Abstract
Genome-scale transcription profile is known to be a good reporter of the state of the cell. Much of the early predictive modelling and cell-type clustering relied on this relation and has experimentally confirmed it. We have examined if this also holds for prediction of cell’s staging, and focused on the inference of stage prediction models for stem cell development. We show that the problem relates to rank learning and, from the user’s point of view, to projection of transcription profile data to a single dimension. Our comparison of several state-of-the-art algorithms on 10 data sets from Gene Expression Omnibus shows that rank-learning can be successfully applied to developmental cell staging, and that relatively simple techniques can perform surprisingly well.
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References
Bolstad, B.M., Irizarry, R.A., Åstrand, M., Speed, T.P.: A comparison of normalization methods for high density oligonucleotide array data based on variance and bias. Bioinformatics 19(2), 185–193 (2003)
Demšar, J.: Statistical comparisons of classifiers over multiple data sets. The Journal of Machine Learning Research 7, 1–30 (2006)
Fürnkranz, J., Hüllermeier, E. (eds.): Preference Learning. Springer, Heidelberg (2010)
Gutkin, M., Shamir, R., Dror, G.: SlimPLS: a method for feature selection in gene expression-based disease classification. PLoS One 4(7), e6416 (2009)
Joachims, T.: Optimizing search engines using clickthrough data. In: Proceedings of the Eighth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD 2002, pp. 133–142 (2002)
Liu, T.Y.: Learning to Rank for Information Retrieval. Now Publishers (2009)
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Zagar, L., Mulas, F., Bellazzi, R., Zupan, B. (2011). Ranking and 1-Dimensional Projection of Cell Development Transcription Profiles. In: Peleg, M., Lavrač, N., Combi, C. (eds) Artificial Intelligence in Medicine. AIME 2011. Lecture Notes in Computer Science(), vol 6747. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-22218-4_11
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DOI: https://doi.org/10.1007/978-3-642-22218-4_11
Publisher Name: Springer, Berlin, Heidelberg
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