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Benjamin D. Lee

dblp:143/9774 · DBLP profile ↗
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3ranked-venue papers
3as first author
1since 2021 · last 2022
0000-0002-7133-8397ORCID · corroborated

Domains — the database's venue-derived domains; a paper can count in several

Applied, interdisciplinary, general and emerging computing · 3 · 3 first-author · 1 since 2021

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Interdisciplinary, comprehensive, and emerging computing
1 paper
Bioinformatics and computational biology · 100%

Topics — the 2 heaviest of 2, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Bioinformatics and computational biology › sequence analysis › sequence visualization
DNA sequence visualization
0.412019
Squiggle: a user-friendly two-dimensional DNA sequence visualization tool · Bioinform. 2019
Bioinformatics and computational biology
sequence analysis
0.412019
Squiggle: a user-friendly two-dimensional DNA sequence visualization tool · Bioinform. 2019

Methods — techniques the papers use, named apart from their topics

web-based visualization · 0.4
YearPublicationVenuePosition
2022 Ten quick tips for deep learning in biology
abstract
Machine learning is a modern approach to problem-solving and task automation.In particular, machine learning is concerned with the development and applications of algorithms that
Benjamin D. Lee, Anthony Gitter, Casey S. Greene, Sebastian Raschka, Finlay Maguire, Alexander J. Titus, Michael D. Kessler, Alexandra Lee, Marc G. Chevrette, Paul Allen Stewart, Thiago Britto-Borges, Evan M. Cofer, Kun-Hsing Yu, Juan Jose Carmona, Elana J. Fertig, Alexandr A. Kalinin, Brandon Signal, Benjamin J. Lengerich, Timothy J. Triche Jr., Simina M. Boca
PLoS Comput. Biol.1
2019 Squiggle: a user-friendly two-dimensional DNA sequence visualization tool
abstract
SUMMARY: Squiggle is a software tool that automatically generates interactive web-based two-dimensional graphical representations of raw DNA sequences. Built with ease of use in mind, Squiggle implements several prior sequence visualization algorithms and introduces novel visualization methods designed to maximize human usability. AVAILABILITY AND IMPLEMENTATION: Squiggle is written in Python 3 and freely available under the permissive MIT license. The open-source code can be downloaded from github.com/Lab41/squiggle as well as the Python Package Index. The installation instructions and user guide are available at squiggle.readthedocs.org.
Benjamin D. Lee
Bioinform.1
2018 Ten simple rules for documenting scientific software
Benjamin D. Lee
PLoS Comput. Biol.1