Author

Alex Finnegan

University of Illinois Urbana-Champaign

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

Bioinformatics· 2 Genomics· 1

Signature Assays, Organisms & Methods

Click to filter · or open tag

Approach:statistical model· 3 Assay:DNA methylation· 2 Research Focus:gene regulatory network inference· 2 Organism:yeast· 2 Assay:ChIP-seq· 1 Approach:machine learning (classical)· 1 Assay:whole-genome sequencing· 1

Frequent Co-Authors

Jun S. SongiD· 3 shared Hu JiniD· 2 shared Michael Gapinske· 1 shared Pablo Pérez‐PiñeraiD· 1 shared Somang Kim· 1 shared Wendy S. WoodsiD· 1 shared

Activity by Year

2017-2020 · peak 1/yr

Co-Author Collaboration Network

6 top collaborators · 15 mutual co-author links

Click any collaborator node to inspect or filter shared papers

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

Curved chords = mutual co-authorship

3 shared papersBioinformatics

Jun S. SongORCID

Mutual co-authors in cluster

Author profile →

3 papers

Recent·Most cited

Epigenetic engineering of yeast reveals dynamic molecular adaptation to methylation stress and genetic modulators of specific DNMT3 family members

Alex Finnegan, Somang Kim, Hu Jin, Michael Gapinske, Wendy S. Woods, Pablo Pérez‐Piñera, Jun S. Song

This study uses yeast to investigate how DNA methylation is regulated by DNMT3 family members and identifies sequence preferences and spatial patterns of methylation.

Nucleic Acids Research · Mar 13, 2020Peer-reviewedGenomics

A unified computational framework for modeling genome-wide nucleosome landscape

Hu Jin, Alex Finnegan, Jun S. Song

This paper develops a computational framework to model genome-wide nucleosome landscape by quantifying sequence features' contributions to occupancy, translational, and rotational positioning in S. cerevisiae.

Physical Biology · Aug 16, 2018Peer-reviewedBioinformatics

Maximum Entropy Methods for Extracting the Learned Features of Deep Neural Networks

Alex Finnegan, Jun S. Song

This paper introduces a method to extract learned features from deep neural networks using maximum entropy principles, demonstrating its application in identifying transcription factor motifs and nucleosome positioning signals.

PLoS Computational Biology · Oct 30, 2017Peer-reviewedBioinformatics