Predicting RNA structures and functions by artificial intelligence

J Zhang, M Lang, Y Zhou, Y Zhang - Trends in Genetics, 2024 - cell.com
RNA functions by interacting with its intended targets structurally. However, due to the
dynamic nature of RNA molecules, RNA structures are difficult to determine experimentally …

[HTML][HTML] Data resources and computational methods for lncRNA-disease association prediction

N Sheng, L Huang, Y Lu, H Wang, L Yang… - Computers in Biology …, 2023 - Elsevier
Increasing interest has been attracted in deciphering the potential disease pathogenesis
through lncRNA-disease association (LDA) prediction, regarding to the diverse functional …

Multi-task prediction-based graph contrastive learning for inferring the relationship among lncRNAs, miRNAs and diseases

N Sheng, Y Wang, L Huang, L Gao… - Briefings in …, 2023 - academic.oup.com
Motivation Identifying the relationships among long non-coding RNAs (lncRNAs),
microRNAs (miRNAs) and diseases is highly valuable for diagnosing, preventing, treating …

LncRNA-disease association identification using graph auto-encoder and learning to rank

Q Liang, W Zhang, H Wu, B Liu - Briefings in Bioinformatics, 2023 - academic.oup.com
Discovering the relationships between long non-coding RNAs (lncRNAs) and diseases is
significant in the treatment, diagnosis and prevention of diseases. However, current …

LDAformer: predicting lncRNA-disease associations based on topological feature extraction and Transformer encoder

Y Zhou, X Wang, L Yao, M Zhu - Briefings in Bioinformatics, 2022 - academic.oup.com
The identification of long noncoding RNA (lncRNA)-disease associations is of great value for
disease diagnosis and treatment, and it is now commonly used to predict potential lncRNA …

Multi-view contrastive heterogeneous graph attention network for lncRNA–disease association prediction

X Zhao, J Wu, X Zhao, M Yin - Briefings in Bioinformatics, 2023 - academic.oup.com
Motivation: Exploring the potential long noncoding RNA (lncRNA)-disease associations
(LDAs) plays a critical role for understanding disease etiology and pathogenesis. Given the …

A survey of deep learning for detecting miRNA-disease associations: databases, computational methods, challenges, and future directions

N Sheng, X Xie, Y Wang, L Huang… - IEEE/ACM …, 2024 - ieeexplore.ieee.org
MicroRNAs (miRNAs) are an important class of non-coding RNAs that play an essential role
in the occurrence and development of various diseases. Identifying the potential miRNA …

HOPEXGB: a consensual model for predicting miRNA/lncRNA-disease associations using a heterogeneous disease-miRNA-lncRNA information network

J He, M Li, J Qiu, X Pu, Y Guo - Journal of Chemical Information …, 2023 - ACS Publications
Predicting disease-related microRNAs (miRNAs) and long noncoding RNAs (lncRNAs) is
crucial to find new biomarkers for the prevention, diagnosis, and treatment of complex …

Node-adaptive graph Transformer with structural encoding for accurate and robust lncRNA-disease association prediction

G Li, P Bai, C Liang, J Luo - BMC genomics, 2024 - Springer
Abstract Background Long noncoding RNAs (lncRNAs) are integral to a plethora of critical
cellular biological processes, including the regulation of gene expression, cell …

GAE-LGA: integration of multi-omics data with graph autoencoders to identify lncRNA–PCG associations

M Gao, S Liu, Y Qi, X Guo, X Shang - Briefings in Bioinformatics, 2022 - academic.oup.com
Long non-coding RNAs (lncRNAs) can disrupt the biological functions of protein-coding
genes (PCGs) to cause cancer. However, the relationship between lncRNAs and PCGs …