Publication Date
1-1-2026
Journal
Human Mutation
DOI
10.1155/humu/3902530
PMID
41777615
PMCID
PMC12951207
PubMedCentral® Posted Date
3-2-2026
PubMedCentral® Full Text Version
Post-print
Abstract
The standard for in silico pathogenicity prediction of in-frame insertions and deletions (indels) is less established compared to other types of variations. We aimed to systematically assess the performance of in silico machine learning (ML) tools on a patient cohort with inherited retinal diseases (IRDs). The performance of four ML tools (CADD, FATHMM-indel, VEST4, and MetaRNN-indel) was compared. Among them, MetaRNN-indel showed the best overall results. MetaRNN-indel was then applied to 1013 unsolved IRD patients, identifying two likely pathogenic causal variants in two unrelated IRD patients by confirming clinical phenotypes. Hence, our findings indicate that reliable prediction of the pathogenicity of in-frame indels can be achieved using existing ML tools with proper evaluation and tuning.
Keywords
Humans, Machine Learning, INDEL Mutation, Retinal Diseases, Phenotype, Computational Biology, Computer Simulation, Genetic Predisposition to Disease, Cohort Studies, in-frame indel, in-silico, IRD, machine learning, unsolved
Published Open-Access
yes
Recommended Citation
Rauch, David E; Wang, Meng; Hafiz, Muhammad Jafar Hussain; et al., "Assessment of In-Frame Indel Variants in an Unsolved Cohort of Inherited Retinal Diseases Using Machine Learning" (2026). Faculty, Staff and Students Publications. 6906.
https://digitalcommons.library.tmc.edu/baylor_docs/6906