A review of feature selection techniques in bioinformatics
Feature selection techniques have become an apparent need in many bioinformatics
applications. In addition to the large pool of techniques that have already been developed in …
applications. In addition to the large pool of techniques that have already been developed in …
DNA microarray technology: devices, systems, and applications
MJ Heller - Annual review of biomedical engineering, 2002 - annualreviews.org
▪ Abstract In this review, recent advances in DNA microarray technology and their
applications are examined. The many varieties of DNA microarray or DNA chip devices and …
applications are examined. The many varieties of DNA microarray or DNA chip devices and …
Persistent serum protein signatures define an inflammatory subcategory of long COVID
Long COVID or post-acute sequelae of SARS-CoV-2 (PASC) is a clinical syndrome featuring
diverse symptoms that can persist for months following acute SARS-CoV-2 infection. The …
diverse symptoms that can persist for months following acute SARS-CoV-2 infection. The …
Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2
In comparative high-throughput sequencing assays, a fundamental task is the analysis of
count data, such as read counts per gene in RNA-seq, for evidence of systematic changes …
count data, such as read counts per gene in RNA-seq, for evidence of systematic changes …
A survey on filter techniques for feature selection in gene expression microarray analysis
A plenitude of feature selection (FS) methods is available in the literature, most of them
rising as a need to analyze data of very high dimension, usually hundreds or thousands of …
rising as a need to analyze data of very high dimension, usually hundreds or thousands of …
[引用][C] Data analysis using regression and multilevel/hierarchical models
A Gelman - 2007 - books.google.com
Data Analysis Using Regression and Multilevel/Hierarchical Models is a comprehensive
manual for the applied researcher who wants to perform data analysis using linear and …
manual for the applied researcher who wants to perform data analysis using linear and …
Linear models and empirical bayes methods for assessing differential expression in microarray experiments
GK Smyth - Statistical applications in genetics and molecular …, 2004 - degruyter.com
The problem of identifying differentially expressed genes in designed microarray
experiments is considered. Lonnstedt and Speed (2002) derived an expression for the …
experiments is considered. Lonnstedt and Speed (2002) derived an expression for the …
Adjusting batch effects in microarray expression data using empirical Bayes methods
WE Johnson, C Li, A Rabinovic - Biostatistics, 2007 - academic.oup.com
Non-biological experimental variation or “batch effects" are commonly observed across
multiple batches of microarray experiments, often rendering the task of combining data from …
multiple batches of microarray experiments, often rendering the task of combining data from …
Springer series in statistics
The idea for this book came from the time the authors spent at the Statistics and Applied
Mathematical Sciences Institute (SAMSI) in Research Triangle Park in North Carolina …
Mathematical Sciences Institute (SAMSI) in Research Triangle Park in North Carolina …
[PDF][PDF] Variance stabilization applied to microarray data calibration and to the quantification of differential expression
W Huber, A Von Heydebreck, H Sültmann… - …, 2002 - researchgate.net
We introduce a statistical model for microarray gene expression data that comprises data
calibration, the quantification of differential expression, and the quantification of …
calibration, the quantification of differential expression, and the quantification of …