Prediction of Lysine-Malonylation Sites via Sequential and Physicochemical Features

dc.contributor.author	Ahmed, Asif
dc.contributor.author	Sarkar, Kenedy
dc.contributor.author	Aziz, Yeazullah
dc.contributor.author	Khan, Toha
dc.date.accessioned	2018-10-08T11:23:35Z
dc.date.available	2018-10-08T11:23:35Z
dc.date.issued	2018-10
dc.identifier.uri	http://dspace.uiu.ac.bd/handle/52243/479
dc.description.abstract	Lysine Malonylation is Post Translational Modification responsible for Type2 diabetes, Cancer etc. It is a challenging problem as the data from kmal studies are highly imbalanced. In this work we propose Hybrid sampling a combination of RUS and SMOTE at certain ratios in combination with mutual information feature selection, Balanced Random Forest to solve this problem.	en_US
dc.language.iso	en	en_US
dc.subject	Machine Learning	en_US
dc.subject	Bioinformatics	en_US
dc.title	Prediction of Lysine-Malonylation Sites via Sequential and Physicochemical Features	en_US
dc.type	Thesis	en_US