Identification of novel RNA design candidates by clustering the extended RNA-as-graphs library

Conclusions: Using linear regression with unsupervised clustering, or quadratic regression with supervised clustering, provides better accuracies than supervised/linear clustering. All accuracies are better than random, especially for newly added existing topologies, thus lending credibility to our approach.General Significance: Our updated RAG-3D database and motif classification by clustering present new RNA substructures and RNA-like motifs as novel design candidates.

Source: Biochimica et Biophysica Acta (BBA) General Subjects - January 17, 2020 Category: Biochemistry Source Type: research