Author

E. Molloy

Listed by name: these papers carry no ORCID for this author, so different people with the same name may be mixed together.

Biology Disciplines

Click to filter author papers

Evolutionary Biology· 3

Signature Assays, Organisms & Methods

Click to filter · or open tag

Research Focus:phylogenetics / evolution· 3 Approach:statistical model· 3 Research Focus:benchmarking· 2 Approach:comparative / phylogenetic analysis· 1 Approach:deep learning· 1 Assay:DNA methylation· 1 Research Focus:gene regulatory network inference· 1 Organism:multiple species· 1

Frequent Co-Authors

J. Gatesy· 1 shared M. S. Springer· 1 shared T. Warnow· 1 shared Y. Han· 1 shared

Activity by Year

2017-2022 · peak 1/yr

Co-Author Collaboration Network

4 top collaborators · 1 mutual co-author link

Click any collaborator node to inspect or filter shared papers

Focal author (center)Selected co-authorORCID co-authorUnlinked name

Curved chords = mutual co-authorship

1 shared paperEvolutionary Biology

J. Gatesy

Mutual co-authors in cluster

Author profile →

3 papers

Recent·Most cited

TREE-QMC: Improving quartet graph construction for scalable and accurate species tree estimation from gene trees

Y. Han, E. Molloy

TREE-QMC improves species tree estimation by addressing scalability and accuracy issues in quartet-based methods using a novel graph construction approach.

bioRxiv · Jun 25, 2022Evolutionary Biology

Theoretical and practical considerations when using retroelement insertions to estimate species trees in the anomaly zone

E. Molloy, J. Gatesy, M. S. Springer

This paper evaluates methods for estimating species trees using retroelement insertions, finding that ASTRAL BP and SDPquartets are statistically consistent and accurate, especially in the anomaly zone.

Systematic Biology · Oct 22, 2021Peer-reviewedEvolutionary Biology

To include or not to include: The impact of gene filtering on species tree estimation methods

E. Molloy, T. Warnow

This study evaluates how gene filtering affects species tree estimation methods, finding that filtering based on missing data is generally harmful, while filtering based on gene tree error can sometimes improve accuracy under specific conditions.

bioRxiv · Jun 12, 2017Evolutionary Biology