Data driven algorithm design is an important aspect of modern data science and algorithm
design. Rather than using off the shelf algorithms that only have worst case performance …

Algorithms often have tunable parameters that impact performance metrics such as runtime
and solution quality. For many algorithms used in practice, no parameter settings admit …

Motivation Protein secondary structure prediction is a fundamental precursor to many
bioinformatics tasks. Nearly all state-of-the-art tools when computing their secondary …

Algorithms—for example for scientific analysis—typically have tunable parameters that
significantly influence computational efficiency and solution quality. If a parameter setting …

The performance of most error-correction (EC) algorithms that operate on genomics reads is
dependent on the proper choice of its configuration parameters, such as the value of k in k …

Algorithms typically come with tunable parameters that have a considerable impact on the
computational resources they consume. Too often, practitioners must hand-tune the …

A common way to integrate and analyze large amounts of biological “omic” data is through
pathway reconstruction: using condition-specific omic data to create a subnetwork of a …

Data-driven algorithm configuration is a promising, learning-based approach for beyond
worst-case analysis of algorithms with tunable parameters. An important open problem is the …

This report summarizes the scientific content and activities of the annual symposium
organized by the Student Council of the International Society for Computational Biology …

Computational tools used for genomic analyses are becoming more accurate but also
increasingly sophisticated and complex. This introduces a new problem in that these pieces …