Details zur Publikation

Kategorie Textpublikation
Referenztyp Zeitschriften
DOI 10.1021/acs.est.3c00351
Lizenz creative commons licence
Titel (primär) A temporal graph model to predict chemical transformations in complex dissolved organic matter
Autor Plamper, P.; Lechtenfeld, O.J.; Herzsprung, P.; Groß, A.
Journal / Serie Environmental Science & Technology
Erscheinungsjahr 2023
Department SEEFO; ANA
Sprache englisch
Topic T5 Future Landscapes
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.
dauerhafte UFZ-Verlinkung
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.