Publicação
Predicting non-coding RNA function using Artificial Intelligence
| datacite.subject.fos | Departamento de Informática | pt_PT |
| dc.contributor.advisor | Martiniano, Hugo Filipe de Mesquita Costa, 1978- | |
| dc.contributor.advisor | Couto, Francisco José Moreira | |
| dc.contributor.author | Correia, David Alexandre da Costa | |
| dc.date.accessioned | 2025-01-07T10:51:19Z | |
| dc.date.available | 2025-01-07T10:51:19Z | |
| dc.date.issued | 2024 | |
| dc.date.submitted | 2024 | |
| dc.description | Tese de mestrado, Bioinformática e Biologia Computacional, 2024, Universidade de Lisboa, Faculdade de Ciências | pt_PT |
| dc.description.abstract | Non-coding RNAs (ncRNAs) represent the majority of human gene products and are involved in various important biological processes, being considered relevant disease biomarkers and therapeutic agents. However, there are few functional annotation databases dedicated to ncRNAs and information about these biomolecules remains sparsely distributed, mostly in the form of scientific research articles. It is then of pivotal importance to aggregate and summarize the existing information. Natural Language Processing methods applied to text mining enable automatic information extraction and summarization from textual data. These techniques can be used to generate collections of annotated sentences expressing relations between entities, called relational corpora. In this work, a text mining pipeline was implemented to generate a ncRNA-phenotype relational corpus (ncoRP) using Distant Supervision Relation Extraction (DSRE), consisting of 21,608 annotated articles, 2,835 unique ncRNAs, 1,118 unique phenotypes and 35,295 unique relations, with a precision of 0.761 and F1-score of 0.593, calculated through human validation. DSRE methods require a set of predocumented relations to function, as such, a high-fidelity ncRNA-phenotype relation dataset, consisting of 214,300 unique relations, was created by the aggregation of five ncRNA-disease functional annotation databases. Then, both ncoRP and the relation dataset represent important contributions towards solving the problem with the sparseness of information about ncRNAs. Large Language Models (LLMs) are an emerging type of language model, showing great capabilities in general task-solving through text generation, without the requirement of fine-tuning with large datasets. This benefit shows promise for applications in Relation Extraction (RE), when compared to data-intensive state-of-the-art deep learning methods. In this work, a LLM RE methodology is proposed and evaluated, achieving an F1-score of 0.978 by combining the RE task with a preceding sentence filtering task and applying prompting principles such as in-context learning and Chain-of-Thought self-explanation. | pt_PT |
| dc.identifier.tid | 203878302 | |
| dc.identifier.uri | http://hdl.handle.net/10400.5/96901 | |
| dc.language.iso | eng | pt_PT |
| dc.subject | RNAs não codificantes | pt_PT |
| dc.subject | Extração de Relações | pt_PT |
| dc.subject | Prospeção de Texto | pt_PT |
| dc.subject | Supervisão à Distância | pt_PT |
| dc.subject | Grandes Modelos de Linguagem | pt_PT |
| dc.subject | Teses de mestrado - 2024 | pt_PT |
| dc.title | Predicting non-coding RNA function using Artificial Intelligence | pt_PT |
| dc.type | master thesis | |
| dspace.entity.type | Publication | |
| rcaap.rights | openAccess | pt_PT |
| rcaap.type | masterThesis | pt_PT |
| thesis.degree.name | Tese de mestrado em Bioinformática e Biologia Computacional | pt_PT |