Sofia Triantafillou
University of Pittsburgh
26 Papers
115 Citations
Sofia Triantafillou is an academic researcher from University of Pittsburgh. The author has contributed to research in topics: Causal model & Computer science. The author has an hindex of 9, co-authored 23 publications. Previous affiliations of Sofia Triantafillou include Karolinska Institutet & University of Crete.
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Papers
Relationship Between Sleep Quality and Mood: Ecological Momentary Assessment Study.
TL;DR: It is found that daily sleep quality and mood are related, with the effect of sleep quality on mood being significantly larger than the reverse.
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Constraint-based Causal Discovery from Multiple Interventions over Overlapping Variable Sets
TL;DR: Algorithm COmbINE is presented, which accepts a collection of data sets over overlapping variable sets under different experimental conditions and outputs a summary of all causal models indicating the invariant and variant structural characteristics of all models that simultaneously fit all of the input data sets.
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Predicting Causal Relationships from Biological Data: Applying Automated Causal Discovery on Mass Cytometry Data of Human Immune Cells.
Sofia Triantafillou,Sofia Triantafillou,Vincenzo Lagani,Christina Heinze-Deml,Angelika Schmidt,Jesper Tegnér,Jesper Tegnér,Ioannis Tsamardinos +7 more
TL;DR: This work applies state-of-the art causal discovery methods on a large collection of public mass cytometry data sets, measuring intra-cellular signaling proteins of the human immune system and their response to several perturbations.
On scoring Maximal Ancestral Graphs with the Max–Min Hill Climbing algorithm
Konstantinos Tsirlis,Konstantinos Tsirlis,Vincenzo Lagani,Sofia Triantafillou,Ioannis Tsamardinos +4 more
TL;DR: A hybrid method, MAG Max–Min Hill-Climbing (M3HC) that takes as input a data set of continuous variables, assumed to follow a multivariate Gaussian distribution, and outputs the best fitting maximal ancestral graph.
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Score-based vs Constraint-based Causal Learning in the Presence of Confounders.
Sofia Triantafillou,Ioannis Tsamardinos +1 more
- 01 Jan 2016
TL;DR: This work uses a greedy search strategy to identify the best fitting maximal ancestral graph (MAG) from continuous data, under the assumption of multivariate normality, and compares score-based and constraint-based learning in the presence of latent confounders.