Demo Data from EDBT 2026 / reviewers in the wild / expert
Jacques S. Beckmann
dblp:78/1348
· DBLP profile ↗
5ranked-venue papers
0as first author
0since 2021 · last 2016
0000-0002-9741-1900ORCID · verified
Domains — the database's venue-derived domains; a paper can count in several
Applied, interdisciplinary, general and emerging computing · 4Graphics, computer vision, multimedia, augmented reality and games · 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
3 papers |
Bioinformatics and computational biology · 100% |
Topics — the 9 heaviest of 9, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Bioinformatics and computational biology › cancer genomics › copy number analysis
copy number variation detection |
0.2 | 1 | 2016 | New quality measure for SNP array based CNV detection · Bioinform. 2016 |
Bioinformatics and computational biology
genomics |
0.2 | 1 | 2016 | New quality measure for SNP array based CNV detection · Bioinform. 2016 |
Bioinformatics and computational biology › genomics
genome-wide association study |
0.1 | 1 | 2016 | New quality measure for SNP array based CNV detection · Bioinform. 2016 |
Bioinformatics and computational biology › protein structure analysis › intrinsically disordered protein
intrinsically disordered protein prediction |
0.1 | 1 | 2005 | FoldIndex copyright: a simple tool to predict whether a given protein sequence is intrinsically unfolded · Bioinform. 2005 |
Bioinformatics and computational biology
protein structure prediction |
0.1 | 1 | 2005 | FoldIndex copyright: a simple tool to predict whether a given protein sequence is intrinsically unfolded · Bioinform. 2005 |
Bioinformatics and computational biology › population genetics › population parameter estimation
haplotype frequency estimation |
0.0 | 1 | 2003 | Resolution of haplotypes and haplotype frequencies from SNP genotypes of pooled samples · RECOMB 2003 |
Bioinformatics and computational biology › genomics
haplotype inference |
0.0 | 1 | 2003 | Resolution of haplotypes and haplotype frequencies from SNP genotypes of pooled samples · RECOMB 2003 |
Bioinformatics and computational biology
population genetics |
0.0 | 1 | 2003 | Resolution of haplotypes and haplotype frequencies from SNP genotypes of pooled samples · RECOMB 2003 |
Bioinformatics and computational biology › genomics
genotyping |
0.0 | 1 | 2003 | Resolution of haplotypes and haplotype frequencies from SNP genotypes of pooled samples · RECOMB 2003 |
Methods — techniques the papers use, named apart from their topics
simulation · 0.2ROC analysis · 0.2
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2016 | New quality measure for SNP array based CNV detectionabstractMOTIVATION: Only a few large systematic studies have evaluated the impact of copy number variants (CNVs) on common diseases. Several million individuals have been genotyped on single nucleotide variation arrays, which could be used for genome-wide CNVs association studies. However, CNV calls remain prone to false positives and only empirical filtering strategies exist in the literature. To overcome this issue, we defined a new quality score (QS) estimating the probability of a CNV called by PennCNV to be confirmed by other software. RESULTS: Out-of-sample comparison showed that the correlation between the consensus CNV status and the QS is twice as high as it is for any previously proposed CNV filters. ROC curves displayed an AUC higher than 0.8 and simulations showed an increase up to 20% in statistical power when using QS in comparison to other filtering strategies. Superior performance was confirmed also for alternative consensus CNV definition and through improving known CNV-trait associations. AVAILABILITY AND IMPLEMENTATION: http://goo.gl/T6yuFM CONTACT: [email protected] or aurelien@[email protected] information: Supplementary data are available at Bioinformatics online. A. Macé, M. A. Tuke, Jacques S. Beckmann, L. Lin, S. Jacquemont, Michael N. Weedon, Alexandre Reymond, Zoltán Kutalik |
Bioinform. | 3 |
| 2008 | Computational Problems in Perfect Phylogeny Haplotyping: Typing without Calling the AlleleabstractA haplotype is an m-long binary vector. The XOR-genotype of two haplotypes is the m-vector of their coordinate-wise XOR. We study the following problem: Given a set of XOR-genotypes, reconstruct their haplotypes so that the set of resulting haplotypes can be mapped onto a perfect phylogeny (PP) tree. The question is motivated by studying population evolution in human genetics, and is a variant of the perfect phylogeny haplotyping problem that has received intensive attention recently. Unlike the latter problem, in which the input is "full" genotypes, here we assume less informative input, and so may be more economical to obtain experimentally. Building on ideas of Gusfield, we show how to solve the problem in polynomial time, by a reduction to the graph realization problem. The actual haplotypes are not uniquely determined by that tree they map onto, and the tree itself may or may not be unique. We show that tree uniqueness implies uniquely determined haplotypes, up to inherent degrees of freedom, and give a sufficient condition for the uniqueness. To actually determine the haplotypes given the tree, additional information is necessary. We show that two or three full genotypes suffice to reconstruct all the haplotypes, and present a linear algorithm for identifying those genotypes. Tamar Barzuza, Jacques S. Beckmann, Ron Shamir, Itsik Pe'er |
IEEE ACM Trans. Comput. Biol. Bioinform. | 2 |
| 2005 | FoldIndex copyright: a simple tool to predict whether a given protein sequence is intrinsically unfoldedabstractSummary: An easy-to-use, versatile and freely available graphic web server, FoldIndex© is described: it predicts if a given protein sequence is intrinsically unfolded implementing the algorithm of Uversky and co-workers, which is based on the average residue hydrophobicity and net charge of the sequence. FoldIndex© has an error rate comparable to that of more sophisticated fold prediction methods. Sliding windows permit identification of large regions within a protein that possess folding propensities different from those of the whole protein. Availability: FoldIndex© can be accessed at http://bioportal.weizmann.ac.il/fldbin/findex Contact: [email protected] Supplementary information: http://www.weizmann.ac.il/sb/faculty_pages/Sussman/papers/suppl/Prilusky_2005 Jaime Prilusky, Clifford E. Felder, Tzviya Zeev-Ben-Mordehai, Edwin H. Rydberg, Orna Man, Jacques S. Beckmann, Israel Silman, Joel L. Sussman |
Bioinform. | 6 |
| 2004 | Computational Problems in Perfect Phylogeny Haplotyping: Xor-Genotypes and Tag SNPs
Tamar Barzuza, Jacques S. Beckmann, Ron Shamir, Itsik Pe'er |
CPM | 2 |
| 2003 | Resolution of haplotypes and haplotype frequencies from SNP genotypes of pooled samplesabstractRecent efforts to characterize genetic variation indicate that humans share large chromosomal blocks, along which little to no recombination is observable. Thus, on a segment-by-segment basis, only a handful of haplotypes account for most human genotypes. Currently, the challenge of registering haplotypes and their frequencies is met by genotyping individuals one by one, a process which is overall resource intensive. Instead, we propose utilizing the ability of current genotyping technologies to pool DNA samples and output allele frequencies of SNP markers. We enable inference of haplotypes and haplotype frequencies from such pooled data, by novel computational methods. This strategy harnesses the economics of pooling for the task of haplotyping, potentially offering a 10-20-fold saving in genotyping reactions performed.We show that a small number of pools can be used to accurately and cost-effectively reconstruct a haplotype block and determine haplotype frequencies. Itsik Pe'er, Jacques S. Beckmann |
RECOMB | 2 |