Supporting Regenerative Medicine by Integrative Dimensionality Reduction
Methods of Information in Medicine, ISSN: 0026-1270, Vol: 51, Issue: 4, Page: 341-347
2012
- 4Citations
- 9Captures
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Example: if you select the 1-year option for an article published in 2019 and a metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019. If you select the 3-year option for the same article published in 2019 and the metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019, 2018 and 2017.
Citation Benchmarking is provided by Scopus and SciVal and is different from the metrics context provided by PlumX Metrics.
Metrics Details
- Citations4
- Citation Indexes4
- CrossRef2
- Captures9
- Readers9
Article Description
Objective: The assessment of the developmental potential of stem cells is a crucial step towards their clinical application in regenerative medicine. It has been demonstrated that genome-wide expression profiles can predict the cellular differentiation stage by means of dimensionality reduction methods. Here we show that these techniques can be further strengthened to support decision making with i) a novel strategy for gene selection; ii) methods for combining the evidence from multiple data sets. Methods: We propose to exploit dimensionality reduction methods for the selection of genes specifically activated in different stages of differentiation. To obtain an integrated predictive model, the expression val - ues of the selected genes from multiple data sets are combined. We investigated distinct approaches that either aggregate data sets or use learning ensembles. Results: We analyzed the performance of the proposed methods on six publicly available data sets. The selection procedure identified a reduced subset of genes whose expression values gave rise to an accurate stage prediction. The assessment of predictive accuracy demonstrated a high quality of predictions for most of the data integration methods pre - sented. Conclusion: The experimental results highlighted the main potentials of proposed approaches. These include the ability to predict the true staging by combining multiple training data sets when this could not be inferred from a single data source, and to focus the analysis on a reduced list of genes of similar predictive performance. © Schattauer 2012.
Bibliographic Details
http://www.scopus.com/inward/record.url?partnerID=HzOxMe3b&scp=84864863010&origin=inward; http://dx.doi.org/10.3414/me11-02-0045; http://www.ncbi.nlm.nih.gov/pubmed/22773076; http://www.schattauer.de/index.php?id=1214&doi=10.3414/ME11-02-0045; http://www.schattauer.de/index.php?id=5236&mid=17845&L=1; http://www.thieme-connect.de/DOI/DOI?10.3414/ME11-02-0045; https://zenodo.org/record/3441074; https://dx.doi.org/10.3414/me11-02-0045; https://www.thieme-connect.de/products/ejournals/abstract/10.3414/ME11-02-0045
Schattauer GmbH
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