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Sentence Classification to Detect Tables for Helping Extraction of Regulatory Interactions in Bacteria

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Computational Intelligence Methods for Bioinformatics and Biostatistics (CIBB 2021)

Part of the book series: Lecture Notes in Computer Science ((LNBI,volume 13483))

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Abstract

The biomedical knowledge about transcriptional regulation in bacteria is rapidly published in scientific articles, so keeping biological databases up to date by manual curation is rather than impossible. Despite the efforts in biomedical text mining, there are still challenges in extracting regulatory interactions (RIs) between transcription factors and genes from text documents. One of them is produced by text extraction from PDF files. We have observed that the extraction of RIs from text lines that comes from tables of the original PDF article produces false positives. Here, we address the problem of automatically separating this text lines from those that are regular sentences by using automatic classification. Our best model was a Support Vector Classifier trained with n-grams of characters of tags of parts of speech, numbers, symbols, punctuation, brackets, and hyphens. Despite a significant imbalanced data, our classifier archived a positive class F1-score of 0.87. Our best classifier will be coupled eventually to a preprocessing pipeline for the automatic generation of transcriptional regulatory networks of bacteria by discarding text lines that comes from tables of the original PDF.

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Acknowledgments

This work was supported by UNAM-PAPIIT IA203420 and the Universidad Nacional Autónoma de México (UNAM). We acknowledge Víctor del Moral Chávez and Alfredo José Hernández Álvarez for computational support.

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Authors and Affiliations

  1. Centro de Ciencias Genómicas, Universidad Nacional Autónoma de México, Avenida Universidad s/n, 62210, Cuernavaca Mor., Mexico

    Dante Sepúlveda, Joel Rodríguez-Herrera, Alfredo Varela-Vega, Axel Zagal Norman & Carlos-Francisco Méndez-Cruz

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  1. Dante Sepúlveda
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  2. Joel Rodríguez-Herrera
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  3. Alfredo Varela-Vega
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  4. Axel Zagal Norman
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  5. Carlos-Francisco Méndez-Cruz
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Corresponding author

Correspondence to Carlos-Francisco Méndez-Cruz .

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Editors and Affiliations

  1. University of Toronto, Toronto, ON, Canada

    Davide Chicco

  2. Consiglio Nazionale delle Ricerche, Avellino, Italy

    Angelo Facchiano

  3. Università di Padova, Padua, Italy

    Erica Tavazzi

  4. Università di Padova, Padua, Italy

    Enrico Longato

  5. Università di Padova, Padua, Italy

    Martina Vettoretti

  6. Politecnico di Milano, Milan, Italy

    Anna Bernasconi

  7. Università di Verona, Verona, Italy

    Simone Avesani

  8. Università di Bergamo, Bergamo, Italy

    Paolo Cazzaniga

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Sepúlveda, D., Rodríguez-Herrera, J., Varela-Vega, A., Zagal Norman, A., Méndez-Cruz, CF. (2022). Sentence Classification to Detect Tables for Helping Extraction of Regulatory Interactions in Bacteria. In: Chicco, D., et al. Computational Intelligence Methods for Bioinformatics and Biostatistics. CIBB 2021. Lecture Notes in Computer Science(), vol 13483. Springer, Cham. https://doi.org/10.1007/978-3-031-20837-9_12

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  • DOI: https://doi.org/10.1007/978-3-031-20837-9_12

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  • Online ISBN: 978-3-031-20837-9

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