Sequence Kernel for Z’ Factor
RNA interference (RNAi) high-content screening (HCS) enables massive parallel gene
silencing and is increasingly being used to reveal novel connections between genes and disease-relevant phenotypes. The application of genome-scale RNAi relies on the
development of high quality HCS assays. The Z’ factor statistic provides a way to evaluate whether or not screening run conditions (reagents, protocols, instrumentation, kinetics, and other conditions not directly related to the test compounds) are optimized. Z’ factor, introduced by Zhang et al., is a dimensionless value that represents both the variability and the dynamic range between two sets of sample control data. This paper describe a new extension of the Z' factor, which integrates multiple readouts for screening quality assessment. Currently presented multivariate Z’ factor is based on linear projection, which may not be suitable for data with nonlinear structure. This paper proposes an algorithm which extends existing algorithm to deal with nonlinear data by using the sequence kernel function. Using sequence kernel methods for projections, multiple readouts are condensed to a single parameter, based on which the screening run quality is monitored. The method is based on Sequence Alignment Kernel, a function reflecting the quantitative measure of match between
two sequences.
1107848898
silencing and is increasingly being used to reveal novel connections between genes and disease-relevant phenotypes. The application of genome-scale RNAi relies on the
development of high quality HCS assays. The Z’ factor statistic provides a way to evaluate whether or not screening run conditions (reagents, protocols, instrumentation, kinetics, and other conditions not directly related to the test compounds) are optimized. Z’ factor, introduced by Zhang et al., is a dimensionless value that represents both the variability and the dynamic range between two sets of sample control data. This paper describe a new extension of the Z' factor, which integrates multiple readouts for screening quality assessment. Currently presented multivariate Z’ factor is based on linear projection, which may not be suitable for data with nonlinear structure. This paper proposes an algorithm which extends existing algorithm to deal with nonlinear data by using the sequence kernel function. Using sequence kernel methods for projections, multiple readouts are condensed to a single parameter, based on which the screening run quality is monitored. The method is based on Sequence Alignment Kernel, a function reflecting the quantitative measure of match between
two sequences.
Sequence Kernel for Z’ Factor
RNA interference (RNAi) high-content screening (HCS) enables massive parallel gene
silencing and is increasingly being used to reveal novel connections between genes and disease-relevant phenotypes. The application of genome-scale RNAi relies on the
development of high quality HCS assays. The Z’ factor statistic provides a way to evaluate whether or not screening run conditions (reagents, protocols, instrumentation, kinetics, and other conditions not directly related to the test compounds) are optimized. Z’ factor, introduced by Zhang et al., is a dimensionless value that represents both the variability and the dynamic range between two sets of sample control data. This paper describe a new extension of the Z' factor, which integrates multiple readouts for screening quality assessment. Currently presented multivariate Z’ factor is based on linear projection, which may not be suitable for data with nonlinear structure. This paper proposes an algorithm which extends existing algorithm to deal with nonlinear data by using the sequence kernel function. Using sequence kernel methods for projections, multiple readouts are condensed to a single parameter, based on which the screening run quality is monitored. The method is based on Sequence Alignment Kernel, a function reflecting the quantitative measure of match between
two sequences.
silencing and is increasingly being used to reveal novel connections between genes and disease-relevant phenotypes. The application of genome-scale RNAi relies on the
development of high quality HCS assays. The Z’ factor statistic provides a way to evaluate whether or not screening run conditions (reagents, protocols, instrumentation, kinetics, and other conditions not directly related to the test compounds) are optimized. Z’ factor, introduced by Zhang et al., is a dimensionless value that represents both the variability and the dynamic range between two sets of sample control data. This paper describe a new extension of the Z' factor, which integrates multiple readouts for screening quality assessment. Currently presented multivariate Z’ factor is based on linear projection, which may not be suitable for data with nonlinear structure. This paper proposes an algorithm which extends existing algorithm to deal with nonlinear data by using the sequence kernel function. Using sequence kernel methods for projections, multiple readouts are condensed to a single parameter, based on which the screening run quality is monitored. The method is based on Sequence Alignment Kernel, a function reflecting the quantitative measure of match between
two sequences.
1.99
In Stock
5
1
Sequence Kernel for Z’ Factor
Sequence Kernel for Z’ Factor
eBook
$1.99
Related collections and offers
1.99
In Stock
Product Details
| BN ID: | 2940013453661 |
|---|---|
| Publisher: | ReadCycle |
| Publication date: | 12/02/2011 |
| Sold by: | Barnes & Noble |
| Format: | eBook |
| File size: | 360 KB |
From the B&N Reads Blog