Abstract
In human mesenchymal stem cells the envelope surrounding the nucleus, as visualized by the nuclear lamina, has a round and flat shape. The lamina structure is considerably deformed after activation of cell death (apoptosis). The spatial organization of the lamina is the initial structural change found after activation of the apoptotic pathway, therefore can be used as a marker to identify cells activated for apoptosis. Here we investigated whether the spatial changes in lamina spatial organization can be recognized by machine learning algorithms to classify normal and apoptotic cells. Classical machine learning algorithms were applied to classification of 3D image sections of nuclear lamina proteins, taken from normal and apoptotic cells. We found that the Evolutionary-optimized Support Vector Machine (SVM) algorithm succeeded in the classification of normal and apoptotic cells in a highly satisfying result.
This is the first time that cells are classified based on lamina spatial organization using the machine learning approach. We suggest that this approach can be used for diagnostic applications to classify normal and apoptotic cells.
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Shir, O.M., Raz, V., Dirks, R.W., Bäck, T. (2007). Classification of Cell Fates with Support Vector Machine Learning. In: Marchiori, E., Moore, J.H., Rajapakse, J.C. (eds) Evolutionary Computation,Machine Learning and Data Mining in Bioinformatics. EvoBIO 2007. Lecture Notes in Computer Science, vol 4447. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-71783-6_25
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DOI: https://doi.org/10.1007/978-3-540-71783-6_25
Publisher Name: Springer, Berlin, Heidelberg
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