Current MInDS graduate students Lodewijk Brand, Kai Liu, and Saad Elbeleidy recently had their paper, “Learning Robust Multi-Label Sample Specific Distances,” accepted to appear in the Proceedings of the 23rd Annual International Conference on Research in Computational Molecular Biology (RECOMB 2019), Washington, D.C.

In this work we propose a novel multi-label Robust Sample Specific Distance (RSSD) method to identify multi-class HIV drug resistance. Our method is novel in that it can illustrate the relative strength of the drug resistance of a reverse transcriptase sequence against a given drug nucleoside analogue and learn the distance metrics for all the drug resistances. To learn the proposed RSSDs, we formulate a learning objective that maximizes the ratio of the summations of a number of L1-norm distances, which is difficult to solve in general. To solve this optimization problem, we derive an efficient, non-greedy, iterative algorithm with rigorously proved convergence. Our new method has been verified on a public HIV-1 drug resistance data set with over 600 RT sequences and five nucleoside analogues.

This work will be presented in May 2019 at George Washington University.