scholarly journals Enhancing Top-Down Proteomics Data Analysis by Combining Deconvolution Results through a Machine Learning Strategy

2020 ◽  
Vol 31 (5) ◽  
pp. 1104-1113 ◽  
Author(s):  
Sean J. McIlwain ◽  
Zhijie Wu ◽  
Molly Wetzel ◽  
Daniel Belongia ◽  
Yutong Jin ◽  
...  
Keyword(s):  
Machine Learning ◽  
Data Analysis ◽  
Learning Strategy ◽  
Proteomics Data ◽  
Top Down ◽  
Proteomics Data Analysis

scholarly journals Using the entrapment sequence method as a standard to evaluate key steps of proteomics data analysis process

BMC Genomics ◽  
2017 ◽  
Vol 18 (S2) ◽  
Author(s):  
Xiao-dong Feng ◽  
Li-wei Li ◽  
Jian-hong Zhang ◽  
Yun-ping Zhu ◽  
Cheng Chang ◽  
...  
Keyword(s):  
Data Analysis ◽  
Proteomics Data ◽  
Analysis Process ◽  
Sequence Method ◽  
Key Steps ◽  
Proteomics Data Analysis

scholarly journals PeptideMind – applying machine learning algorithms to assess replicate quality in shotgun proteomic data

2020 ◽  
Author(s):  
D.C.L. Handler ◽  
P.A. Haynes
Keyword(s):  
Machine Learning ◽  
Learning Algorithms ◽  
Shotgun Proteomics ◽  
Machine Learning Algorithms ◽  
Proteomics Data ◽  
Proteomic Data ◽  
Proteomics Experiment ◽  
Significance Measures ◽  
Proteomics Data Analysis

AbstractAssessment of replicate quality is an important process for any shotgun proteomics experiment. One fundamental question in proteomics data analysis is whether any specific replicates in a set of analyses are biasing the downstream comparative quantitation. In this paper, we present an experimental method to address such a concern. PeptideMind uses a series of clustering Machine Learning algorithms to assess outliers when comparing proteomics data from two states with six replicates each. The program is a JVM native application written in the Kotlin language with Python sub-process calls to scikit-learn. By permuting the six data replicates provided into four hundred triplet non redundant pairwise comparisons, PeptideMind determines if any one replicate is biasing the downstream quantitation of the states. In addition, PeptideMind generates useful visual representations of the spread of the significance measures, allowing researchers a rapid, effective way to monitor the quality of those identified proteins found to be differentially expressed between sample states.

scholarly journals Philosopher: a versatile toolkit for shotgun proteomics data analysis

2020 ◽  
Vol 17 (9) ◽  
pp. 869-870 ◽  
Author(s):  
Felipe da Veiga Leprevost ◽  
Sarah E. Haynes ◽  
Dmitry M. Avtonomov ◽  
Hui-Yin Chang ◽  
Avinash K. Shanmugam ◽  
...  
Keyword(s):  
Data Analysis ◽  
Shotgun Proteomics ◽  
Proteomics Data ◽  
Proteomics Data Analysis
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