ArticleiScience2019
An Algorithmic Information Calculus for Causal Discovery and Reprogramming Systems.
Article in iScience, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 33 papers.
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
33 citing papers in PubMed.
- Time Series Correlations and Kolmogorov Complexity: A Hausdorff Dimension Perspective.Entropy (Basel, Switzerland) · 2026Article
- SuperARC: a test for artificial superintelligence based on compressed modelling, recursive prediction and problem complexity.Nature communications · 2026Article
- Binarized neural networks converge toward algorithmic simplicity: empirical support for the learning-as-compression hypothesis.Frontiers in computational neuroscience · 2026Article
- Multi-omic enriched blood-derived digital signatures reveal mechanistic and confounding disease clusters for differential diagnosis.Frontiers in network physiology · 2026Article
- The future of fundamental science led by generative closed-loop artificial intelligence.Frontiers in artificial intelligence · 2026Review
- Cellular reprogramming of H3K27M pediatric high-grade glioma to neuron-like state.Acta neuropathologica communications · 2025Article
- Cell Fate Dynamics Reconstruction Identifies TPT1 and PTPRZ1 Feedback Loops as Master Regulators of Differentiation in Pediatric Glioblastoma-Immune Cell Networks.Interdisciplinary sciences, computational life sciences · 2025Article
- Approaching the general quantification of functional information.Scientific reports · 2025Article
- Robust Model-Free Identification of the Causal Networks Underlying Complex Nonlinear Systems.Entropy (Basel, Switzerland) · 2024Article
- Cascades Towards Noise-Induced Transitions on Networks Revealed Using Information Flows.Entropy (Basel, Switzerland) · 2024Article
- Key Node Identification Method Based on Multilayer Neighbor Node Gravity and Information Entropy.Entropy (Basel, Switzerland) · 2024Article
- On the salient limitations of the methods of assembly theory and their classification of molecular biosignatures.NPJ systems biology and applications · 2024Review
- Exploring Simplicity Bias in 1D Dynamical Systems.Entropy (Basel, Switzerland) · 2024Article
- Causality Analysis with Information Geometry: A Comparison.Entropy (Basel, Switzerland) · 2023Article
- Downward-Growing Neural Networks.Entropy (Basel, Switzerland) · 2023Article
- Auxiliary Graph for Attribute Graph Clustering.Entropy (Basel, Switzerland) · 2022Article
- Relative Entropy of Distance Distribution Based Similarity Measure of Nodes in Weighted Graph Data.Entropy (Basel, Switzerland) · 2022Article
- Dissecting cell fate dynamics in pediatric glioblastoma through the lens of complex systems and cellular cybernetics.Biological cybernetics · 2022Review
- Emergence and algorithmic information dynamics of systems and observers.Philosophical transactions. Series A, Mathematical, physical, and engineering sciences · 2022Article
- Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
We introduce and develop a method that demonstrates that the algorithmic information content of a system can be used as a steering handle in the dynamical phase space, thus affording an avenue for controlling and reprogramming systems. The method consists of applying a series of controlled interventions to a networked system while estimating how the algorithmic information content is affected. We demonstrate the method by reconstructing the phase space and their generative rules of some discrete dynamical systems (cellular automata) serving as controlled case studies. Next, the model-based interventional or causal calculus is evaluated and validated using (1) a huge large set of small graphs, (2) a number of larger networks with different topologies, and finally (3) biological networks derived from a widely studied and validated genetic network (E. coli) as well as on a significant number of differentiating (Th17) and differentiated human cells from a curated biological network data.
Indexed as
Identifiers
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.