TY  - JOUR
A1  - Hansen, Bjoern Oest
A1  - Meyer, Etienne H.
A1  - Ferrari, Camilla
A1  - Vaid, Neha
A1  - Movahedi, Sara
A1  - Vandepoele, Klaas
A1  - Nikoloski, Zoran
A1  - Mutwil, Marek
T1  - Ensemble gene function prediction database reveals genes important for complex I formation in Arabidopsis thaliana
JF  - New phytologist : international journal of plant science
N2  - Recent advances in gene function prediction rely on ensemble approaches that integrate results from multiple inference methods to produce superior predictions. Yet, these developments remain largely unexplored in plants. We have explored and compared two methods to integrate 10 gene co-function networks for Arabidopsis thaliana and demonstrate how the integration of these networks produces more accurate gene function predictions for a larger fraction of genes with unknown function. These predictions were used to identify genes involved in mitochondrial complex I formation, and for five of them, we confirmed the predictions experimentally. The ensemble predictions are provided as a user-friendly online database, EnsembleNet. The methods presented here demonstrate that ensemble gene function prediction is a powerful method to boost prediction performance, whereas the EnsembleNet database provides a cutting-edge community tool to guide experimentalists.
KW  - Arabidopsis thaliana
KW  - co-function network
KW  - complex I
KW  - ensemble prediction
KW  - gene function prediction
Y1  - 2017
U6  - https://doi.org/10.1111/nph.14921
SN  - 0028-646X
SN  - 1469-8137
VL  - 217
IS  - 4
SP  - 1521
EP  - 1534
PB  - Wiley
CY  - Hoboken
ER  - 
TY  - JOUR
A1  - Bürger, Gerd
T1  - A seamless filter for daily to seasonal forecasts, with applications to Iran and Brazil
JF  - Quarterly Journal of the Royal Meteorological Society
N2  - A digital filter is introduced which treats the problem of predictability versus time averaging in a continuous, seamless manner. This seamless filter (SF) is characterized by a unique smoothing rule that determines the strength of smoothing in dependence on lead time. The rule needs to be specified beforehand, either by expert knowledge or by user demand. As a result, skill curves are obtained that allow a predictability assessment across a whole range of time-scales, from daily to seasonal, in a uniform manner. The SF is applied to downscaled SEAS5 ensemble forecasts for two focus regions in or near the tropical belt, the river basins of the Karun in Iran and the Sao Francisco in Brazil. Both are characterized by strong seasonality and semi-aridity, so that predictability across various time-scales is in high demand. Among other things, it is found that from the start of the water year (autumn), areal precipitation is predictable with good skill for the Karun basin two and a half months ahead; for the Sao Francisco it is only one month, longer-term prediction skill is just above the critical level.
KW  - climate drift
KW  - ensemble prediction
KW  - seamless prediction
KW  - seasonal forecast skill
Y1  - 2019
VL  - 146
IS  - 726
PB  - WILEY-VCH
CY  - Weinheim
ER  - 
TY  - GEN
A1  - Bürger, Gerd
T1  - A seamless filter for daily to seasonal forecasts, with applications to Iran and Brazil
T2  - Postprints der Universität Potsdam : Mathematisch-Naturwissenschaftliche Reihe
N2  - A digital filter is introduced which treats the problem of predictability versus time averaging in a continuous, seamless manner. This seamless filter (SF) is characterized by a unique smoothing rule that determines the strength of smoothing in dependence on lead time. The rule needs to be specified beforehand, either by expert knowledge or by user demand. As a result, skill curves are obtained that allow a predictability assessment across a whole range of time-scales, from daily to seasonal, in a uniform manner. The SF is applied to downscaled SEAS5 ensemble forecasts for two focus regions in or near the tropical belt, the river basins of the Karun in Iran and the Sao Francisco in Brazil. Both are characterized by strong seasonality and semi-aridity, so that predictability across various time-scales is in high demand. Among other things, it is found that from the start of the water year (autumn), areal precipitation is predictable with good skill for the Karun basin two and a half months ahead; for the Sao Francisco it is only one month, longer-term prediction skill is just above the critical level.
T3  - Zweitveröffentlichungen der Universität Potsdam : Mathematisch-Naturwissenschaftliche Reihe - 1214 
KW  - climate drift
KW  - ensemble prediction
KW  - seamless prediction
KW  - seasonal forecast skill
Y1  - 2019
U6  - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:kobv:517-opus4-523835
SN  - 1866-8372
IS  - 726
ER  -