Demo Data from VLDB 2026 / reviewers in the wild / expert
Gianandrea Padovani
dblp:97/2462
· DBLP profile ↗
1ranked-venue papers
0as first author
0since 2021 · last 2008
—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 1 heaviest of 1, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Bioinformatics and computational biology › structural bioinformatics
protein crystallization prediction |
0.1 | 1 | 2008 | ParCrys: a Parzen window density estimation approach to protein crystallization propensity prediction · Bioinform. 2008 |
Methods — techniques the papers use, named apart from their topics
parzen window density estimation · 0.1
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2008 | ParCrys: a Parzen window density estimation approach to protein crystallization propensity predictionabstractThe ability to rank proteins by their likely success in crystallization is useful in current Structural Biology efforts and in particular in high-throughput Structural Genomics initiatives. We present ParCrys, a Parzen Window approach to estimate a protein's propensity to produce diffraction-quality crystals. The Protein Data Bank (PDB) provided training data whilst the databases TargetDB and PepcDB were used to define feature selection data as well as test data independent of feature selection and training. ParCrys outperforms the OB-Score, SECRET and CRYSTALP on the data examined, with accuracy and Matthews correlation coefficient values of 79.1% and 0.582, respectively (74.0% and 0.227, respectively, on data with a 'real-world' ratio of positive:negative examples). ParCrys predictions and associated data are available from www.compbio.dundee.ac.uk/parcrys. Ian M. Overton, Gianandrea Padovani, Mark A. Girolami, Geoffrey J. Barton |
Bioinform. | 2 |