47 citations · 115 across the 7 of their papers we have counts for
7 papers
Temporal epistasis inference from more than 3,500,000 SARS-CoV-2 Genomic Sequences
Hong-Li Zeng, Yue Liu, Vito Dichio +1
We use Direct Coupling Analysis (DCA) to determine epistatic interactions between loci of variability of the SARS-CoV-2 virus, segmenting genomes by month of sampling. We use full-…
Inferring epistasis from genomic data with comparable mutation and outcrossing rate
Hong-Li Zeng, Eugenio Mauri, Vito Dichio +3
We consider a population evolving due to mutation, selection and recombination, where selection includes single-locus terms (additive fitness) and two-loci terms (pairwise epistati…
Network reconstruction from asynchronously updated evolutionary game
Hong-Li Zeng, Shu-Xuan Wang, Yan-Dong Guo +1
The interactions between players of prisoner's dilemma (PD) game are reconstructed with evolutionary game data. All participants play the game with their counterparts and gain corr…
Inferring genetic fitness from genomic data
Hong-Li Zeng, Erik Aurell
The genetic composition of a naturally developing population is considered as due to mutation, selection, genetic drift and recombination. Selection is modeled as single-locus term…
Financial interaction networks inferred from traded volumes
Hongli Zeng, Rémi Lemoy, Mikko Alava
In order to use the advanced inference techniques available for Ising models, we transform complex data (real vectors) into binary strings, by local averaging and thresholding. Thi…
Maximum likelihood reconstruction for Ising models with asynchronous updates
Hong-Li Zeng, Mikko Alava, Erik Aurell +2
We describe how the couplings in an asynchronous kinetic Ising model can be inferred. We consider two cases, one in which we know both the spin history and the update times and one…
Network inference using asynchronously updated kinetic Ising Model
Hong-Li Zeng, Erik Aurell, Mikko Alava +1
Network structures are reconstructed from dynamical data by respectively naive mean field (nMF) and Thouless-Anderson-Palmer (TAP) approximations. For TAP approximation, we use two…