Asymmetric trichotomous partitioning overcomes dataset limitations in building machine learning models for predicting siRNA efficacy.

ABSTRACT: Chemically modified small interfering RNAs (siRNAs) are promising therapeutics guiding sequence-specific silencing of disease genes. Identifying chemically modified siRNA sequences that effectively silence target genes remains challenging. Such determinations necessitate computational algorithms. Machine learning is a powerful predictive approach for tackling biological problems but typically requires datasets significantly larger than most available siRNA datasets. Here, we describe a framework applying machine learning to a small dataset (356 modified sequences) for siRNA efficacy prediction. To overcome noise and biological limitations in siRNA datasets, we apply a trichotomous, two-threshold, partitioning approach, producing several combinations of classification threshold pairs. We th

SUBMITTER: Monopoli KR 

PROVIDER: S-EPMC10338369 | biostudies-literature | 2023 Sep

REPOSITORIES: biostudies-literature