activity
20142025
most citedLow Algorithmic Complexity Entropy-deceiving Graphs

85 citations · 178 across the 22 of their papers we have counts for

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Showing q-bio.MNShow all

7 papers · 1 filter

q-bio.MN2025

Epigenetic feedback reshapes dynamical landscapes in gene regulatory networks

Sascha H. Hauck, Sandip Saha, Narsis A. Kiani +1

Understanding how gene regulatory networks (GRNs) give rise to stable and dynamic cellular states remains a central challenge in theoretical biology, particularly when slow epigene…

q-bio.MN2018

Algorithmic Complexity and Reprogrammability of Chemical Structure Networks

Hector Zenil, Narsis A. Kiani, Ming-Mei Shang +1

Here we address the challenge of profiling causal properties and tracking the transformation of chemical compounds from an algorithmic perspective. We explore the potential of appl…

q-bio.MN2018

Predictive Systems Toxicology

Narsis A. Kiani, Ming-Mei Shang, Hector Zenil +1

In this review we address to what extent computational techniques can augment our ability to predict toxicity. The first section provides a brief history of empirical observations…

q-bio.MN2017

HiDi: An efficient reverse engineering schema for large scale dynamic regulatory network reconstruction using adaptive differentiation

Yue Deng, Hector Zenil, Jesper Tégner +1

The use of differential equations (ODE) is one of the most promising approaches to network inference. The success of ODE-based approaches has, however, been limited, due to the dif…

q-bio.MN2015

Evaluating Network Inference Methods in Terms of Their Ability to Preserve the Topology and Complexity of Genetic Networks

Narsis A. Kiani, Hector Zenil, Jakub Olczak +1

Network inference is a rapidly advancing field, with new methods being proposed on a regular basis. Understanding the advantages and limitations of different network inference meth…

q-bio.MN2015

Quantifying Loss of Information in Network-based Dimensionality Reduction Techniques

Hector Zenil, Narsis A. Kiani, Jesper Tegnér

To cope with the complexity of large networks, a number of dimensionality reduction techniques for graphs have been developed. However, the extent to which information is lost or p…