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As explained in the Introduction, manifold embedding algorithms can be easily adopted for network embedding, although not all algorithms that learn manifolds are applicable for this task: only those able to embed a topology starting from a distance or adjacency matrix can be used.
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This coarse discretization makes these methods less applicable for the task of building cell-specific models, in cases where a high similarity in transcriptomics levels of different samples is observed.
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However, in many applications the resulting grasps should not only be stable but also applicable for a particular task.
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