Machine learning was applied to a challenging and biologically significant protein classification problem: the prediction of avonoid UGT acceptor regioselectivity from primary sequence. Novel indices characterizing graphical models of residues were proposed and found to be widely distributed among existing amino acid indices and to cluster residues appropriately. UGT subsequences biochemically linked to regioselectivity were modeled as sets of index sequences. Several learning techniques incorporating these UGT models were compared with classifications based on standard sequence alignment scores. These techniques included an application of time series distance functions to protein classification. Time series distances defined on the index sequences were used in nearest neighbor and support vector machine classifiers. Additionally, Bayesian neural network classifiers were applied to the index sequences. The experiments identified improvements over the nearest neighbor and support vector machine classifications relying on standard alignment similarity scores, as well as strong correlations between specific subsequences and regioselectivities.

Researchers in music education are exploring the use of virtual reality (VR) and augmented reality (AR) to support piano instruction. Beginner piano students tend to receive short, infrequent lessons, which they practice on their own. This lack of instructor feedback creates opportunities for students to develop improper technique. Current strategies for using AR and VR to guide solo practice use moving shapes to help students to identify what notes to play. Improvements in commercial AR/VR technology will be needed to provide more detailed real-time feedback.