ReviewEndocrine-related cancer2026
Hallmarks of disease: how tuned hierarchies of intelligent molecular neural networks (MNNs) may matter.
Review in Endocrine-related cancer, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
1 citing paper in PubMed.
- Modulation of Network Plasticity Opens Novel Therapeutic Possibilities in Cancer, Diabetes, and Neurodegeneration.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
1 author.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The intracellular molecular networks are highly complex and plastic. Yet, they obey the network principles discovered by systems biology. Precise mathematical models of some networks enable one to predict how molecular properties determine the function and malfunction of the network. However, the complexity is even greater than this: due to selection in evolutionary biology, the molecular networks are not only causal to cell function, but also caused by the requirement that the cell's functions be optimal. We discuss how the combination of complexity, plasticity, and this circular causality makes the networks similar to trained artificial neural networks. Causation by purpose might thereby dominate over causation by mechanism. Comparison of challenges that may cause or cure disease with actual challenges in evolutionary, developmental, or cell biology, and assessment of the learned responses thereto, might add another interesting layer of systems biology.
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What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.