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Compound protein interactions are newly predicted using compound chemical structure similarities in the framework of supervised classification.
Based on the definitions of inner product and tensor norm, multi-view classification can be formulated as a global convex optimization problem in the framework of supervised tensor learning [15].
It also makes sense in the framework of supervised learning (see Sect. 5.1).
The other statistical approach is the distance learning in the framework of supervised bipartite graph inference (Yamanishi, 2009; Yamanishi et al., 2008).
The proposed method consists of two steps: (i) prediction of pharmacological effects from chemical structures of given compounds and (ii) inference of unknown drug target interactions based on the pharmacological effect similarity in the framework of supervised bipartite graph inference.
The originality of the proposed method lies in prediction of pharmacological effects from chemical structures of given compounds, and its use for identification of unknown drug target interactions in the framework of supervised bipartite graph inference.
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Within the framework of supervised learning, this problem is equivalent to a feature selection problem.
Our task formulation is closely related to the framework of supervised classification of protein pairs through information integration.
We use semi-supervised spectral clustering (SSC) as the base clustering, hybrid bipartite graph formulation (HBGF) as the consensus function, and spectral clustering (SC) as final clustering in the framework of consensus clustering in SSCC.
The course focuses on the problem of supervised learning within the framework of Statistical Learning Theory.
This work was performed within the framework of PU's 6 month period of civil service in the division of pediatric infectious diseases and vaccinology, University Children's Hospital Basel, supervised by UH.
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