Details zur Publikation |
| Kategorie | Textpublikation |
| Referenztyp | Preprints |
| DOI | 10.21203/rs.3.rs-5010617/v1 |
Lizenz  |
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| Titel (primär) | MLinvitroTox reloaded for high-throughput hazard-based prioritization of high-resolution mass spectrometry data |
| Autor | Arturi, K.; Harris, E.J.; Gasser, L.; Escher, B.I.
; Bosshard, R.; Hollender, J. |
| Quelle | Research Square |
| Erscheinungsjahr | 2024 |
| Department | ZELLTOX |
| Sprache | englisch |
| Topic | T9 Healthy Planet |
| Abstract | MLinvitroTox is an automated Python pipeline developed for high-throughput hazard-driven prioritization of toxicologically relevant features detected in complex environmental samples through nontarget high-resolution mass spectrometry (NTS HRMS/MS). MLinvitroTox is a machine learning (ML) framework comprising 490 independent XGBoost classifiers trained on molecular fingerprints from chemical structures and target-specific endpoints from the ToxCast/Tox21 invitroDBv4.1 database. For each analyzed feature, MLinvitroTox generates a 490-bit bioactivity fingerprint used as a basis for prioritization, focusing the time-consuming molecular identification efforts on features most likely to cause adverse effects. The practical advantages of MLinvitroTox are demonstrated for groundwater HRMS data. Among the 874 features for which molecular fingerprints were derived from spectra, including 630 nontargets, 185 spectral matches, and 59 targets, around 4\% of the feature/endpoint relationship pairs were predicted to be active. Cross-checking the predictions for targets and spectral matches with invitroDB data confirmed the bioactivity of 120 active and 6,791 nonactive pairs while mislabelling of 88 active and 56 non-active relationships. By applying the provided scores and metrics, the number of potentially toxic features was further reduced by at least one order of magnitude. This refinement makes the analytical confirmation of the toxicologically most relevant features feasible, offering significant benefits for cost-efficient chemical risk assessment. |
| dauerhafte UFZ-Verlinkung | https://www.ufz.de/index.php?en=20939&ufzPublicationIdentifier=29933 |
| Arturi, K., Harris, E.J., Gasser, L., Escher, B.I., Bosshard, R., Hollender, J. (2024): MLinvitroTox reloaded for high-throughput hazard-based prioritization of high-resolution mass spectrometry data Research Square 10.21203/rs.3.rs-5010617/v1 |
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