Demo Data from EDBT 2026 / reviewers in the wild / expert
M. Schoniger
dblp:37/1535
· DBLP profile ↗
2ranked-venue papers
1as first author
0since 2021 · last 1997
—ORCID · none
Domains — the database's venue-derived domains; a paper can count in several
Applied, interdisciplinary, general and emerging computing · 2 · 1 first-author
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 · 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
phylogenetics |
0.0 | 2 | 1997 | DISTREE: a tool for estimating genetic distances between aligned DNA sequences · Comput. Appl. Biosci. 1997 Simulating efficiently the evolution of DNA sequences · Comput. Appl. Biosci. 1995 |
Bioinformatics and computational biology › molecular evolution
molecular evolution simulation |
0.0 | 1 | 1995 | Simulating efficiently the evolution of DNA sequences · Comput. Appl. Biosci. 1995 |
Bioinformatics and computational biology › molecular evolution
sequence evolution simulation |
0.0 | 1 | 1995 | Simulating efficiently the evolution of DNA sequences · Comput. Appl. Biosci. 1995 |
Bioinformatics and computational biology
molecular evolution |
0.0 | 1 | 1997 | DISTREE: a tool for estimating genetic distances between aligned DNA sequences · Comput. Appl. Biosci. 1997 |
Methods — techniques the papers use, named apart from their topics
neighbor-joining · 0.0gamma-distributed rate model · 0.0stochastic model · 0.0pseudo-random number generation · 0.0
| Year | Publication | Venue | Position |
|---|---|---|---|
| 1997 | DISTREE: a tool for estimating genetic distances between aligned DNA sequencesabstractMOTIVATION: Substitution rates estimated from aligned DNA data can be used as genetic distances to investigate the phylogenetic relationship of those sequences. For this purpose, a Markov model of nucleotide substitution has to be assumed that describes this process most adequately. RESULTS: A program is presented that estimates substitution rates and their standard errors for a variety of Markov models. The model introduced by Hasegawa et al. (J. Mol. Evol., 22, 160-174, 1985) is the only one for which distances and standard deviations need to be calculated numerically, since analytical formulae cannot be derived. Each model is implemented in two different variants: (i) assuming rate homogeneity or (ii) starting from Gamma-distributed substitution rates across sequence sites. The estimation of heterogeneous substitution rates is based on a method suggested by Tamura and Nei (Mol. Biol. Evol., 10, 512-526, 1993). All required parameters are estimated from sequence data, hence the user is not asked to supply any additional input. One goal of the program is to support the user when choosing a particular model that describes most adequately the evolution of the given data set. For this purpose, a more detailed analysis of this model fit is provided. Phylogenetic trees reconstructed from the inferred distances using the neighbor-joining algorithm are also available. J. Schafer, M. Schoniger |
Comput. Appl. Biosci. | 2 |
| 1995 | Simulating efficiently the evolution of DNA sequencesabstractTwo menu-driven FORTRAN programs are described that simulate the evolution of DNA sequences in accordance with a user-specified model. This general stochastic model allows for an arbitrary stationary nucleotide composition and any transition-transversion bias during the process of base substitution. In addition, the user may define any hypothetical model tree according to which a family of sequences evolves. The programs suggest the computationally most inexpensive approach to generate nucleotide substitutions. Either reproducible or non-repeatable simulations, depending on the method of initializing the pseudo-random number generator, can be performed. The corresponding options are offered by the interface menu. M. Schoniger, Arndt von Haeseler |
Comput. Appl. Biosci. | 1 |