640 12 3106 2 15376 document 3106 MIC_Result.csv text/plain 220e86ee4679126ec4f5a3311b63bf04 MD5 3760611 2021-04-03 04:46:09 http://edata.bham.ac.uk/640/1/MIC_Result.csv 640 1 1 text/plain text public cc_by_4
MIC_Result.csv
data archive 267 disk0/00/00/06/40 2021-04-14 17:04:19 2021-04-14 17:43:23 2021-04-14 17:04:19 data_collection show Wan Jizheng JXW178@bham.ac.uk Maximal Information Coefficient (MIC) full result data 10col_ephy MIC result for the second experiment in the thesis "Semantic Impact – a novel approach for domain concept selection in ontology learning", which aims to assess the semantic impact for various disease concepts in the Candida domain (MIC value is one of the parameters for the final semantic impact calculation). 2021-04-14 published University of Birmingham https://doi.org/10.25500/edata.bham.00000640 Dataset research-data@contacts.bham.ac.uk School of Computer Science PhD Thesis: "Semantic Impact – a novel approach for domain concept selection in ontology learning" na na na TRUE