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Andy's Brain Blog: AFNI Bootcamp: Day 2
Andy's Brain Blog: AFNI Bootcamp: Day 2
Today was a walkthrough of the AFNI interface, and a tutorial for how to view timeseries data and model fits. Daniel started off with a sh...
·andysbrainblog.blogspot.com·
Andy's Brain Blog: AFNI Bootcamp: Day 2
Andy's Brain Blog: AFNI Tutorial: 3dTcat
Andy's Brain Blog: AFNI Tutorial: 3dTcat
AFNI's 3dTcat is used to concatenate datasets. For example, after performing first- and second-level analyses, you may want to join several ...
·andysbrainblog.blogspot.com·
Andy's Brain Blog: AFNI Tutorial: 3dTcat
Andy's Brain Blog: Overview of afni_proc.py
Andy's Brain Blog: Overview of afni_proc.py
For those of you without access to a Unix-based platform, or whether you are just having a difficult time correctly installing your Python l...
·andysbrainblog.blogspot.com·
Andy's Brain Blog: Overview of afni_proc.py
Andy's Brain Blog: SUMA Demo
Andy's Brain Blog: SUMA Demo
I've posted a demo of AFNI's surface mapper program, SUMA, over here on my screencast account. Specifically, I talk about how to map volume...
·andysbrainblog.blogspot.com·
Andy's Brain Blog: SUMA Demo
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·google.com·
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A comparison of three algorithms for chromatograms alignment - PubMed
A comparison of three algorithms for chromatograms alignment - PubMed
In this paper the performance of three alignment algorithms, correlation optimized warping, parametric time warping and semi-parametric time warping, is compared on real chromatograms. Among these, parametric time warping is the simplest and fastest; generally less than 1s is required to align two c …
·pubmed.ncbi.nlm.nih.gov·
A comparison of three algorithms for chromatograms alignment - PubMed
A new method for alignment of LC-MALDI-TOF data | Proteome Science | Full Text
A new method for alignment of LC-MALDI-TOF data | Proteome Science | Full Text
Background In proteomics studies, liquid chromatography coupled to mass spectrometry (LC-MS) has proven to be a powerful technology to investigate differential expression of proteins/peptides that are characterized by their peak intensities, mass-to-charge ratio (m/z), and retention time (RT). The variable complexity of peptide mixtures and occasional drifts lead to substantial variations in m/z and RT dimensions. Thus, label-free differential protein expression studies by LC-MS technology require alignment with respect to both RT and m/z to ensure that same proteins/peptides are compared from multiple runs. Methods In this study, we propose a new strategy to align LC-MALDI-TOF data by combining quality threshold cluster analysis and support vector regression. Our method performs alignment on the basis of measurements in three dimensions (RT, m/z, intensity). Results and conclusions We demonstrate the suitability of our proposed method for alignment of LC-MALDI-TOF data through a previously published spike-in dataset and a new in-house generated spike-in dataset. A comparison of our method with other methods that utilize only RT and m/z dimensions reveals that the use of intensity measurements enhances alignment performance.
·proteomesci.biomedcentral.com·
A new method for alignment of LC-MALDI-TOF data | Proteome Science | Full Text
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·google.com·
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