We present an approach for convolving single-nucleotide variants (SNVs) with a position kernel in order to augment SNVs with information about close-by SNVs. By means of the Position-Dependent Kernel Association Test (PODKAT), we demonstrate the potential of this approach to leverage the analysis of rare and private SNVs. Finally, we also provide some ideas how machine-learning based predictions from genomic data can benefit from this augmentation.
|Original language||English (American)|
|Publication status||Published - Dec 2017|
|Event||NIPS Workshop on Machine Learning in Computational Biology - Long Beach, CA, United States|
Duration: 9 Dec 2017 → 9 Dec 2017
|Workshop||NIPS Workshop on Machine Learning in Computational Biology|
|Period||09.12.2017 → 09.12.2017|