数据库名称：SMACC
数据库简述：Small molecule antiviral compound collection
所属国家/地区：Kuwait
数据库主要信息：Budding yeast datasets were used to develop high-performance Multinomial Regression (MN) models capable of predicting the impact of single, double and triple genetic disruptions on viability. These models are interpretable and give realistic non-binary predictions and can predict negative genetic interactions (GIs) in triple-gene knockouts. They are based on a limited set of gene features and their predictions are influenced by the probability of target gene participating in molecular complexes or pathways. Furthermore, the MN models have utility in other organisms such as fission yeast, fruit flies and humans, with the single gene fitness MN model being able to distinguish essential genes necessary for cell-autonomous viability from those required for multicellular survival. Finally, our models exceed the performance of previous models, without sacrificing interpretability.
建立年份：2022
联系信息：Contact information
University/Institution:
University of North Carolina at Chapel Hill
Address:
UNC Eshelman School of Pharmacy, University of North Carolina, Chapel Hill, NC, USA. Electronic address: alex_tropsha@unc.edu.
City:
Province/State:
Country/Region:
Kuwait
Contact name (PI/Team):
Alexander Tropsha
Contact email (PI/Helpdesk):
alex_tropsha@unc.edu
