The component-by-component migration of a program from untyped to typed can trigger unintended performance degradations. When such a degradation occurs, typing well-chosen components can lessen the cost of type enforcement, while typing poorly chosen components can exacerbate it. This article examines whether off-the-shelf profiling tools deliver information that is an effective guide for navigating these migration choices in Typed Racket. Using the rational-programmer method, the article tests this hypothesis with an experiment that involves tens of thousands of performance-debugging scenarios, two different profiler types, and 23 strategies that convert profiler output to migration choices. The most effective strategy relies on a boundary profiler to identify the costliest inter-component boundary and then adds deeply enforced types to both sides. When the output of this profiler fails to identify a boundary, the strategy takes a random migration step—similar to the way many computational approaches resort temporarily to random choices to avoid getting stuck. The strategy completely eliminates the cost of run-time type checks in more than half of the scenarios if at most two performance degradations are tolerable along the way.

MOTIVATION: Model organisms are widely used to better understand the molecular causes of human disease. While sequence similarity greatly aids this cross-species transfer, sequence similarity does not imply functional similarity, and thus, several current approaches incorporate protein-protein interactions to help map findings between species. Existing transfer methods either formulate the alignment problem as a matching problem which pits network features against known orthology, or more recently, as a joint embedding problem. RESULTS: We propose a novel state-of-the-art joint embedding solution: Embeddings to Network Alignment (ETNA). ETNA generates individual network embeddings based on network topological structure and then uses a Natural Language Processing-inspired cross-training approach to align the two embeddings using sequence-based orthologs. The final embedding preserves both within and between species gene functional relationships, and we demonstrate that it captures both pairwise and group functional relevance. In addition, ETNA's embeddings can be used to transfer genetic interactions across species and identify phenotypic alignments, laying the groundwork for potential opportunities for drug repurposing and translational studies. AVAILABILITY AND IMPLEMENTATION: https://github.com/ylaboratory/ETNA.