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It is a consequence of huge classes disproportion (even 1000:1), large datasets (over 100,000 of samples) and restricted data representation (mostly high-dimensional, sparse, binary vectors). ... In this paper, we try to tackle this problem through three important innovations. First we represent compounds with 2-dimensional, graph representation. ... We showed a proof of concept solution for the classification of large dataset of extremely imbalanced compounds. ...doi:10.1109/trustcom.2015.581 dblp:conf/trustcom/CzarneckiR15 fatcat:7jghn6lkljgypgif7uedmqq64y
Reviews in computational chemistry
and 15 non-active compounds) and pIC 50 ¼ 6.0 (28 active and 45 non-active compounds). ... ), whereas drugs with no reported cases of TdP in humans were selected as the non-active compounds (204 for training and 39 for prediction). ...doi:10.1002/9780470116449.ch6 fatcat:aumcn53nvfhhhocxvav32rhwzm