[HTML][HTML] Incorporating biological information into linear models: A Bayesian approach to the selection of pathways and genes
FC Stingo, YA Chen, MG Tadesse… - The annals of applied …, 2011 - ncbi.nlm.nih.gov
The vast amount of biological knowledge accumulated over the years has allowed
researchers to identify various biochemical interactions and define different families of …
researchers to identify various biochemical interactions and define different families of …
Methods for proteogenomics data analysis, challenges, and scalability bottlenecks: a survey
Big Data Proteogenomics lies at the intersection of high-throughput Mass Spectrometry (MS)
based proteomics and Next Generation Sequencing based genomics. The combined and …
based proteomics and Next Generation Sequencing based genomics. The combined and …
Comparison of algorithms for pre-processing of SELDI-TOF mass spectrometry data
A Cruz-Marcelo, R Guerra, M Vannucci, Y Li… - …, 2008 - academic.oup.com
Motivation: Surface-enhanced laser desorption and ionization (SELDI) time of flight (TOF) is
a mass spectrometry technology. The key features in a mass spectrum are its peaks. In order …
a mass spectrometry technology. The key features in a mass spectrum are its peaks. In order …
A novel wavelet‐based thresholding method for the pre‐processing of mass spectrometry data that accounts for heterogeneous noise
In recent years there has been an increased interest in using protein mass spectroscopy to
discriminate diseased from healthy individuals with the aim of discovering molecular …
discriminate diseased from healthy individuals with the aim of discovering molecular …
[PDF][PDF] Bayesian models for variable selection that incorporate biological information
Variable selection has been the focus of much research in recent years. Bayesian methods
have found many successful applications, particularly in situations where the amount of …
have found many successful applications, particularly in situations where the amount of …
Hierarchical Bayesian formulations for selecting variables in regression models
The objective of finding a parsimonious representation of the observed data by a statistical
model that is also capable of accurate prediction is commonplace in all domains of statistical …
model that is also capable of accurate prediction is commonplace in all domains of statistical …
An efficient stochastic search for Bayesian variable selection with high-dimensional correlated predictors
D Kwon, MT Landi, M Vannucci, HJ Issaq… - … statistics & data analysis, 2011 - Elsevier
We present a Bayesian variable selection method for the setting in which the number of
independent variables or predictors in a particular dataset is much larger than the available …
independent variables or predictors in a particular dataset is much larger than the available …
Swarm intelligence based wavelet coefficient feature selection for mass spectral classification: An application to proteomics data
This paper introduces the ant colony algorithm, a novel swarm intelligence based
optimization method, to select appropriate wavelet coefficients from mass spectral data as a …
optimization method, to select appropriate wavelet coefficients from mass spectral data as a …
Bayesian approach to multivariate component-based logistic regression: analyzing correlated multivariate ordinal data
Applications of component-based models have gained much attention as a means of
accompanying dimension reduction in the regression setting and have been successfully …
accompanying dimension reduction in the regression setting and have been successfully …
[HTML][HTML] A Bayesian approach to identify genes and gene-level SNP aggregates in a genetic analysis of cancer data
Complex diseases, such as cancer, arise from complex etiologies consisting of multiple
single-nucleotide polymorphisms (SNPs), each contributing a small amount to the overall …
single-nucleotide polymorphisms (SNPs), each contributing a small amount to the overall …