A5081660906- RNA modifications and cancer
- RNA and protein synthesis mechanisms
- Cancer-related molecular mechanisms research
- Machine Learning in Bioinformatics
- Computational Drug Discovery Methods
- Biochemical and Structural Characterization
- Machine Learning in Materials Science
- vaccines and immunoinformatics approaches
- Genomics and Phylogenetic Studies
- Cancer-related gene regulation
- Protein Structure and Dynamics
- Natural Language Processing Techniques
- Speech Recognition and Synthesis
- AI in cancer detection
- Microbial Natural Products and Biosynthesis
- Domain Adaptation and Few-Shot Learning
- Colorectal Cancer Screening and Detection
- Nanopore and Nanochannel Transport Studies
- Algorithms and Data Compression
- Neural Networks and Applications
- Machine Learning in Healthcare
- Antimicrobial Peptides and Activities
Shandong University
2022-2025
Abstract Motivation 5-Methylcytosine (5mC), a fundamental element of DNA methylation in eukaryotes, plays vital role gene expression regulation, embryonic development, and other biological processes. Although several computational methods have been proposed for detecting the base modifications like 5mC sites from Nanopore sequencing data, they face challenges including sensitivity to noise, ignoring imbalanced distribution real-world scenarios. Results Here, we develop NanoCon, deep hybrid...
N-7methylguanosine (m7G) modification plays a crucial role in various biological processes and is closely associated with the development progression of many cancers. Accurate identification m7G sites essential for understanding their regulatory mechanisms advancing cancer therapy. Previous studies often suffered from insufficient research data, underutilization motif information, lack interpretability. In this work, we designed novel motif-based interpretable method site prediction, called...
Antimicrobial peptides are that effective against bacteria and viruses, the discovery of new antimicrobial is great importance to human life health. Although design using machine learning methods has achieved good results in recent years, it remains a challenge learn novel with multiple properties interest from peptide data certain property labels. To this end, we propose Multi-CGAN, deep generative model-based architecture can single-attribute generate sequences attributes need, which may...
Abstract The emergence of non-coding RNA-encoded small peptides (ncPEPs) has sparked significant interest within the realm cancer immunotherapy, owing to their potential as valuable therapeutic targets and biomarkers. identification characterization cancer-associated ncPEPs assume a crucial role in propelling research forward augmenting our comprehension immune-related processes. However, prevailing methods for primarily rely on sequence order, neglecting latent relationships between...
RNA modifications are important for deciphering the function of cells and their regulatory mechanisms. In recent years, researchers have developed many deep learning methods to identify specific modifications. However, these require model retraining each new modification cannot progressively newly identified To address this challenge, we propose an innovative incremental framework that incorporates multiple methods. Our experimental results confirm efficacy strategies in addressing...