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The ability to estimate the probability of a drug to receive approval in clinical trials provides natural advantages to optimizing pharmaceutical research workflows. Success rates of a clinical trials have deep implications to costs, duration of development, and under pressure due to stringent regulatory approval processes. We propose a machine learning approach that can predict the outcome of trial with reliable accuracies, using biological activities, physico-chemical properties of the compounds, target related features and NLP-based compound representation.
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Component based software development (CBSD) is a trend in the indusrty. This dataset contains the size of the components in various softwares viz. WEKA, Apache crypto library, Apache Codec library and basic linear alebra library by M. Elsheimy. The first three are in Java and last one is in C# programming languages, From the WEKA repository, 11 packages have been considered,
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A dataset containing system and service performance metrics, and user-facing quality metrics generated by running load tests against a microservice-based system under varying environmental and service configuration conditons.
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