Discovering Content through Text Mining for a Synthetic Biology Knowledge System

McInnes, Bridget T.; Downie, J. Stephen; Hao, Yikai; Jett, Jacob; Keating, Kevin W; Nakum, Gaurav; Ranjan, Sudhanshu; Rodriguez, Nicholas E.; Tang, Jiawei; Du, Xiuli; Young, Eric M.; Nguyen, Manh Duc

doi:10.1021/acssynbio.1c00611

Cited by 2 publications

(1 citation statement)

References 44 publications

(61 reference statements)

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“…While BERT excels in extracting specific words from a document, it is less suitable for quantifying the relationships between words in a document or their properties. Numerous other attempts to extract information from documents using natural language processing have been made. − …”

Section: Introductionmentioning

confidence: 99%

Compound Classification and Consideration of Correlation with Chemical Descriptors from Articles on Antioxidant Capacity Using Natural Language Processing

Matsumoto,

Gotoh

2023

J. Chem. Inf. Model.

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In recent times, there has been a substantial increase in the number of articles focusing on antioxidants. However, the development of a comprehensive estimator for antioxidant capacity remains elusive due to the challenge of integrating information from these articles. Furthermore, the complexity of the antioxidant mechanism, which involves a multitude of factors, makes it difficult to establish a simple equation or correlation. Hence, there is a pressing need for a model that can effectively interpret the collective knowledge from these articles, especially from a chemistry perspective. In this research, we employed natural language processing techniques, specifically Word2Vec, to analyze articles related to antioxidant capacity. We extracted representation vectors of compound names from these documents and organized them into 10 distinct clusters. In our investigation of two of these clusters, we unveiled that the majority of the compounds in question were flavonoids and flavonoid glycosides. To establish a link between the descriptors and clusters, we utilized kernel density estimation and generated scatter plots to visualize their similarity. These visualizations clearly indicated a strong relationship between the descriptors and clusters, affirming that a tangible connection exists between word vectors and compound descriptors through a document analysis conducted with natural language processing techniques. This study represents a pioneering approach that utilizes document analysis to shed light on the field of antioxidant capacity research, marking a significant advancement in this domain.

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Section: Introductionmentioning

confidence: 99%

Compound Classification and Consideration of Correlation with Chemical Descriptors from Articles on Antioxidant Capacity Using Natural Language Processing

Matsumoto,

Gotoh

2023

J. Chem. Inf. Model.

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SeqImprove: Machine-Learning-Assisted Curation of Genetic Circuit Sequence Information

Mante,

Sents,

Britt

et al. 2024

ACS Synth. Biol.

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The progress and utility of synthetic biology is currently hindered by the lengthy process of studying literature and replicating poorly documented work. Reconstruction of crucial design information through post hoc curation is highly noisy and error-prone. To combat this, author participation during the curation process is crucial. To encourage author participation without overburdening them, an ML-assisted curation tool called SeqImprove has been developed. Using named entity recognition, called entity normalization, and sequence matching, SeqImprove creates machine-accessible sequence data and metadata annotations, which authors can then review and edit before submitting a final sequence file. SeqImprove makes it easier for authors to submit sequence data that is FAIR (findable, accessible, interoperable, and reusable).

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Discovering Content through Text Mining for a Synthetic Biology Knowledge System

Cited by 2 publications

References 44 publications

Compound Classification and Consideration of Correlation with Chemical Descriptors from Articles on Antioxidant Capacity Using Natural Language Processing

Compound Classification and Consideration of Correlation with Chemical Descriptors from Articles on Antioxidant Capacity Using Natural Language Processing

SeqImprove: Machine-Learning-Assisted Curation of Genetic Circuit Sequence Information

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