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Nomothethic Network Psychiatry: A Bottom-Up Approach to Build Data-Driven Disease Models Based on Risk-Resilience, Adverse Outcome Pathways, and Phenome Scores, and to Discover New Biosignature-Based Classifications
Version 1
: Received: 11 October 2020 / Approved: 13 October 2020 / Online: 13 October 2020 (14:07:26 CEST)
How to cite:
Stoyanov, D.; Maes, M. Nomothethic Network Psychiatry: A Bottom-Up Approach to Build Data-Driven Disease Models Based on Risk-Resilience, Adverse Outcome Pathways, and Phenome Scores, and to Discover New Biosignature-Based Classifications. Preprints2020, 2020100283. https://doi.org/10.20944/preprints202010.0283.v1
Stoyanov, D.; Maes, M. Nomothethic Network Psychiatry: A Bottom-Up Approach to Build Data-Driven Disease Models Based on Risk-Resilience, Adverse Outcome Pathways, and Phenome Scores, and to Discover New Biosignature-Based Classifications. Preprints 2020, 2020100283. https://doi.org/10.20944/preprints202010.0283.v1
Stoyanov, D.; Maes, M. Nomothethic Network Psychiatry: A Bottom-Up Approach to Build Data-Driven Disease Models Based on Risk-Resilience, Adverse Outcome Pathways, and Phenome Scores, and to Discover New Biosignature-Based Classifications. Preprints2020, 2020100283. https://doi.org/10.20944/preprints202010.0283.v1
APA Style
Stoyanov, D., & Maes, M. (2020). Nomothethic Network Psychiatry: A Bottom-Up Approach to Build Data-Driven Disease Models Based on Risk-Resilience, Adverse Outcome Pathways, and Phenome Scores, and to Discover New Biosignature-Based Classifications. Preprints. https://doi.org/10.20944/preprints202010.0283.v1
Chicago/Turabian Style
Stoyanov, D. and Michael Maes. 2020 "Nomothethic Network Psychiatry: A Bottom-Up Approach to Build Data-Driven Disease Models Based on Risk-Resilience, Adverse Outcome Pathways, and Phenome Scores, and to Discover New Biosignature-Based Classifications" Preprints. https://doi.org/10.20944/preprints202010.0283.v1
Abstract
Psychiatry remains in a permanent state of crisis, which fragmented psychiatry from the field of medicine. The crisis in psychiatry is evidenced by the many different competing approaches to psychiatric illness including psychodynamic, biological, molecular, pan-omics, precision, cognitive and phenomenological psychiatry, folk psychology, mind-brain dualism, descriptive psychopathology, and postpsychiatry. The current “gold standard” DSM/ICD taxonomies of mood disorders and schizophrenia are unreliable and preclude to employ a deductive reasoning approach. Therefore, it is not surprising that mood disorders and schizophrenia research was unable to revise the conventional classifications and did not provide more adequate therapeutic approaches. The aim of this paper is to explain the new nomothetic network psychiatry (NNP) approach, which uses machine learning methods to build data-driven causal models of mental illness by ensembling risk-resilience, adverse outcome pathways (AOP), cognitome, brainome, symptomatome, and phenomenome latent scores in a causal model. The latter may be trained, tested and validated with Partial Least Squares (PLS) analysis. This approach not only allows to compute pathway-phenotypes or biosignatures, but also to construct reliable and replicable nomothetic networks, which are, therefore, generalizable as disease models. After integrating the validated feature vectors into a well-fitting nomothetic network, clustering analysis may be applied on the latent variable scores of the R/R, AOP, cognitome, brainome, and phenome latent vectors. This pattern recognition method may expose new (transdiagnostic) classes of patients which if cross-validated in independent samples may constitute new (transdiagnostic) nosological categories.
Keywords
psychiatry; major depression; mood disorders; schizophrenia; antioxidants; oxidative stress
Subject
Medicine and Pharmacology, Immunology and Allergy
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.