Causal reductionism is the widespread assumption that there is no room for additional causes once we have accounted for all elementary mechanisms within a system. Due to its intuitive appeal, causal reductionism is prevalent in neuroscience: once all neurons have been caused to fire or not to fire, it seems that causally there is nothing left to be accounted for. Here, we argue that these reductionist intuitions are based on an implicit, unexamined notion of causation that conflates causation with prediction. By means of a simple model organism, we demonstrate that causal reductionism cannot provide a complete and coherent account of 'what caused what'. To that end, we outline an explicit, operational approach to analyzing causal structures.
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http://dx.doi.org/10.1038/s41593-021-00911-8 | DOI Listing |
Brief Bioinform
September 2024
Key Laboratory of Symbol Computation and Knowledge Engineering of Ministry of Education, College of Computer Science and Technology, Jilin University, No. 2699, Qianjin Street, Changchun 130012, China.
Motivation: Research shows that competing endogenous RNA is widely involved in gene regulation in cells, and identifying the association between circular RNA (circRNA), microRNA (miRNA), and cancer can provide new hope for disease diagnosis, treatment, and prognosis. However, affected by reductionism, previous studies regarded the prediction of circRNA-miRNA interaction, circRNA-cancer association, and miRNA-cancer association as separate studies. Currently, few models are capable of simultaneously predicting these three associations.
View Article and Find Full Text PDFBrief Bioinform
September 2024
Key Laboratory of Symbol Computation and Knowledge Engineering of Ministry of Education, College of Computer Science and Technology, Jilin University, No. 2699, Qianjin Street, Changchun, 130012, China.
The discovery of diagnostic and therapeutic biomarkers for complex diseases, especially cancer, has always been a central and long-term challenge in molecular association prediction research, offering promising avenues for advancing the understanding of complex diseases. To this end, researchers have developed various network-based prediction techniques targeting specific molecular associations. However, limitations imposed by reductionism and network representation learning have led existing studies to narrowly focus on high prediction efficiency within single association type, thereby glossing over the discovery of unknown types of associations.
View Article and Find Full Text PDFAntioxidants (Basel)
October 2024
Division of Bioorganic Chemistry, School of Pharmacy, Saarland University, D-66123 Saarbruecken, Germany.
Recent years have witnessed a rather controversial debate on what antioxidants are and how beneficial they may be in the context of human health. Despite a considerable increase in scientific evidence, the matter remains highly divisive as different pieces of new data seem to support both the pro- and the anti-antioxidant perspective. Here, we argue that the matter at the heart of this debate is not necessarily empirical but of semantics.
View Article and Find Full Text PDFBackground: Psychiatry may currently hold unprecedented knowledge in the diagnosis and treatment of psychiatric conditions. Yet, there is a widely held belief that this knowledge is not adequately integrated, nor does it fully account for the complexity of the phenomena under study.
Objective: To assess the effectiveness of a system-oriented and network-focused approach in capturing and integrating the complexity of psychiatric disorders.
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