human Cancer. In: The MicroRNA Quantum Code Book. Springer, Singapore. https://doi.org/10.1007/978-981-19-8586-7_6
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DOI: https://doi.org/10.1007/978-981-19-8586-7_6
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