ArticleJournal of the Royal Society, Interface2022
Predicting phenotype transition probabilities via conditional algorithmic probability approximations.
Article in Journal of the Royal Society, Interface, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
What it found
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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
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Who cites it
5 citing papers in PubMed.
- Bounding phenotype transition probabilities via conditional complexity.Journal of the Royal Society, Interface · 2025Article
- Exploring Simplicity Bias in 1D Dynamical Systems.Entropy (Basel, Switzerland) · 2024Article
- Bias in the arrival of variation can dominate over natural selection in Richard Dawkins's biomorphs.PLoS computational biology · 2024Article
- The Boltzmann distributions of molecular structures predict likely changes through random mutations.Biophysical journal · 2023Article
- Predicting phenotype transition probabilities via conditional algorithmic probability approximations.Journal of the Royal Society, Interface · 2022Article
Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Unravelling the structure of genotype-phenotype (GP) maps is an important problem in biology. Recently, arguments inspired by algorithmic information theory (AIT) and Kolmogorov complexity have been invoked to uncover
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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.