ParSMURF, a high-performance computing tool for the genome-wide detection of pathogenic variants.

ABSTRACT:

Background

Several prediction problems in computational biology and genomic medicine are characterized by both big data as well as a high imbalance between examples to be learned, whereby positive examples can represent a tiny minority with respect to negative examples. For instance, deleterious or pathogenic variants are overwhelmed by the sea of neutral variants in the non-coding regions of the genome: thus, the prediction of deleterious variants is a challenging, highly imbalanced classification problem, and classical prediction tools fail to detect the rare pathogenic examples among the huge amount of neutral variants or undergo severe restrictions in managing big genomic data.

Results

To overcome these limitations we propose parSMURF, a method that adopts a hyper-ensemb

SUBMITTER: Petrini A 

PROVIDER: S-EPMC7244787 | biostudies-literature | 2020 May

REPOSITORIES: biostudies-literature