@article{Wang_B-2022_96146,
title = {Systematic comparison of ranking aggregation methods for gene lists in experimental results},
author = {Wang, B. and Law, A. and Regan, T. and Parkinson, N. and Cole, J. and Russell, C. and Dockrell, D. and Gutmann, M. and Baillie, J.},
month = {sep},
year = {2022},
abstract = {Motivation:A common experimental output in biomedical science is a list of genes implicated in a given biological process or disease. The gene lists resulting from a group of studies answering the same, or similar, questions can be combined by ranking aggregation methods to find a consensus or a more reliable answer. Evaluating a ranking aggregation method on a specific type of data before using it is required to support the reliability since the property of a dataset can influence the performance of an algorithm. Such evaluation on gene lists is usually based on a simulated database because of the lack of a known truth for real data. However, simulated datasets tend to be too small compared to experimental data and neglect key features, including heterogeneity of quality, relevance and the inclusion of unranked lists.},
journal = {Bioinformatics},
publisher = {Oxford University Press},
url = {https://doi.org/10.1093/bioinformatics/btac621},
}
