Demo Data from EDBT 2026 / reviewers in the wild / expert
Paurush Praveen
dblp:95/9005
· DBLP profile ↗
2ranked-venue papers
0as first author
0since 2021 · last 2013
0000-0003-1155-4140ORCID · corroborated
Domains — the database's venue-derived domains; a paper can count in several
Applied, interdisciplinary, general and emerging computing · 2
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 · 77% Computational science and engineering · 23% | |
| Artificial intelligence
1 paper |
Probabilistic and Bayesian machine learning · 100% |
Topics — the 5 heaviest of 6, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Machine learning › Probabilistic and Bayesian machine learning
causal inference |
0.2 | 1 | 2013 | Learning gene network structure from time laps cell imaging in RNAi Knock downs · Bioinform. 2013 |
Bioinformatics and computational biology › network bioinformatics › biological network analysis › biological network inference
gene network inference |
0.2 | 1 | 2013 | Learning gene network structure from time laps cell imaging in RNAi Knock downs · Bioinform. 2013 |
Computational science and engineering › dynamic network analysis
dynamic network inference |
0.1 | 1 | 2011 | Fast and efficient dynamic nested effects models · Bioinform. 2011 |
Bioinformatics and computational biology › biological network › network biology › network inference
gene regulatory network inference |
0.1 | 1 | 2011 | Fast and efficient dynamic nested effects models · Bioinform. 2011 |
Bioinformatics and computational biology › biological network › network biology › network inference › gene regulatory network inference
nested effects models |
0.1 | 1 | 2011 | Fast and efficient dynamic nested effects models · Bioinform. 2011 |
Methods — techniques the papers use, named apart from their topics
time-lapse microscopy · 0.3markov chain monte carlo · 0.3dynamic nested effects models · 0.3factor graph · 0.1bayesian network · 0.1
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2013 | Learning gene network structure from time laps cell imaging in RNAi Knock downsabstractMOTIVATION: As RNA interference is becoming a standard method for targeted gene perturbation, computational approaches to reverse engineer parts of biological networks based on measurable effects of RNAi become increasingly relevant. The vast majority of these methods use gene expression data, but little attention has been paid so far to other data types. RESULTS: Here we present a method, which can infer gene networks from high-dimensional phenotypic perturbation effects on single cells recorded by time-lapse microscopy. We use data from the Mitocheck project to extract multiple shape, intensity and texture features at each frame. Features from different cells and movies are then aligned along the cell cycle time. Subsequently we use Dynamic Nested Effects Models (dynoNEMs) to estimate parts of the network structure between perturbed genes via a Markov Chain Monte Carlo approach. Our simulation results indicate a high reconstruction quality of this method. A reconstruction based on 22 gene knock downs yielded a network, where all edges could be explained via the biological literature. AVAILABILITY: The implementation of dynoNEMs is part of the Bioconductor R-package nem. Henrik Failmezger, Paurush Praveen, Achim Tresch, Holger Fröhlich |
Bioinform. | 2 |
| 2011 | Fast and efficient dynamic nested effects modelsabstractMOTIVATION: Targeted interventions in combination with the measurement of secondary effects can be used to computationally reverse engineer features of upstream non-transcriptional signaling cascades. Nested effect models (NEMs) have been introduced as a statistical approach to estimate the upstream signal flow from downstream nested subset structure of perturbation effects. The method was substantially extended later on by several authors and successfully applied to various datasets. The connection of NEMs to Bayesian Networks and factor graph models has been highlighted. RESULTS: Here, we introduce a computationally attractive extension of NEMs that enables the analysis of perturbation time series data, hence allowing to discriminate between direct and indirect signaling and to resolve feedback loops. AVAILABILITY: The implementation (R and C) is part of the Supplement to this article. Holger Fröhlich, Paurush Praveen, Achim Tresch |
Bioinform. | 2 |