Author

Ian Kerman

Dassault Systèmes (United States)

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· 1

Signature Assays, Organisms & Methods

Click to filter · or open tag

Approach:machine learning (classical)· 1 Assay:protein sequence· 1 Research Focus:sequence-to-function modeling· 1

Frequent Co-Authors

Chan Hong· 1 shared Naim MatasciiD· 1 shared Rachel S. Schibler· 1 shared Reza SadeghiiD· 1 shared Thomas DoughertyiD· 1 shared Xingyao CheniD· 1 shared Yi Cong Zhao· 1 shared Yi-Chieh WuiD· 1 shared

Activity by Year

2020-2020 · peak 1/yr

Co-Author Collaboration Network

8 top collaborators · 28 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

1 shared paperBioinformatics

Chan Hong

Mutual co-authors in cluster

Author profile →

1 paper

Recent·Most cited

Predicting Antibody Developability from Sequence using Machine Learning

Xingyao Chen, Thomas Dougherty, Chan Hong, Rachel S. Schibler, Yi Cong Zhao, Reza Sadeghi, Naim Matasci, Yi-Chieh Wu, Ian Kerman

This paper develops a machine learning pipeline to predict antibody developability from sequence, achieving high sensitivity and specificity on a dataset of 2400 antibodies.

bioRxiv · Jun 18, 2020 · PreprintBioinformatics