Machine Learning for Multi-Omics Data Integration and Variant Pathogenicity Estimation

Shuang Li; K. Joeri van der Velde; Morris A. Swertz

doi:10.1109/eScience.2018.00062

2018 IEEE 14th International Conference on e-Science (e-Science)

Machine Learning for Multi-Omics Data Integration and Variant Pathogenicity Estimation

Year: 2018, Pages: 301-301

DOI Bookmark: 10.1109/eScience.2018.00062

Authors

Shuang Li, Genomics Coordination Center, Univ. of Groningen, Groningen, Netherlands
K. Joeri van der Velde, Genomics Coordination Center, Univ. of Groningen, Groningen, Netherlands
Morris A. Swertz, Genomics Coordination Center, Univ. of Groningen, Groningen, Netherlands

Abstract

The rapid advances in the genomic study have made genetics testing common in today's diagnostic practices [1]. Next-generation sequencing provides researchers with a huge amount of genomic data, yet the interpretation still in its infancy [5].The diagnostics yield is still around 30% [1, 3, 8]. Interpretation tools for analyzing the genomic data is needed.

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Machine Learning for Multi-Omics Data Integration and Variant Pathogenicity Estimation

Authors

Abstract

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