Systems Biology and Predictive Neuroscience: A Double Helical Approach
This article explores the overlap between systems biology and predictive neuroscience, placing them in their larger context, the contemporary trend of bioinformatic convergence across the sciences. These two domains overlap with respect to their interest in data accumulation and data integration; th...
| Main Author: | |
|---|---|
| Format: | Electronic Article |
| Language: | English |
| Check availability: | HBZ Gateway |
| Journals Online & Print: | Drawer...
|
| Fernleihe: | Fernleihe für die Fachinformationsdienste |
| Published: |
Wiley-Blackwell
[2017]
|
| In: |
Zygon
Year: 2017, Volume: 52, Issue: 2, Pages: 516-537 |
| Standardized Subjects / Keyword chains: | B
Biology
/ Systematics
/ Neurosciences
|
| RelBib Classification: | AB Philosophy of religion; criticism of religion; atheism AE Psychology of religion ZA Social sciences ZD Psychology |
| Further subjects: | B
computational biology
B systems biology B Agency B omics B Holism B predictive neuroscience B Modeling B scientific convergence B Mechanism |
| Online Access: |
Volltext (Verlag) Volltext (doi) |
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| 520 | |a This article explores the overlap between systems biology and predictive neuroscience, placing them in their larger context, the contemporary trend of bioinformatic convergence across the sciences. These two domains overlap with respect to their interest in data accumulation and data integration; their reliance on computational statistical correlation; and their translational goals, that is, producing practical fruits and applications from the interscientific cross-pollination that contemporary data-integrative approaches make possible. The interventions that such translational conversations generate are medical and social in nature, and are aimed at both prevention (through prediction) and treatment. It will be argued that such approaches, socially and medically applied, contain potential for conveying both agency-enhancing and agency-diminishing social messages. The article concludes with a call to balance the overwhelmingly quantitative focus characteristic of predictive neuroscience with more qualitative empirical methodologies. This would represent a double helical approach. | ||
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