Tsukiyama, Sho; Hasan, Mehedi Md; Deng, Hong-Wen; Kurata, Hiroyuki published an article in 2022, the title of the article was BERT6mA: prediction of DNA N6-methyladenine site using deep learning-based approaches.Recommanded Product: 443-72-1 And the article contains the following content:
N6-methyladenine (6mA) is associated with important roles in DNA replication, DNA repair, transcription, regulation of gene expression. Several exptl. methods were used to identify DNA modifications. However, these exptl. methods are costly and time-consuming. To detect the 6mA and complement these shortcomings of exptl. methods, we proposed a novel, deep leaning approach called BERT6mA. To compare the BERT6mA with other deep learning approaches, we used the benchmark datasets including 11 species. The BERT6mA presented the highest AUCs in eight species in independent tests. Furthermore, BERT6mA showed higher and comparable performance with the state-of-the-art models while the BERT6mA showed poor performances in a few species with a small sample size. To overcome this issue, pretraining and fine-tuning between two species were applied to the BERT6mA. The pretrained and fine-tuned models on specific species presented higher performances than other models even for the species with a small sample size. In addition to the prediction, we analyzed the attention weights generated by BERT6mA to reveal how the BERT6mA model extracts critical features responsible for the 6mA prediction. The experimental process involved the reaction of N-Methyl-7H-purin-6-amine(cas: 443-72-1).Recommanded Product: 443-72-1
The Article related to dna n6 methyladenine deep learning, 6ma modification prediction, bert, cnn, gru, lstm, word2vec, Biochemical Methods: Biological and other aspects.Recommanded Product: 443-72-1
Referemce:
Imidazole – Wikipedia,
Imidazole | C3H4N2 – PubChem