Publication Details |
| Category | Text Publication |
| Reference Category | Journals |
| DOI | 10.1021/acs.est.3c00351 |
Licence  |
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| Title (Primary) | A temporal graph model to predict chemical transformations in complex dissolved organic matter |
| Author | Plamper, P.; Lechtenfeld, O.J.
; Herzsprung, P.; Groß, A. |
| Source Titel | Environmental Science & Technology |
| Year | 2023 |
| Department | SEEFO; ANA |
| Volume | 57 |
| Issue | 46 |
| Page From | 18116 |
| Page To | 18126 |
| Language | englisch |
| Topic | T5 Future Landscapes |
| Supplements | https://pubs.acs.org/doi/suppl/10.1021/acs.est.3c00351/suppl_file/es3c00351_si_001.pdf |
| Keywords | temporal graph; molecular network; compositional network; DOM; complex mixtures; photodegradation; photo-oxidation; link prediction; machine learning; community detection; unsupervised clustering |
| Abstract | Dissolved organic matter (DOM) is a complex mixture of thousands of natural molecules that undergo constant transformation in the environment, such as sunlight induced photochemical reactions. Despite molecular level resolution from ultrahigh resolution mass spectrometry (UHRMS), trends of mass peak intensities are currently the only way to follow photochemically induced molecular changes in DOM. Many real-world relationships and temporal processes can be intuitively modeled using graph data structures (networks). Graphs enhance the potential and value of AI applications by adding context and interconnections allowing the uncovering of hidden or unknown relationships in data sets. We use a temporal graph model and link prediction to identify transformations of DOM molecules in a photo-oxidation experiment. Our link prediction algorithm simultaneously considers educt removal and product formation for molecules linked by predefined transformation units (oxidation, decarboxylation, etc.). The transformations are further weighted by the extent of intensity change and clustered on the graph structure to identify groups of similar reactivity. The temporal graph is capable of identifying relevant molecules subject to similar reactions and enabling to study their time course. Our approach overcomes previous data evaluation limitations for mechanistic studies of DOM and leverages the potential of temporal graphs to study DOM reactivity by UHRMS. |
| Persistent UFZ Identifier | https://www.ufz.de/index.php?en=20939&ufzPublicationIdentifier=22541 |
| Plamper, P., Lechtenfeld, O.J., Herzsprung, P., Groß, A. (2023): A temporal graph model to predict chemical transformations in complex dissolved organic matter Environ. Sci. Technol. 57 (46), 18116 - 18126 10.1021/acs.est.3c00351 |
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