Advancing transdiagnostic data analytics using knowledge graphs
Artificial intelligence approaches have tremendous potential to advance our understanding of biological and other processes contributing to mental illness risk. An important question is how such approaches can be tailored to support transdiagnostic investigations that are considered central for gain...
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| Format: | Article |
| Language: | English |
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Elsevier
2025-06-01
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| Series: | Biomarkers in Neuropsychiatry |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2666144625000048 |
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| _version_ | 1825199455986515968 |
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| author | Fiona Klaassen Emanuel Schwarz |
| author_facet | Fiona Klaassen Emanuel Schwarz |
| author_sort | Fiona Klaassen |
| collection | DOAJ |
| description | Artificial intelligence approaches have tremendous potential to advance our understanding of biological and other processes contributing to mental illness risk. An important question is how such approaches can be tailored to support transdiagnostic investigations that are considered central for gaining deeper insight into etiological processes and psychopathology that may not align well with categorical illness delineations. Here, we present the so-called “knowledge graphs” that could be leveraged in analytic approaches to synthesize multimodal data of transdiagnostic relevance, identify important latent structures and biomarkers, and support the evaluation of existing transdiagnostic frameworks. |
| format | Article |
| id | doaj-art-d89976ce0e4c46eb9d0bb2004e3f1b41 |
| institution | Kabale University |
| issn | 2666-1446 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Biomarkers in Neuropsychiatry |
| spelling | doaj-art-d89976ce0e4c46eb9d0bb2004e3f1b412025-02-08T05:01:15ZengElsevierBiomarkers in Neuropsychiatry2666-14462025-06-0112100122Advancing transdiagnostic data analytics using knowledge graphsFiona Klaassen0Emanuel Schwarz1Hector Institute for Artificial Intelligence in Psychiatry, Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany; Department of Psychiatry and Psychotherapy, Central Institute of Mental Health, Medical Faculty Mannheim, University of Heidelberg, Mannheim, GermanyHector Institute for Artificial Intelligence in Psychiatry, Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany; Department of Psychiatry and Psychotherapy, Central Institute of Mental Health, Medical Faculty Mannheim, University of Heidelberg, Mannheim, Germany; German Center for Mental Health (DZPG), partner site Mannheim-Heidelberg-Ulm; Corresponding author at: Hector Institute for Artificial Intelligence in Psychiatry, Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany.Artificial intelligence approaches have tremendous potential to advance our understanding of biological and other processes contributing to mental illness risk. An important question is how such approaches can be tailored to support transdiagnostic investigations that are considered central for gaining deeper insight into etiological processes and psychopathology that may not align well with categorical illness delineations. Here, we present the so-called “knowledge graphs” that could be leveraged in analytic approaches to synthesize multimodal data of transdiagnostic relevance, identify important latent structures and biomarkers, and support the evaluation of existing transdiagnostic frameworks.http://www.sciencedirect.com/science/article/pii/S2666144625000048BiomarkerConstructKnowledge graphRDoCTransdiagnosticValidity |
| spellingShingle | Fiona Klaassen Emanuel Schwarz Advancing transdiagnostic data analytics using knowledge graphs Biomarkers in Neuropsychiatry Biomarker Construct Knowledge graph RDoC Transdiagnostic Validity |
| title | Advancing transdiagnostic data analytics using knowledge graphs |
| title_full | Advancing transdiagnostic data analytics using knowledge graphs |
| title_fullStr | Advancing transdiagnostic data analytics using knowledge graphs |
| title_full_unstemmed | Advancing transdiagnostic data analytics using knowledge graphs |
| title_short | Advancing transdiagnostic data analytics using knowledge graphs |
| title_sort | advancing transdiagnostic data analytics using knowledge graphs |
| topic | Biomarker Construct Knowledge graph RDoC Transdiagnostic Validity |
| url | http://www.sciencedirect.com/science/article/pii/S2666144625000048 |
| work_keys_str_mv | AT fionaklaassen advancingtransdiagnosticdataanalyticsusingknowledgegraphs AT emanuelschwarz advancingtransdiagnosticdataanalyticsusingknowledgegraphs |