GPU Accelerated Support Vector Machines for Mining High-Throughput Screening Data

2009 ◽  
Vol 49 (12) ◽  
pp. 2718-2725 ◽  
Author(s):  
Quan Liao ◽  
Jibo Wang ◽  
Yue Webster ◽  
Ian A. Watson
Keyword(s):  
Support Vector Machines ◽  
High Throughput ◽  
High Throughput Screening ◽  
Support Vector ◽  
Vector Machines

scholarly journals Support Vector Machines in HTS Data Mining: Type I MetAPs Inhibition Study

2005 ◽  
Vol 11 (2) ◽  
pp. 138-144 ◽  
Author(s):  
Jianwen Fang ◽  
Yinghua Dong ◽  
Gerald H. Lushington ◽  
Qi-Zhuang Ye ◽  
Gunda I. Georg
Keyword(s):  
Support Vector Machines ◽  
High Throughput Screening ◽  
Minimum Size ◽  
Support Vector ◽  
Type I ◽  
Training Set ◽  
Inhibition Study ◽  
Data Set ◽  
Test Set ◽  
Vector Machines

This article reports a successful application of support vector machines (SVMs) in mining high-throughput screening (HTS) data of a type I methionine aminopeptidases (MetAPs) inhibition study. A library with 43,736 small organic molecules was used in the study, and 1355 compounds in the library with 40% or higher inhibition activity were considered as active. The data set was randomly split into a training set and a test set (3:1 ratio). The authors were able to rank compounds in the test set using their decision values predicted by SVM models that were built on the training set. They defined a novel score PT50, the percentage of the test set needed to be screened to recover 50% of the actives, to measure the performance of the models. With carefully selected parameters, SVM models increased the hit rates significantly, and 50% of the active compounds could be recovered by screening just 7% of the test set. The authors found that the size of the training set played a significant role in the performance of the models. A training set with 10,000 member compounds is likely the minimum size required to build a model with reasonable predictive power.

Soft Biometrics Classification Using Denoising Convolutional Autoencoders and Support Vector Machines

2018 ◽  
Author(s):  
Nelson Marcelo Romero Aquino ◽  
Matheus Gutoski ◽  
Leandro Takeshi Hattori ◽  
Heitor Silvério Lopes
Keyword(s):  
Support Vector Machines ◽  
Support Vector ◽  
Soft Biometrics ◽  
Vector Machines
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