scholarly journals The FAST-AIMS Clinical Mass Spectrometry Analysis System

2009 ◽  
Vol 2009 ◽  
pp. 1-4
Author(s):  
Nafeh Fananapazir ◽  
Alexander Statnikov ◽  
Constantin F. Aliferis
Keyword(s):  
Mass Spectrometry ◽  
Performance Estimation ◽  
Mass Spectrometry Data ◽  
Clinical Proteomics ◽  
Classification Model ◽  
Mass Spectrometry Analysis ◽  
Prognostic Models ◽  
Spectrometry Analysis ◽  
And Performance ◽  
Analysis System

Within clinical proteomics, mass spectrometry analysis of biological samples is emerging as an important high-throughput technology, capable of producing powerful diagnostic and prognostic models and identifying important disease biomarkers. As interest in this area grows, and the number of such proteomics datasets continues to increase, the need has developed for efficient, comprehensive, reproducible methods of mass spectrometry data analysis by both experts and nonexperts. We have designed and implemented a stand-alone software system, FAST-AIMS, which seeks to meet this need through automation of data preprocessing, feature selection, classification model generation, and performance estimation. FAST-AIMS is an efficient and user-friendly stand-alone software for predictive analysis of mass spectrometry data. The present resource review paper will describe the features and use of the FAST-AIMS system. The system is freely available for download for noncommercial use.

Utilities for Mass Spectrometry Analysis of Proteins (UMSAP): Fast post-processing of mass spectrometry data

2018 ◽  
Vol 32 (19) ◽  
pp. 1659-1667 ◽  
Author(s):  
Kenny Bravo-Rodriguez ◽  
Birte Hagemeier ◽  
Lea Drescher ◽  
Marian Lorenz ◽  
Juliana Rey ◽  
...  
Keyword(s):  
Mass Spectrometry ◽  
Mass Spectrometry Data ◽  
Mass Spectrometry Analysis ◽  
Post Processing ◽  
Spectrometry Analysis

Protein profile of the ovarian follicular fluid of brown brocket deer (Mazama gouazoubira; Fisher, 1814)

Zygote ◽  
2019 ◽  
Vol 28 (2) ◽  
pp. 170-173
Author(s):  
Thaís T.S. Souza ◽  
Maria J.B. Bezerra ◽  
Maurício F. van Tilburg ◽  
Celso S. Nagano ◽  
Luciana D. Rola ◽  
...  
Keyword(s):  
Mass Spectrometry ◽  
Follicular Fluid ◽  
Protein Profile ◽  
Mass Spectrometry Data ◽  
Mass Spectrometry Analysis ◽  
Spectrometry Analysis ◽  
Significant Difference ◽  
Mazama Gouazoubira ◽  
Ovarian Follicular Fluid ◽  
Brocket Deer

SummaryThe aim of this study was to characterize the protein profile of ovarian follicular fluid (FF) of brown brocket deer (Mazama gouazoubira). Five adult females received an ovarian stimulation treatment and the FF was collected by laparoscopy from small/medium (≤3.5 mm) and large (>3.5 mm) follicles. Concentrations of soluble proteins in FF samples were measured and proteins were analyzed by 1-D SDS-PAGE followed by tryptic digestion and tandem mass spectrometry. Data from protein list defined after a Mascot database search were analyzed using the STRAP software tool. For the protein concentration, no significant difference (P > 0.05) was observed between small/medium and large follicles: 49.2 ± 22.8 and 56.7 ± 27.4 μg/μl, respectively. Mass spectrometry analysis identified 13 major proteins, but with no significant difference (P > 0.05) between follicle size class. This study provides insight into elucidating folliculogenesis in brown brocket deer.

Bio-Inspired Metaheuristic Optimization Algorithms for Biomarker Identification in Mass Spectrometry Analysis

2012 ◽  
Vol 3 (2) ◽  
pp. 64-85 ◽  
Author(s):  
Syarifah Adilah Mohamed Yusoff ◽  
Ibrahim Venkat ◽  
Umi Kalsom Yusof ◽  
Rosni Abdullah
Keyword(s):  
Mass Spectrometry ◽  
Chemical Properties ◽  
Feature Subset Selection ◽  
Mass Spectrometry Data ◽  
Mass Spectrometry Analysis ◽  
Computational Techniques ◽  
Feature Subset ◽  
Protein Patterns ◽  
Spectrometry Analysis ◽  
Biomarker Analysis

Mass spectrometry is an emerging technique that is continuously gaining momentum among bioinformatics researchers who intend to study biological or chemical properties of complex structures such as protein sequences. This advancement also embarks in the discovery of proteomic biomarkers through accessible body fluids such as serum, saliva, and urine. Recently, literature reveals that sophisticated computational techniques mimetic survival and natural processes adapted from biological life for reasoning voluminous mass spectrometry data yields promising results. Such advanced approaches can provide efficient ways to mine mass spectrometry data in order to extract parsimonious features that represent vital information, specifically in discovering disease-related protein patterns in complex proteins sequences. This article intends to provide a systematic survey on bio-inspired approaches for feature subset selection via mass spectrometry data for biomarker analysis.

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