University of Maryland Baltimore County, UNITED STATES These algorithms can successfully produce dissimilar training and test sets for more protein families than a previous approach, allowing us to include more families in benchmark datasets for biological sequence analysis tasks.Ĭitation: Petti S, Eddy SR (2022) Constructing benchmark test sets for biological sequence analysis using independent set algorithms. To this end, we used ideas from computer science involving graph algorithms to design two new methods for splitting sequence data into dissimilar training and test sets. This motivates the design of strategies for dividing sequence families into dissimilar training and test sets. In this case, applying a standard approach of randomly splitting the data into training and test sets could yield test sequences that are nearly identical to some sequence in the training set, and the resultant benchmark may overstate the model’s performance. Biological sequences (such as protein or RNA) within a particular family are related by evolution and therefore may be very similar to each other. This ensures that the reported performance accurately reflects how well the method would do on previously unseen data. Typically, machine learning and statistical inference models are trained on a “training” dataset and evaluated on an separate “test” set.

Benchmark test