Development of AI-based tools for functional annotation of lncRNA and its applications

Abstract

Long non-coding RNAs (lncRNAs) are a significant component of the human transcriptome and are well known for their ability to regulate gene expression. They have been implicated in a variety of disease-associated pathways. However, functional annotation of these lncRNAs remains a major challenge in the era of genomics and metagenomics, where large-scale genome sequencing has become routine. In this thesis, we have attempted to develop computational resources for annotating lncRNAs and to explore their applications in cancer diagnostics. In the first part of this thesis, we compiled all lncRNAs resources that includes experimental techniques, prediction methods and data repositories. These resources were manually curated from the literature and made available to the scientific community through a web platform called lncInfo. Next, we developed CytoLNCpred, a cell line–specific computational method for identifying cytoplasm-associated lncRNAs across 15 human cell lines. We implemented multiple computational approaches, including machine learning, deep learning, and large language models; in order to achieve improved prediction accuracy. The second part of the thesis describes a method, lncrnaPI, developed to predict lncRNA–protein interacting pairs using transformer-based sequence embeddings as features. In the final section of the thesis, we explored the cancer diagnostic potential of lncRNAs based on their transcriptomic profiles. Specifically, we analyzed lncRNA transcriptomic profiles in patients with pancreatic ductal adenocarcinoma (PDAC). We explored two independent approaches for identifying lncRNA-based biomarkers capable of discriminating PDAC patients from healthy individuals. First, we developed machine learning–based models using traditional approaches for biomarker identification. Second, we investigated a new concept in which large language models were used for mining transcriptomic data. One key objective of this research work is to facilitate the scientific community working in the field of lncRNA biology. Therefore, we developed web-based servers and standalone tools that are publicly available fostering open science. The work presented in this thesis provides novel computational strategies for enhancing the functional annotation of lncRNAs, decoding their molecular interactions, and exploiting their expression patterns for clinically meaningful applications.