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The purpose of this paper is to provide a concise review of (1) the most recent methods that streamline the collection and analysis of the visual data captured from these platforms, and (2) methods that transform and visualize actionable performance information from the collected data, with and without using Building Information Models (BIM) as a priori information.
Methods based on centroid correlations (e.g. subtype signatures, 70-gene and WR) and methods that transform the data into an invariant scale before computing the risk scores (e.g. GGI) have more consistent performances across different studies.
Methods that transform the data into a common form, such as kernel matrices or Bayesian posterior probabilities, are often used (Hwang et al., 2005; Jarman et al., 2008; Lanckriet et al., 2004; Troyanskaya et al., 2003; Webb-Robertson et al., 2009), since after applying such methods, fusion is simply a matter of merging matrices in a statistical manner.
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