Data analysis techniques in phosphoproteomics
(2014) In Electrophoresis 35(24). p.62-3452- Abstract
The interpretation of phosphoproteomics data sets is crucial for generating hypotheses that guide therapeutic solutions, yet not many techniques have been applied to this type of analysis. This paper intends to give an overview about the two main standard techniques that can be applied to the analysis of these large scale data sets. These are data-driven or exploratory techniques based on a statistical model and topology-driven methods that analyze the signaling network from a dynamical standpoint. While employing different paradigms, these algorithms will detect unique "fingerprints" by revealing the intricate interactions at the proteome level and will support the experimental environment for novel therapeutics for many diseases.
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https://lup.lub.lu.se/record/d9de43b7-cc08-4687-aabb-006c77eee2dd
- author
- Meyer-Baese, Anke ; Wildberger, Joachim ; Meyer-Baese, Uwe and Nilsson, Carol L LU
- publishing date
- 2014-12
- type
- Contribution to journal
- publication status
- published
- keywords
- Cluster Analysis, Data Interpretation, Statistical, Least-Squares Analysis, Phosphopeptides, Phosphoproteins, Principal Component Analysis, Proteomics, Support Vector Machine, Journal Article, Research Support, Non-U.S. Gov't, Review
- in
- Electrophoresis
- volume
- 35
- issue
- 24
- pages
- 11 pages
- publisher
- John Wiley & Sons Inc.
- external identifiers
-
- pmid:25311575
- scopus:84916625072
- ISSN
- 0173-0835
- DOI
- 10.1002/elps.201400219
- language
- English
- LU publication?
- no
- id
- d9de43b7-cc08-4687-aabb-006c77eee2dd
- date added to LUP
- 2017-05-16 10:26:20
- date last changed
- 2024-01-13 20:57:07
@article{d9de43b7-cc08-4687-aabb-006c77eee2dd, abstract = {{<p>The interpretation of phosphoproteomics data sets is crucial for generating hypotheses that guide therapeutic solutions, yet not many techniques have been applied to this type of analysis. This paper intends to give an overview about the two main standard techniques that can be applied to the analysis of these large scale data sets. These are data-driven or exploratory techniques based on a statistical model and topology-driven methods that analyze the signaling network from a dynamical standpoint. While employing different paradigms, these algorithms will detect unique "fingerprints" by revealing the intricate interactions at the proteome level and will support the experimental environment for novel therapeutics for many diseases.</p>}}, author = {{Meyer-Baese, Anke and Wildberger, Joachim and Meyer-Baese, Uwe and Nilsson, Carol L}}, issn = {{0173-0835}}, keywords = {{Cluster Analysis; Data Interpretation, Statistical; Least-Squares Analysis; Phosphopeptides; Phosphoproteins; Principal Component Analysis; Proteomics; Support Vector Machine; Journal Article; Research Support, Non-U.S. Gov't; Review}}, language = {{eng}}, number = {{24}}, pages = {{62--3452}}, publisher = {{John Wiley & Sons Inc.}}, series = {{Electrophoresis}}, title = {{Data analysis techniques in phosphoproteomics}}, url = {{http://dx.doi.org/10.1002/elps.201400219}}, doi = {{10.1002/elps.201400219}}, volume = {{35}}, year = {{2014}}, }