Demonstration venue · read-only. Every page can be browsed; the buttons that would change it are switched off. Create an account to run TaxoReview on your own data. A simulation from public publication records: the people listed were not invited and did not agree to review.
? Tutorial for this page

Adam J. Riesselman

dblp:241/1967 · DBLP profile ↗
← Back
2ranked-venue papers
0as first author
0since 2021 · last 2019
—ORCID · unresolved

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

Artificial intelligence and machine learning · 1Applied, interdisciplinary, general and emerging computing · 1

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
2 papers
Bioinformatics and computational biology · 75% Computational science and engineering · 25%
Artificial intelligence
1 paper
Deep learning architectures and training · 100%

Topics — the 3 heaviest of 4, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Bioinformatics and computational biology
protein structure prediction
0.822019
The EVcouplings Python framework for coevolutionary sequence analysis · Bioinform. 2019
Learning Protein Structure with a Differentiable Simulator · ICLR 2019
Bioinformatics and computational biology › molecular evolution
co-evolutionary analysis
0.412019
The EVcouplings Python framework for coevolutionary sequence analysis · Bioinform. 2019
Computational science and engineering › scientific machine learning
differentiable simulation
0.412019
Learning Protein Structure with a Differentiable Simulator · ICLR 2019

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

sequence alignment · 0.4direct coupling analysis · 0.4
YearPublicationVenuePosition
2019 Learning Protein Structure with a Differentiable Simulator
John Ingraham, Adam J. Riesselman, Chris Sander, Debora S. Marks
ICLR2
2019 The EVcouplings Python framework for coevolutionary sequence analysis
abstract
SUMMARY: Coevolutionary sequence analysis has become a commonly used technique for de novo prediction of the structure and function of proteins, RNA, and protein complexes. We present the EVcouplings framework, a fully integrated open-source application and Python package for coevolutionary analysis. The framework enables generation of sequence alignments, calculation and evaluation of evolutionary couplings (ECs), and de novo prediction of structure and mutation effects. The combination of an easy to use, flexible command line interface and an underlying modular Python package makes the full power of coevolutionary analyses available to entry-level and advanced users. AVAILABILITY AND IMPLEMENTATION: https://github.com/debbiemarkslab/evcouplings.
Thomas A. Hopf, Anna G. Green, Benjamin Schubert, Sophia Mersmann, Charlotta Schärfe, John Ingraham, Ágnes Tóth-Petróczy, Kelly Brock, Adam J. Riesselman, Perry Palmedo, Chan Kang, Robert P. Sheridan, Eli J. Draizen, Christian Dallago, Chris Sander, Debora S. Marks
Bioinform.9