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Identifying and Analyzing Topic Clusters in a Nutri-, Food-, and Diet-Proteomic Corpus Using Machine Reading

Nutrition affects the early stages of disease development, but the mechanisms remain poorly understood. High-throughput proteomic methods are being used to generate data and information on the effects of nutrients, foods, and diets on health and disease processes. In this report, a novel machine rea...

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Detalles Bibliográficos
Autores principales: Monteiro, Jacqueline Pontes, Morine, Melissa J., Ued, Fabio V., Kaput, Jim
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9863309/
https://www.ncbi.nlm.nih.gov/pubmed/36678141
http://dx.doi.org/10.3390/nu15020270
Descripción
Sumario:Nutrition affects the early stages of disease development, but the mechanisms remain poorly understood. High-throughput proteomic methods are being used to generate data and information on the effects of nutrients, foods, and diets on health and disease processes. In this report, a novel machine reading pipeline was used to identify all articles and abstracts on proteomics, diet, food, and nutrition in humans. The resulting proteomic corpus was further analyzed to produce seven clusters of “thematic” content defined as documents that have similar word content. Examples of publications from several of these clusters were then described in a similar way to a typical descriptive review.