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Bioinformatics (Oxford, England)|July 14, 2020
IDMIL: an alignment-free Interpretable Deep Multiple Instance Learning (MIL) for predicting disease from whole-metagenomic dataMohammad Arifur Rahman, Huzefa RangwalaIEEE/ACM Transactions on Computational Biology and Bioinformatics|October 6, 2017
Phenotype Prediction from Metagenomic Data Using Clustering and Assembly with Multiple Instance Learning (CAMIL)Mohammad Arifur Rahman, Nathan LaPierre, Huzefa RangwalaJournal of Bioinformatics and Computational Biology|November 9, 2017
Metagenome sequence clustering with hash-based canopiesMohammad Arifur Rahman, Nathan LaPierre, Huzefa Rangwala, et al.Bioinformatics (Oxford, England)|September 29, 2005
Profile-based direct kernels for remote homology detection and fold recognitionHuzefa Rangwala, George KarypisComputational Systems Bioinformatics. Computational Systems Bioinformatics Conference|October 24, 2007
fRMSDPred: predicting local RMSD between structural fragments using sequence informationHuzefa Rangwala, George KarypisProteins|February 27, 2008
fRMSDPred: predicting local RMSD between structural fragments using sequence informationHuzefa Rangwala, George KarypisJournal of Bioinformatics and Computational Biology|August 2, 2012
Metagenomic taxonomic classification using extreme learning machinesZeehasham Rasheed, Huzefa RangwalaBMC Bioinformatics|October 18, 2006
Building multiclass classifiers for remote homology detection and fold recognitionHuzefa Rangwala, George KarypisBioinformatics (Oxford, England)|January 24, 2007
Incremental window-based protein sequence alignment algorithmsHuzefa Rangwala, George KarypisIEEE/ACM Transactions on Computational Biology and Bioinformatics|September 11, 2015
Classifying Protein Sequences Using Regularized Multi-Task LearningAnveshi Charuvaka, Huzefa RangwalaPageof 5