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   Proteoforms, which arise from post-translational modifications, genetic polymorphisms and RNA splice variants, play a pivotal role as drivers in biology. Understanding proteoforms is essential to unravel the intricacies of biological systems and bridge the gap between genotypes and phenotypes. By analysing whole proteins without digestion, top-down proteomics (TDP) provides a holistic view of the proteome and can decipher protein function, uncover disease mechanisms and advance precision medicine. This Primer explores TDP, including the underlying principles, recent advances and an outlook on the future. The experimental section discusses instrumentation, sample preparation, intact protein separation, tandem mass spectrometry techniques and data collection. The results section looks at how to decipher raw data, visualize intact protein spectra and unravel data analysis. Additionally, proteoform identification, characterization and quantification are summarized, alongside approaches for statistical analysis. Various applications are described, including the human proteoform project and biomedical, biopharmaceutical and clinical sciences. These are complemented by discussions on measurement reproducibility, limitations and a forward-looking perspective that outlines areas where the field can advance, including potential future applications. 

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   Katz-Samuels, Julian; Scott, Clayton (January 2019, Proceedings of the 22nd International Conference on Artificial Intelligence and Statistics (AISTATS))

   We propose a new variant of the top arm identification problem, top feasible arm identification, where there are K arms associated with D-dimensional distributions and the goal is to find m arms that maximize some known linear function of their means subject to the constraint that their means belong to a given set P € R D. This problem has many applications since in many settings, feedback is multi-dimensional and it is of interest to perform constrained maximization. We present problem-dependent lower bounds for top feasible arm identification and upper bounds for several algorithms. Our most broadly applicable algorithm, TF-LUCB-B, has an upper bound that is loose by a factor of OpDlogpKqq. Many problems of practical interest are two dimensional and, for these, it is loose by a factor of OplogpKqq. Finally, we conduct experiments on synthetic and real-world datasets that demonstrate the effectiveness of our algorithms. Our algorithms are superior both in theory and in practice to a naive two-stage algorithm that first identifies the feasible arms and then applies a best arm identification algorithm to the feasible arms. 

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