Position kernels as a key to making sense of very rare and private single-nucleotide variants

Ulrich Bodenhofer; Sepp Hochreiter

Position kernels as a key to making sense of very rare and private single-nucleotide variants

Publikation: Konferenzbeitrag › Papier › Begutachtung

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Abstract

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.

Originalsprache	Englisch (Amerika)
Publikationsstatus	Veröffentlicht - Dez. 2017
Extern publiziert	Ja
Veranstaltung	NIPS Workshop on Machine Learning in Computational Biology - Long Beach, CA, USA/Vereinigte Staaten Dauer: 9 Dez. 2017 → 9 Dez. 2017

Workshop

Workshop	NIPS Workshop on Machine Learning in Computational Biology
Land/Gebiet	USA/Vereinigte Staaten
Zeitraum	09.12.2017 → 09.12.2017

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BodenhoferHochreiter17

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abstract = "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.",

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AU - Hochreiter, Sepp

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AB - 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.

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