DFM-ac4C, a novel computational framework designed for the accurate prediction of ac4C modification sites, significantly outperforms existing models, achieving outstanding predictive metrics, and underscores DFM-ac4C's effectiveness as a robust and efficient tool for RNA ac4C site identification.
Identifying transcription factor binding sites (TFBSs) is fundamental to understanding complex gene regulatory mechanisms and the functions of non-coding regions. Although existing methods have achieved substantial strides, capturing both local structural features and long-range spatial dependencies within DNA sequence...
Jingjue Wei, Jie Feng· Match-communications in Math...· 0 citations
A concatenated deep neural network that incorporates both peptide and nucleotide embedding from masked language modeling (MLM), being able to discover complex associations between sequence information and lncRNA classification.
Zongrui Dai, Feiyang Deng, Hsiao H. Sung· bioRxiv· 4 citations
Case studies further validated predicted circRNA-drug associations, including cisplatin, enzalutamide, and sorafenib, against published experimental findings, demonstrating HMCDSP's capacity to reveal clinically relevant biomarkers and inform personalized therapeutic strategies.
Yong-Tian Wang, Wen-Kai Shen, Jiahao Li et al.· Interdisciplinary Sciences C...· 0 citations
MKMC (Multi-sample Kmer Counter), a scalable, reference-free toolkit for RNA-seq analysis that leverages k-mer–based statistics to detect biological variation without requiring alignment, is presented.
L. Mboning, Maciej Dlugosz, Marek Kokot et al.· bioRxiv· 0 citations
It is demonstrated that sequence and structure provide complementary predictive information, and that sites with greater prediction sensitivity to structural perturbation exhibit distinct local structural profiles between cell lines, and provides a multifeature deep learning framework for accurate and interpretable str...
Ming-Ze Sun, Di Zhang, Zhi-Yuan Li et al.· PLoS Computational Biology· 0 citations
PLM-ArgMe is presented that is based on a symmetry-sensitive Transformer framework using context-aware ESM-2 residue embeddings, which is mapped through a novel Bio-Symmetric Mirrored Sinusoidal Encoding strategy to address the biological symmetry hypothesis of arginine methylation.
Nitika Bhatt, Kartik Joshi, R. Rout et al.· Biochemical and Biophysical...· 0 citations
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