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Identifying At-Risk Students for Early Interventions - A Time-Series Clustering Approach.

, , , , , and . IEEE Trans. Emerg. Top. Comput., 5 (1): 45-55 (2017)

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Reusing Random Walks in Monte Carlo Methods for Linear Systems., and . ICCS, volume 9 of Procedia Computer Science, page 383-392. Elsevier, (2012)Grid-based Quasi-Monte Carlo Applications., and . Monte Carlo Methods Appl., 11 (1): 39-55 (2005)Construction of Protein Backbone Fragments Libraries on Large Protein Sets Using a Randomized Spectral Clustering Algorithm., , , and . ISBRA, volume 10330 of Lecture Notes in Computer Science, page 108-119. Springer, (2017)FLEXc: protein flexibility prediction using context-based statistics, predicted structural features, and sequence information., , , , , , and . BMC Bioinform., 17 (S-8): 281 (2016)Efficient Randomized Algorithms for the Fixed-Precision Low-Rank Matrix Approximation., , and . SIAM J. Matrix Anal. Appl., 39 (3): 1339-1359 (2018)An adaptive and trustworthy software testing framework on the grid., and . J. Supercomput., 46 (2): 124-138 (2008)Building a Knowledge-Based Statistical Potential by Capturing High-Order Inter-residue Interactions and its Applications in Protein Secondary Structure Assessment., , , and . Journal of Chemical Information and Modeling, 53 (2): 500-508 (2013)Secure Remote Compiling Services on the Grid., , , , and . PDPTA, page 373-378. CSREA Press, (2005)Accurate Prediction of Human Essential Proteins Using Ensemble Deep Learning., , , , and . IEEE ACM Trans. Comput. Biol. Bioinform., 19 (6): 3263-3271 (2022)Biomedical Data and Deep Learning Computational Models for Predicting Compound-Protein Relations., , , , and . IEEE ACM Trans. Comput. Biol. Bioinform., 19 (4): 2092-2110 (2022)