Title:Prediction of Deleterious Single Amino Acid Polymorphisms with a
Consensus Holdout Sampler
Volume: 25
Issue: 3
Author(s): Óscar Álvarez-Machancoses, Eshel Faraggi, Enrique J. deAndrés-Galiana, Juan L. Fernández-Martínez and Andrzej Kloczkowski*
Affiliation:
- Institute for Genomic Medicine, Nationwide Children’s Hospital, Columbus, OH, USA
- Department
of Pediatrics, The Ohio State University, Columbus, OH, USA
Keywords:
Polymorphisms, holdout sampler, protein mutation, deep sampling, machine learning, single amino acid variants.
Abstract:
Background: Single Amino Acid Polymorphisms (SAPs) or nonsynonymous Single Nucleotide
Variants (nsSNVs) are the most common genetic variations. They result from missense
mutations where a single base pair substitution changes the genetic code in such a way that the triplet
of bases (codon) at a given position is coding a different amino acid. Since genetic mutations
sometimes cause genetic diseases, it is important to comprehend and foresee which variations are
harmful and which ones are neutral (not causing changes in the phenotype). This can be posed as a
classification problem.
Methods: Computational methods using machine intelligence are gradually replacing repetitive and
exceedingly overpriced mutagenic tests. By and large, uneven quality, deficiencies, and irregularities
of nsSNVs datasets debase the convenience of artificial intelligence-based methods. Subsequently,
strong and more exact approaches are needed to address these problems. In the present
work paper, we show a consensus classifier built on the holdout sampler, which appears strong and
precise and outflanks all other popular methods.
Results: We produced 100 holdouts to test the structures and diverse classification variables of diverse
classifiers during the training phase. The finest performing holdouts were chosen to develop a
consensus classifier and tested using a k-fold (1 ≤ k ≤5) cross-validation method. We also examined
which protein properties have the biggest impact on the precise prediction of the effects of nsSNVs.
Conclusion: Our Consensus Holdout Sampler outflanks other popular algorithms, and gives excellent
results, highly accurate with low standard deviation. The advantage of our method emerges
from using a tree of holdouts, where diverse LM/AI-based programs are sampled in diverse ways.
