Biomarker Discovery and Redundancy Reduction towards Classification using a Multi-factorial MALDI-TOF MS T2DM Mouse Model Dataset
<p>Abstract</p> <p>Background</p> <p>Diabetes like many diseases and biological processes is not mono-causal. On the one hand multi-factorial studies with complex experimental design are required for its comprehensive analysis. On the other hand, the data from these stu...
| Main Authors: | , , , , , , , , , , , |
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| Format: | Article |
| Language: | English |
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BMC
2011-05-01
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| Series: | BMC Bioinformatics |
| Online Access: | http://www.biomedcentral.com/1471-2105/12/140 |
| _version_ | 1811294612077150208 |
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| author | Al-Hasani Hadi Beule Dieter Dreja Tanja Chadt Alexandra Tiss Ali Towers Mark W Smith Celia J Kleinjung Frank Bauer Chris Reinert Knut Schuchhardt Johannes Cramer Rainer |
| author_facet | Al-Hasani Hadi Beule Dieter Dreja Tanja Chadt Alexandra Tiss Ali Towers Mark W Smith Celia J Kleinjung Frank Bauer Chris Reinert Knut Schuchhardt Johannes Cramer Rainer |
| author_sort | Al-Hasani Hadi |
| collection | DOAJ |
| description | <p>Abstract</p> <p>Background</p> <p>Diabetes like many diseases and biological processes is not mono-causal. On the one hand multi-factorial studies with complex experimental design are required for its comprehensive analysis. On the other hand, the data from these studies often include a substantial amount of redundancy such as proteins that are typically represented by a multitude of peptides. Coping simultaneously with both complexities (experimental and technological) makes data analysis a challenge for Bioinformatics.</p> <p>Results</p> <p>We present a comprehensive work-flow tailored for analyzing complex data including data from multi-factorial studies. The developed approach aims at revealing effects caused by a distinct combination of experimental factors, in our case genotype and diet. Applying the developed work-flow to the analysis of an established polygenic mouse model for diet-induced type 2 diabetes, we found peptides with significant fold changes exclusively for the combination of a particular strain and diet. Exploitation of redundancy enables the visualization of peptide correlation and provides a natural way of feature selection for classification and prediction. Classification based on the features selected using our approach performs similar to classifications based on more complex feature selection methods.</p> <p>Conclusions</p> <p>The combination of ANOVA and redundancy exploitation allows for identification of biomarker candidates in multi-dimensional MALDI-TOF MS profiling studies with complex experimental design. With respect to feature selection our method provides a fast and intuitive alternative to global optimization strategies with comparable performance. The method is implemented in R and the scripts are available by contacting the corresponding author.</p> |
| first_indexed | 2024-04-13T05:20:01Z |
| format | Article |
| id | doaj.art-39027ace414d41d7a498a3012cec2f1a |
| institution | Directory Open Access Journal |
| issn | 1471-2105 |
| language | English |
| last_indexed | 2024-04-13T05:20:01Z |
| publishDate | 2011-05-01 |
| publisher | BMC |
| record_format | Article |
| series | BMC Bioinformatics |
| spelling | doaj.art-39027ace414d41d7a498a3012cec2f1a2022-12-22T03:00:47ZengBMCBMC Bioinformatics1471-21052011-05-0112114010.1186/1471-2105-12-140Biomarker Discovery and Redundancy Reduction towards Classification using a Multi-factorial MALDI-TOF MS T2DM Mouse Model DatasetAl-Hasani HadiBeule DieterDreja TanjaChadt AlexandraTiss AliTowers Mark WSmith Celia JKleinjung FrankBauer ChrisReinert KnutSchuchhardt JohannesCramer Rainer<p>Abstract</p> <p>Background</p> <p>Diabetes like many diseases and biological processes is not mono-causal. On the one hand multi-factorial studies with complex experimental design are required for its comprehensive analysis. On the other hand, the data from these studies often include a substantial amount of redundancy such as proteins that are typically represented by a multitude of peptides. Coping simultaneously with both complexities (experimental and technological) makes data analysis a challenge for Bioinformatics.</p> <p>Results</p> <p>We present a comprehensive work-flow tailored for analyzing complex data including data from multi-factorial studies. The developed approach aims at revealing effects caused by a distinct combination of experimental factors, in our case genotype and diet. Applying the developed work-flow to the analysis of an established polygenic mouse model for diet-induced type 2 diabetes, we found peptides with significant fold changes exclusively for the combination of a particular strain and diet. Exploitation of redundancy enables the visualization of peptide correlation and provides a natural way of feature selection for classification and prediction. Classification based on the features selected using our approach performs similar to classifications based on more complex feature selection methods.</p> <p>Conclusions</p> <p>The combination of ANOVA and redundancy exploitation allows for identification of biomarker candidates in multi-dimensional MALDI-TOF MS profiling studies with complex experimental design. With respect to feature selection our method provides a fast and intuitive alternative to global optimization strategies with comparable performance. The method is implemented in R and the scripts are available by contacting the corresponding author.</p>http://www.biomedcentral.com/1471-2105/12/140 |
| spellingShingle | Al-Hasani Hadi Beule Dieter Dreja Tanja Chadt Alexandra Tiss Ali Towers Mark W Smith Celia J Kleinjung Frank Bauer Chris Reinert Knut Schuchhardt Johannes Cramer Rainer Biomarker Discovery and Redundancy Reduction towards Classification using a Multi-factorial MALDI-TOF MS T2DM Mouse Model Dataset BMC Bioinformatics |
| title | Biomarker Discovery and Redundancy Reduction towards Classification using a Multi-factorial MALDI-TOF MS T2DM Mouse Model Dataset |
| title_full | Biomarker Discovery and Redundancy Reduction towards Classification using a Multi-factorial MALDI-TOF MS T2DM Mouse Model Dataset |
| title_fullStr | Biomarker Discovery and Redundancy Reduction towards Classification using a Multi-factorial MALDI-TOF MS T2DM Mouse Model Dataset |
| title_full_unstemmed | Biomarker Discovery and Redundancy Reduction towards Classification using a Multi-factorial MALDI-TOF MS T2DM Mouse Model Dataset |
| title_short | Biomarker Discovery and Redundancy Reduction towards Classification using a Multi-factorial MALDI-TOF MS T2DM Mouse Model Dataset |
| title_sort | biomarker discovery and redundancy reduction towards classification using a multi factorial maldi tof ms t2dm mouse model dataset |
| url | http://www.biomedcentral.com/1471-2105/12/140 |
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