Predicting drug toxicity at the intersection of informatics and biology: DTox builds a foundation

doi:10.1016/j.patter.2022.100586

. 2022 Sep 9;3(9):100586.

doi: 10.1016/j.patter.2022.100586.

Predicting drug toxicity at the intersection of informatics and biology: DTox builds a foundation

Matthew J Sniatynski^{1

2}, Bruce S Kristal^{1

2}

Affiliations

¹ Division of Sleep and Circadian Disorders, Department of Medicine, Brigham and Women's Hospital, 221 Longwood Ave, LM322B, Boston, MA 02115, USA.
² Division of Sleep Medicine, Harvard Medical School, Boston, MA 02115, USA.

PMID: 36124303
PMCID: PMC9481942
DOI: 10.1016/j.patter.2022.100586

Predicting drug toxicity at the intersection of informatics and biology: DTox builds a foundation

Matthew J Sniatynski et al. Patterns (N Y). 2022.

. 2022 Sep 9;3(9):100586.

doi: 10.1016/j.patter.2022.100586.

Authors

Matthew J Sniatynski^{1

2}, Bruce S Kristal^{1

2}

Affiliations

¹ Division of Sleep and Circadian Disorders, Department of Medicine, Brigham and Women's Hospital, 221 Longwood Ave, LM322B, Boston, MA 02115, USA.
² Division of Sleep Medicine, Harvard Medical School, Boston, MA 02115, USA.

PMID: 36124303
PMCID: PMC9481942
DOI: 10.1016/j.patter.2022.100586

Abstract

Hao et al. (2022) present DTox (deep learning for toxicology), a neural network designed to predict and probe the sites and potential mechanisms underlying chemical toxicity; results provide a map to facilitate modular testing and improvements across multiple disparate applications.

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Conflict of interest statement

M.J.S. and B.S.K. have a filed US patent application related to Sniatynski et al. (2022). The patent is not directly related to Hao et al., but the two may be useful in tandem.

References

1. Futreal P.A., Liu Q., Shattuck-Eidens D., Cochran C., Harshman K., Tavtigian S., Bennett L.M., Haugen-Strano A., Swensen J., Miki Y., et al. BRCA1 mutations in primary breast and ovarian carcinomas. Science. 1994;266:120–122. doi: 10.1126/science.7939630. - DOI - PubMed
1. Hao Y., Romano J.D., Moore J.H. Knowledge-guided deep learning models of drug toxicity improve interpretation. Patterns. 2022;3:100565. - PMC - PubMed
1. Ribeiro M.T., Singh S., Guestrin C. Association for Computing Machinery; 2016. Why should i trust you? Explaining the predictions of any classifier; pp. 1135–1144. (Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD '16)). - DOI
1. Lundberg S.M., Lee S.-I. In: Guyon I., Von Luxburg U., Bengio S., Wallach H., Fergus R., Vishwanathan S., Garnett R., editors. Vol. 30. 2017. A unified approach to interpreting model predictions; pp. 4765–4774. (Proceedings of Advances in Neural Information Processing Systems).
1. Mohanty S.D., Lekan D., McCoy T.P., Jenkins M., Manda P. Machine learning for predicting readmission risk among the frail: explainable AI for healthcare. Patterns. 2021;3:100395. doi: 10.1016/j.patter.2021.100395. - DOI - PMC - PubMed

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[1] Futreal P.A., Liu Q., Shattuck-Eidens D., Cochran C., Harshman K., Tavtigian S., Bennett L.M., Haugen-Strano A., Swensen J., Miki Y., et al. BRCA1 mutations in primary breast and ovarian carcinomas. Science. 1994;266:120–122. doi: 10.1126/science.7939630. - DOI - PubMed

[2] Futreal P.A., Liu Q., Shattuck-Eidens D., Cochran C., Harshman K., Tavtigian S., Bennett L.M., Haugen-Strano A., Swensen J., Miki Y., et al. BRCA1 mutations in primary breast and ovarian carcinomas. Science. 1994;266:120–122. doi: 10.1126/science.7939630. - DOI - PubMed

[3] Hao Y., Romano J.D., Moore J.H. Knowledge-guided deep learning models of drug toxicity improve interpretation. Patterns. 2022;3:100565. - PMC - PubMed

[4] Hao Y., Romano J.D., Moore J.H. Knowledge-guided deep learning models of drug toxicity improve interpretation. Patterns. 2022;3:100565. - PMC - PubMed

[5] Ribeiro M.T., Singh S., Guestrin C. Association for Computing Machinery; 2016. Why should i trust you? Explaining the predictions of any classifier; pp. 1135–1144. (Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD '16)). - DOI

[6] Ribeiro M.T., Singh S., Guestrin C. Association for Computing Machinery; 2016. Why should i trust you? Explaining the predictions of any classifier; pp. 1135–1144. (Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD '16)). - DOI

[7] Lundberg S.M., Lee S.-I. In: Guyon I., Von Luxburg U., Bengio S., Wallach H., Fergus R., Vishwanathan S., Garnett R., editors. Vol. 30. 2017. A unified approach to interpreting model predictions; pp. 4765–4774. (Proceedings of Advances in Neural Information Processing Systems).

[8] Lundberg S.M., Lee S.-I. In: Guyon I., Von Luxburg U., Bengio S., Wallach H., Fergus R., Vishwanathan S., Garnett R., editors. Vol. 30. 2017. A unified approach to interpreting model predictions; pp. 4765–4774. (Proceedings of Advances in Neural Information Processing Systems).

[9] Mohanty S.D., Lekan D., McCoy T.P., Jenkins M., Manda P. Machine learning for predicting readmission risk among the frail: explainable AI for healthcare. Patterns. 2021;3:100395. doi: 10.1016/j.patter.2021.100395. - DOI - PMC - PubMed

[10] Mohanty S.D., Lekan D., McCoy T.P., Jenkins M., Manda P. Machine learning for predicting readmission risk among the frail: explainable AI for healthcare. Patterns. 2021;3:100395. doi: 10.1016/j.patter.2021.100395. - DOI - PMC - PubMed

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Predicting drug toxicity at the intersection of informatics and biology: DTox builds a foundation

Affiliations

Predicting drug toxicity at the intersection of informatics and biology: DTox builds a foundation

Authors

Affiliations

Abstract

Conflict of interest statement

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References

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