LncDisease

LncDisease predicts associations between long non-coding RNAs (lncRNAs) and human diseases by using a sequence-based approach that leverages interactions between lncRNAs and microRNAs (miRNAs) to infer potential disease links.

Key Features:

• Sequence-Based Prediction Methodology: Employs a sequence-based approach that integrates sequence data and known interactions between lncRNAs and miRNAs to predict lncRNA–disease associations.
• Validation through Experimental Data: Validates predictions by quantitative real-time PCR (qRT-PCR), including tests in MCF-7 and MDA-MB-231 breast tumor cell lines with a 91.7% validation rate and in human vascular smooth muscle cells treated with angiotensin II with a 75% validation rate.
• Literature Mining for Disease Association Validation: Confirms predicted associations via literature mining, exemplified by predicting six diseases for lncRNA GAS5 and finding literature support for four of them.

Scientific Applications:

• Biomarker discovery: Identify candidate lncRNA biomarkers for disease diagnosis and prognosis by prioritizing lncRNAs associated with human diseases.
• Therapeutic target investigation: Investigate potential therapeutic targets by elucidating roles of specific lncRNAs in disease-related pathways.
• Regulatory network analysis: Enhance understanding of regulatory networks by analyzing interactions between lncRNAs and miRNAs in the context of disease.

Methodology:

Integrates sequence data with known lncRNA–miRNA interactions in a sequence-based prediction framework to infer lncRNA–disease associations.