Conformal prediction is usually presented as a method of set prediction [10, Part I], i.e., as a way of producing prediction sets (rather than pointpredictions). Another way to look at a conformal predictor is as a way of producin g a p-value function (discussed, in a slightly different context, in, e.g., [4]), which is a function mapping each possible label y of a test object to the corresponding conformal p-value. In analogy with "prediction sets", we will call su ch p-value functions "prediction functions".

Proteins are sequences of amino acids that serve as the basic building blocks of living organisms. Despite rapidly growing databases documenting structural and functional information for various protein sequences, our understanding of proteins remains limited because of the large possible sequence space and the complex inter- and intra-molecular forces. Deep learning, which is characterized by its ability to learn relevant features directly from large datasets, has demonstrated remarkable performance in fields such as computer vision and natural language processing. It has also been increasingly applied in recent years to the data-rich domain of protein sequences with great success, most notably with Alphafold2's breakout performance in the protein structure prediction. The performance improvements achieved by deep learning unlocks new possibilities in the field of protein bioinformatics, including protein design, one of the most difficult but useful tasks. In this paper, we broadly categorize problems in protein bioinformatics into three main categories: 1) structural prediction, 2) functional prediction, and 3) protein design, and review the progress achieved from using deep learning methodologies in each of them. We expand on the main challenges of the protein design problem and highlight how advances in structural and functional prediction have directly contributed to design tasks. Finally, we conclude by identifying important topics and future research directions.

In some applications of machine learning, predictive performance is all that matters. Indeed, in these cases it is often possible to build a large number of alternative models that, while different in structure, all make excellent near-optimal functional predictions. In biology, however, prediction is not enough. The key additional question is which of the many excellent predictive models is the one actually used by the living system, as optimized not by computation but by evolution. DCell provides proof-ofconcept of a system that, while optimizing functional prediction, respects biological structure.

We describe a machine learning method for predicting the value of a real-valued function, given the values of multiple input variables. The method induces solutions from samples in the form of ordered disjunctive normal form (DNF) decision rules. A central objective of the method and representation is the induction of compact, easily interpretable solutions. This rule-based decision model can be extended to search efficiently for similar cases prior to approximating function values. Experimental results on real-world data demonstrate that the new techniques are competitive with existing machine learning and statistical methods and can sometimes yield superior regression performance.