AIMC Topic: Adenine
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Deep learning-driven drug response prediction and mechanistic insights in cancer genomics.
Scientific reports
Jul 1, 2025
In the field of cancer therapy, the diversity and heterogeneity of cancer genomes in clinical patients complicate and challenge the effective use of non-targeted drugs, as these drugs often fail to address specific genetic events. Recent advancements...
DeepRice6mA: A convolutional neural network approach for 6mA site prediction in the rice Genome.
PloS one
Jun 18, 2025
As one of the most critical post-replication modifications, N6-methylation (6mA) at adenine residue plays an important role in a variety of biological functions. Existing computational methods for identifying 6mA sites across large genomic regions te...
Transformer-based deep learning for accurate detection of multiple base modifications using single molecule real-time sequencing.
Communications biology
Apr 14, 2025
We had previously reported a convolutional neural network (CNN) based approach, called the holistic kinetic model (HK model 1), for detecting 5-methylcytosine (5mC) by single molecule real-time sequencing (Pacific Biosciences). In this study, we cons...
N6-methyladenine identification using deep learning and discriminative feature integration.
BMC medical genomics
Mar 29, 2025
N6-methyladenine (6 mA) is a pivotal DNA modification that plays a crucial role in epigenetic regulation, gene expression, and various biological processes. With advancements in sequencing technologies and computational biology, there is an increasin...
Ense-i6mA: Identification of DNA N-Methyladenine Sites Using XGB-RFE Feature Selection and Ensemble Machine Learning.
IEEE/ACM transactions on computational biology and bioinformatics
Dec 10, 2024
DNA N-methyladenine (6mA) is an important epigenetic modification that plays a vital role in various cellular processes. Accurate identification of the 6mA sites is fundamental to elucidate the biological functions and mechanisms of modification. How...
SNN6mA: Improved DNA N6-methyladenine site prediction using Siamese network-based feature embedding.
Computers in biology and medicine
Sep 27, 2023
DNA N6-methyladenine (6mA) is one of the most common and abundant modifications, which plays essential roles in various biological processes and cellular functions. Therefore, the accurate identification of DNA 6mA sites is of great importance for a ...
Prediction of base editor off-targets by deep learning.
Nature communications
Sep 2, 2023
Due to the tolerance of mismatches between gRNA and targeting sequence, base editors frequently induce unwanted Cas9-dependent off-target mutations. Here, to develop models to predict such off-targets, we design gRNA-off- target pairs for adenine bas...
GC6mA-Pred: A deep learning approach to identify DNA N6-methyladenine sites in the rice genome.
Methods (San Diego, Calif.)
Feb 9, 2022
MOTIVATION: DNA N6-methyladenine (6mA) is a pivotal DNA modification for various biological processes. More accurate prediction of 6mA methylation sites plays an irreplaceable part in grasping the internal rationale of related biological activities. ...
Using k-mer embeddings learned from a Skip-gram based neural network for building a cross-species DNA N6-methyladenine site prediction model.
Plant molecular biology
Nov 29, 2021
This study used k-mer embeddings as effective feature to identify DNA N6-Methyladenine sites in plant genomes and obtained improved performance without substantial effort in feature extraction, combination and selection. Identification of DNA N6-meth...
A convolution based computational approach towards DNA N6-methyladenine site identification and motif extraction in rice genome.
Scientific reports
May 14, 2021
DNA N6-methylation (6mA) in Adenine nucleotide is a post replication modification responsible for many biological functions. Automated and accurate computational methods can help to identify 6mA sites in long genomes saving significant time and money...
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