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Fusing Dual-Event Data Sets for Mycobacterium tuberculosis Machine Learning Models and Their Evaluation

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What this work says

Outgoing claims: the papers this work cites, builds on, or argues with.

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Bayesian Models Leveraging Bioactivity and Cytotoxicity Information for Drug Discovery

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Rising standards for tuberculosis drug development

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High-throughput screening for inhibitors of Mycobacterium tuberculosis H37Rv

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Improved Naïve Bayesian Modeling of Numerical Data for Absorption, Distribution, Metabolism and Excretion (ADME) Property Prediction

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Combination of Similarity Rankings Using Data Fusion

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Support vector machines

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TB Mobile: a mobile app for anti-tuberculosis molecules with known targets

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Open Source Drug Discovery with Bioclipse

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Incoming claims: where this work appears in other papers’ reference lists and arguments.

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