Development of Machine Learning Models for Accurately Predicting and Ranking the Activity of Lead Molecules to Inhibit PRC2 Dependent Cancer

Development of Machine Learning Models for Accurately Predicting and Ranking the Activity of Lead Molecules to Inhibit PRC2 Dependent Cancer

Disruption of epigenetic processes to eradicate tumor cells is among the most promising interventions for cancer control. EZH2 (Enhancer of zeste homolog 2), a catalytic component of polycomb repressive complex 2 (PRC2), methylates lysine 27 of histone H3 to promote transcriptional silencing and is...

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Hlavní autoři: Danishuddin, Vikas Kumar, Shraddha Parate, Ashutosh Bahuguna, Gihwan Lee, Myeong Ok Kim, Keun Woo Lee
Médium: Článek
Jazyk:English
Vydáno: MDPI AG 2021-07-01
Edice:Pharmaceuticals
Témata:
cancer
epigenetic
PRC2
machine learning
multi-class models
On-line přístup:https://www.mdpi.com/1424-8247/14/7/699

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https://www.mdpi.com/1424-8247/14/7/699

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