A statistical framework for genomic data fusion

By Gert R. G. Lanckriet, Tijl De Bie, Nello Cristianini, Michael I. Jordan, William Stafford Noble. 2004

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In Molecular Biology

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Fast Learning from Distributed Datasets without Entity Matching (2016, background): “, 2003]; joining heterogeneous data helps prediction in genomics [Lanckriet et al., 2004; Yamanishi et al., 2004]; security agencies integrate various sources for terrorism intelligence [Sweeney, 2005; Christen, 2006; Sproull et al.”
Learning kernel logistic regression in the presence of class label noise (2014, background): “In contrast to the majority of MKL literature where the aim is centred around combining heterogeneous data sources [34,23], our adoption of MKL focuses on the combination of multiple kernels that correspond to different notions of similarity, as defined by different kernel widths.”
Optimization with Sparsity-Inducing Penalties (2011, background): “(1.25) Assuming that the kernels have equal trace, the two constraints of the previous program are avoided by considering convex combinations of kernels, which leads to a quadratically-constrained quadratic program (QCQP) [78]: min η∈Rp + G( ∑p i=1 ηiKi) s.t. ∑p i=1 ηi = 1.”
Network-based prediction of protein function (2007, methodology): “Lanckriet et al (2004) and later Tsuda et al (2005) represent each data type using a matrix of kernel similarity values.”
More efficiency in multiple kernel learning (2007, background): “Recent applications (Lanckriet et al., 2004a) and developments based on SVMs have shown that using multiple kernels instead of a single one can enhance interpretability of the decision function and improve classi.er performance.”
A Transfer Learning Approach and Selective Integration of Multiple Types of Assays for Biological Network Inference (2010, methodology): “It can easily be shown that the matrix G associated with the integrated kernel matrix t is expressed as a linear combination of the modified kernel k n +∈S k n +∈S int ma kern int n k inK trices: 1k= Hence, we can straightforwardly employ .kμ= ∑G G { } kern 1k nk = G as inputs of the SDP/SVM algorit”
Support Vector Machines and Kernels for Computational Biology (2008, background): “Rather than choose a single kernel, several papers have established that using a combination of multiple kernels can significantly boost classifier performance [47, 48, 49].”
Automatic classification of patients with Alzheimer's disease from structural MRI: A comparison of ten methods using the ADNI database (2011, background): “The problem of learning both the coefficients of the best convex linear combination of kernels and the optimal margin hyperplane (OMH) is known as the multiple kernel learning (MKL) problem (Lanckriet et al., 2004; Bach et al., 2004; Sonnenburg et al., 2006).”
Consistency of the group Lasso and multiple kernel learning (2007, positive): “When the Hilbert spaces are reproducing kernel Hilbert spaces (RKHS), this procedure turns out to be equivalent to learn the best convex combination of a set of basis positive deﬁnite kernels, where each kernel corresponds to one Hilbertian norm used for regularization (Bach et al., 2004a).”