AI techniques have facilitated the understanding of epitranscriptome distribution

Daiyun Huang; Jia Meng; Kunqi Chen

doi:10.1016/j.xgen.2024.100718

AI techniques have facilitated the understanding of epitranscriptome distribution

Daiyun Huang, Jia Meng, Kunqi Chen^*

^*Corresponding author for this work

Research output: Contribution to journal › Comment/debate

1 Citation (Scopus)

Abstract

N⁶-methyladenosine (m6A), the most prevalent internal mRNA modification in higher eukaryotes, plays diverse roles in cellular regulation. By incorporating both sequence- and genome-derived features, Fan et al.¹ designed a novel Transformer-BiGRU framework that achieves superior performance in computational m6A identification, thus demonstrating the potential of AI in genomic studies.

Original language	English
Article number	100718
Journal	Cell Genomics
Volume	4
Issue number	12
DOIs	https://doi.org/10.1016/j.xgen.2024.100718
Publication status	Published - 11 Dec 2024

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10.1016/j.xgen.2024.100718

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@article{e4ac9b29e0a7451e840dd131a7442fd0,

title = "AI techniques have facilitated the understanding of epitranscriptome distribution",

abstract = "N6-methyladenosine (m6A), the most prevalent internal mRNA modification in higher eukaryotes, plays diverse roles in cellular regulation. By incorporating both sequence- and genome-derived features, Fan et al.1 designed a novel Transformer-BiGRU framework that achieves superior performance in computational m6A identification, thus demonstrating the potential of AI in genomic studies.",

author = "Daiyun Huang and Jia Meng and Kunqi Chen",

note = "Publisher Copyright: {\textcopyright} 2024 The Author(s)",

year = "2024",

month = dec,

day = "11",

doi = "10.1016/j.xgen.2024.100718",

language = "English",

volume = "4",

journal = "Cell Genomics",

issn = "2666-979X",

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TY - JOUR

T1 - AI techniques have facilitated the understanding of epitranscriptome distribution

AU - Huang, Daiyun

AU - Meng, Jia

AU - Chen, Kunqi

PY - 2024/12/11

Y1 - 2024/12/11

N2 - N6-methyladenosine (m6A), the most prevalent internal mRNA modification in higher eukaryotes, plays diverse roles in cellular regulation. By incorporating both sequence- and genome-derived features, Fan et al.1 designed a novel Transformer-BiGRU framework that achieves superior performance in computational m6A identification, thus demonstrating the potential of AI in genomic studies.

AB - N6-methyladenosine (m6A), the most prevalent internal mRNA modification in higher eukaryotes, plays diverse roles in cellular regulation. By incorporating both sequence- and genome-derived features, Fan et al.1 designed a novel Transformer-BiGRU framework that achieves superior performance in computational m6A identification, thus demonstrating the potential of AI in genomic studies.

UR - https://www.scopus.com/pages/publications/85211223669

U2 - 10.1016/j.xgen.2024.100718

DO - 10.1016/j.xgen.2024.100718

M3 - Comment/debate

AN - SCOPUS:85211223669

SN - 2666-979X

VL - 4

JO - Cell Genomics

JF - Cell Genomics

IS - 12

M1 - 100718

ER -

AI techniques have facilitated the understanding of epitranscriptome distribution

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