Demo Data from SIGIR 2026 / reviewers in the wild / expert
Aditya Unnithan
dblp:67/3724
· DBLP profile ↗
1ranked-venue papers
0as first author
0since 2021 · last 2006
—ORCID · none
Domains — the database's venue-derived domains; a paper can count in several
Applied, 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
1 paper |
Bioinformatics and computational biology · 100% |
Topics — the 4 heaviest of 4, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Bioinformatics and computational biology › protein structure prediction
protein folding |
0.1 | 1 | 2006 | iFold: a platform for interactive folding simulations of proteins · Bioinform. 2006 |
Bioinformatics and computational biology › protein structure prediction › protein folding
protein folding simulation |
0.1 | 1 | 2006 | iFold: a platform for interactive folding simulations of proteins · Bioinform. 2006 |
Bioinformatics and computational biology
protein structure prediction |
0.1 | 1 | 2006 | iFold: a platform for interactive folding simulations of proteins · Bioinform. 2006 |
Bioinformatics and computational biology
thermodynamic analysis |
0.1 | 1 | 2006 | iFold: a platform for interactive folding simulations of proteins · Bioinform. 2006 |
Methods — techniques the papers use, named apart from their topics
gō-interactions · 0.1discrete molecular dynamics · 0.1coarse-grained model · 0.1
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2006 | iFold: a platform for interactive folding simulations of proteinsabstractUNLABELLED: We built a novel web-based platform for performing discrete molecular dynamics simulations of proteins. In silico protein folding involves searching for minimal frustration in the vast conformational landscape. Conventional approaches for simulating protein folding insufficiently address the problem of simulations in relevant time and length scales necessary for a mechanistic understanding of underlying biomolecular phenomena. Discrete molecular dynamics (DMD) offers an opportunity to bridge the size and timescale gaps and uncover the structural and biological properties of experimentally undetectable protein dynamics. The iFold server supports large-scale simulations of protein folding, thermal denaturation, thermodynamic scan, simulated annealing and p(fold) analysis using DMD and coarse-grained protein model with structure-based Gō-interactions between amino acids. AVAILABILITY: http://ifold.dokhlab.org Shantanu Sharma 0002, Feng Ding 0002, Huifen Nie, Daniel Watson, Aditya Unnithan, Jameson Lopp, Diane Pozefsky, Nikolay V. Dokholyan |
Bioinform. | 5 |