CI-SpliceAI-Improving machine learning predictions of disease causing splicing variants using curated alternative splice sites.

ABSTRACT:

Background

It is estimated that up to 50% of all disease causing variants disrupt splicing. Due to its complexity, our ability to predict which variants disrupt splicing is limited, meaning missed diagnoses for patients. The emergence of machine learning for targeted medicine holds great potential to improve prediction of splice disrupting variants. The recently published SpliceAI algorithm utilises deep neural networks and has been reported to have a greater accuracy than other commonly used methods.

Methods and findings

The original SpliceAI was trained on splice sites included in primary isoforms combined with novel junctions observed in GTEx data, which might introduce noise and de-correlate the machine learning input with its output. Limiting the data to only validated

SUBMITTER: Strauch Y 

PROVIDER: S-EPMC9165884 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature