Gene-Disease-Food Relation Extraction from Biomedical Database

Karâa, Wahiba Ben Abdessalem; Mannai, Monia; Dey, Nilanjan; Ashour, Amira S.; Olariu, Iustin

doi:10.1007/978-3-319-62521-8_34

Cited by 6 publications

(2 citation statements)

References 34 publications

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“…These should be specialized for the task at hand. For instance, Ben Abdessalem Karaa et al created separate regular expressions to extract causal, preventative, and associative relationships between types of food, genes, and diseases [262], Nikfarjam et al used a list of key phrases to describe patient responses to drugs in social health networks [263], and Fan and Zhang created several regular expressions to extract patient dietary supplement use from clinical notes [264].…”

Section: Computationally Predicted Resourcesmentioning

confidence: 99%

Metabolomics and Multi-Omics Integration: A Survey of Computational Methods and Resources

et al. 2020

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As researchers are increasingly able to collect data on a large scale from multiple clinical and omics modalities, multi-omics integration is becoming a critical component of metabolomics research. This introduces a need for increased understanding by the metabolomics researcher of computational and statistical analysis methods relevant to multi-omics studies. In this review, we discuss common types of analyses performed in multi-omics studies and the computational and statistical methods that can be used for each type of analysis. We pinpoint the caveats and considerations for analysis methods, including required parameters, sample size and data distribution requirements, sources of a priori knowledge, and techniques for the evaluation of model accuracy. Finally, for the types of analyses discussed, we provide examples of the applications of corresponding methods to clinical and basic research. We intend that our review may be used as a guide for metabolomics researchers to choose effective techniques for multi-omics analyses relevant to their field of study.

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Section: Computationally Predicted Resourcesmentioning

confidence: 99%

Metabolomics and Multi-Omics Integration: A Survey of Computational Methods and Resources

et al. 2020

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“…Several researchers have studied automating pre-processing and post-processing for accurately extracting relations from unstructured data. Most pre-processing techniques involve the extraction of all available abstracts within a specific timeline from online resources such as PubMed [9][10][11]. However, these steps, while effective in domains with simple concepts and interactions, prove inefficient in the complex biological domain, with entity relations specific to organisms, cell function, or disease conditions.…”

Section: Introductionmentioning

confidence: 99%

Large language model based framework for automated extraction of genetic interactions from unstructured data

Gill,

Chetty,

Lim

et al. 2024

PLoS ONE

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Extracting biological interactions from published literature helps us understand complex biological systems, accelerate research, and support decision-making in drug or treatment development. Despite efforts to automate the extraction of biological relations using text mining tools and machine learning pipelines, manual curation continues to serve as the gold standard. However, the rapidly increasing volume of literature pertaining to biological relations poses challenges in its manual curation and refinement. These challenges are further compounded because only a small fraction of the published literature is relevant to biological relation extraction, and the embedded sentences of relevant sections have complex structures, which can lead to incorrect inference of relationships. To overcome these challenges, we propose GIX, an automated and robust Gene Interaction Extraction framework, based on pre-trained Large Language models fine-tuned through extensive evaluations on various gene/protein interaction corpora including LLL and RegulonDB. GIX identifies relevant publications with minimal keywords, optimises sentence selection to reduce computational overhead, simplifies sentence structure while preserving meaning, and provides a confidence factor indicating the reliability of extracted relations. GIX’s Stage-2 relation extraction method performed well on benchmark protein/gene interaction datasets, assessed using 10-fold cross-validation, surpassing state-of-the-art approaches. We demonstrated that the proposed method, although fully automated, performs as well as manual relation extraction, with enhanced robustness. We also observed GIX’s capability to augment existing datasets with new sentences, incorporating newly discovered biological terms and processes. Further, we demonstrated GIX’s real-world applicability in inferring E. coli gene circuits.

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A deep learning relation extraction approach to support a biomedical semi-automatic curation task: The case of the gluten bibliome

Pérez-Pérez

Ferreira

Igrejas

et al. 2022

Expert Systems with Applications

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Gene-Disease-Food Relation Extraction from Biomedical Database

Cited by 6 publications

References 34 publications

Metabolomics and Multi-Omics Integration: A Survey of Computational Methods and Resources

Metabolomics and Multi-Omics Integration: A Survey of Computational Methods and Resources

Large language model based framework for automated extraction of genetic interactions from unstructured data

A deep learning relation extraction approach to support a biomedical semi-automatic curation task: The case of the gluten bibliome

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