Prediction of Lysine-Malonylation Sites via Sequential and Physicochemical Features

Simple item page
dc.contributor.authorAhmed, Asif
dc.contributor.authorSarkar, Kenedy
dc.contributor.authorAziz, Yeazullah
dc.contributor.authorKhan, Toha
dc.date.accessioned2018-10-08T11:23:35Z
dc.date.available2018-10-08T11:23:35Z
dc.date.issued2018-10
dc.description.abstractLysine 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.identifier.urihttp://dspace.uiu.ac.bd/handle/52243/479
dc.language.isoenen_US
dc.subjectMachine Learningen_US
dc.subjectBioinformaticsen_US
dc.titlePrediction of Lysine-Malonylation Sites via Sequential and Physicochemical Featuresen_US
dc.typeThesisen_US

Files

Original bundle

Now showing 1 - 1 of 1
Thumbnail Image
Name:
Thesis_Machine_Learning_Bioinformatics_Malonylation.pdf
Size:
487.8 KB
Format:
Adobe Portable Document Format
Description:
Download

License bundle

Now showing 1 - 1 of 1
Thumbnail Image
Name:
license.txt
Size:
1.62 KB
Format:
Item-specific license agreed upon to submission
Description:
Download

Collections

B.Sc Thesis/Project