Kernel Methods in Computational Biology

By Bernhard Schölkopf, Koji Tsuda, Jean-Philippe Vert. 2005

998 citations recorded by the source corpus.

Subfields: Molecular Biology, Computational Biology & Bioinformatics

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Citations in the corpus

Discriminative Embeddings of Latent Variable Models for Structured Data (2016, methodology): “Structured data, such as sequences, trees and graphs, are prevalent in a number of interdisciplinary areas such as protein design, genomic sequence analysis, and drug design (Schölkopf et al., 2004).”
Simulation-assisted machine learning (2018, methodology): “It is well known in ML that good similarity measures, which come from expert domain knowledge, result in better ML performance (Schölkopf et al., 2004).”
Filtering with State-Observation Examples via Kernel Monte Carlo Filter (2013, background): “Examples include kernels for images in computer vision (Lazebnik, Schmid, & Ponce, 2006), graph-structured data in bioinformatics (Schölkopf et al., 2004), and genomic sequences (Schaid, 2010a, 2010b).”
Sharp analysis of low-rank kernel matrix approximations (2012, neutral): “tion Kernel methods, such as the support vector machine or kernel ridge regression, are now widely used in many areas of science and engineering, such as computer vision or bioinformatics (see, e.g., [1, 2, 3, 4]). Their main attractive features are that (1) they allow non-linear predictions through”
Kernel Belief Propagation (2011, methodology): “An expression for the conditional distribution embedding was proposed by Song et al. (2009). We describe this expression by analogy with the conditioning operation for a Gaussian random vector z ∼ N (0, C), where we partition z = (z> 1 , z > 2 ) > such that z1 ∈ R and z2 ∈ R ′ .”
A Kernel Method for the Two-Sample-Problem (2006, background): “We may further generalize to kernels on structured objects such as strings and graphs (Schölkopf et al., 2004): see also our experiments in Section 8.”
Kernel methods in machine learning (2007, positive): “• A combination of LS and LAD loss yields a penalized version of Huber’s robust regression (Huber [71] and Smola and Schölkopf [126]).”
Hilbert Space Embeddings of Hidden Markov Models (2010, background): “Examples of kernels include the Gaussian RBF kernel k(x, x′) = exp(−s ‖x− x′‖(2)), however kernel functions have also been defined on strings, graphs, and other structured objects (Schölkopf et al., 2004).”
A Kernel Statistical Test of Independence (2007, positive): “Our setting allows for more general kernels, however, such as kernels on strings (as in our experiments in Section 4) and graphs (see [20] for further details of kernels on structures): this is not possible under the characteristic function framework, which is restricted to Euclidean spaces ( R in t”
Learning Nonsparse Kernels by Self-Organizing Maps for Structured Data (2009, background): “For example, the definition of kernels for structured domains (see [10] for an overview, and [11] for applications”
A discriminative framework for clustering via similarity functions (2008, background): “We then analyze a much weaker average-attraction property in Section 4 that has close connections to large margin properties studied in Learning Theory [6, 7, 21, 31, 29].”
Matching Node Embeddings for Graph Similarity (2017, methodology): “Graph kernels have been successfully used for comparing graphs in a variety of fields such as Computational Biology (Schölkopf, Tsuda, and Vert 2004), Chemistry (Mahé and Vert 2009), Information Retrieval (Hermansson et al. 2013) and Medicine (Feragen et al. 2013).”
Partially supervised feature selection with regularized linear models (2009, background): “In some cases feature selection has also been shown to improve classi.cation accuracy (Krishnapuram et al., 2004).”