Colorectal Cancer Incidence Prediction Model
16 Jun 2008 | Tools | Contributor(s): Eric Sherer, Mohd Rahmad
Stochastic simulation of polyp and colorectal cancer (CRC) incidence with patient age.
GCxGC-MS Data Classification and Alignment
02 Dec 2008 | Tools | Contributor(s): Dabao Zhang, Min Zhang, Jason Catlin
Align and classify GCxGC-MS data
Monte Carlo Simulation for four CRC Incidence Models
12 Dec 2008 | Tools | Contributor(s): Eric Sherer
A Monte Carlo Simulator determines the risk of colorectal cancer (CRC) development in a population by calculating multiple, individual patient trajectories.
27 Apr 2009 | Tools | Contributor(s): Nicholas Kisseberth
Spectrum Deconvolution of LC-MS Data
03 Dec 2008 | Tools | Contributor(s): Ann Christine Catlin, George Howlett
Spectral deconvolution differentiates analyte signals from contaminants or instrumental noise, and reduces data dimensionality to benefit downstream statistical analysis.
Peak Alignment of LC-MS Data
24 Jun 2009 | Tools | Contributor(s): Ann Christine Catlin, George Howlett
Peak alignment addresses retention time shift by recongnizing and aligning significant peaks; it then uses discrete deconvolutio to align overlapped peaks.
Normalization of Aligned LC-MS Data
18 Jun 2009 | Tools | Contributor(s): Ann Christine Catlin, George Howlett
Normalization attempts to quantitatively filter overall peak intensity variations due to experiment errors such as systematic variable injection volumes loaded onto LC-MS.
Significance Testing of Normalized LC-MS Data
23 Jun 2009 | Tools | Contributor(s): Ann Christine Catlin
Several statistical significance tests are employed to identify peptide or metabolite peaks that either make significant contributions to the molecular profile of a sample or distinguish a group of samples from others.
Pattern Recognition for Normalized LC-MS Data
01 Jul 2009 | Tools | Contributor(s): Ann Christine Catlin, George Howlett
This tool provides principal component analysis (PCA), linear discriminate analysis (LDA), and canonical discriminate analysis (CDA) for data clustering on aligned, normalized LC-MS datasets.
The MACH 1.0 Markov Chain based Haplotyper
15 Aug 2010 | Tools | Contributor(s): Yanzhu Lin, Guoheng Chen, Ann Christine Catlin
The MaCH Tool is used to infer missing genotypes in a two step process, where Step 1 estimates the model parameters, and Step 2 uses the parameters estimated in Step 1 -- calibrated to your specific dataset and genotyping platform -- to impute all SNPs.