BioConductor is an R Package “framework’ (or sub-environment) for bioinformatics; it has a large-scale peer-review system for packages, and any package that is submitted must use and "blend" specific structural components. More importantly, BioConductor has a Discussion Forum (akin to StackOverflow) but specific to BioConductor. The main diffierence of BioC with CRAN (in R programming) is that all packages contributing to BioConductor are somewhat related, must be installed using specific methods, and must leverage BioConductor structure. In that sense, it’s somewhat similar to PyTorch. However, while PyTorch is for generic DeepLearning, BioConductor works only on bioinformatics (to analyse biological data). In this project you will be using advanced, state-of-the-art natural language processing (NLP) algorithms to analyse BioConductor's discussion forum according to the software releases--meaning, you will be linking your topic-modelling to software version releases. After modelling, you will be expected ccomplete manual perusals of the data, to draw conclusions, and provide visualisations and a report.

 

Note:

 

The project will be co-supervised with my colleague Dr Fatemeh Fard (from the University of British Columbia, Canada), whose work in NLP applied to software engineering and data science is quite novel. Meetings are done mostly with me, but at least once per month with her.