Support Vector Machines and Kernels for Computational Biology

By Asa Ben-Hur, Cheng Soon Ong, Sören Sonnenburg, Bernhard Schölkopf, Gunnar Rätsch. PLoS Comput. Biol., 2008

771 citations recorded by the source corpus.

The increasing wealth of biological data coming from a large variety of platforms and the continued development of new high-throughput methods for probing biological systems require increasingly more sophisticated computational approaches. Putting all these data in simple-to-use databases is a first step; but realizing the full potential of the data requires algorithms that automatically extract regularities from the data, which can then lead to biological insight. Many of the problems in computational biology are in the form of prediction: starting from prediction of a gene's structure, prediction of its function, interactions, and role in disease. Support vector machines (SVMs) and related kernel methods are extremely good at solving such problems [1]–[3]. SVMs are widely used in computational biology due to their high accuracy, their ability to deal with high-dimensional and large datasets, and their flexibility in modeling diverse sources of data [2], [4]–[6]. The simplest form of a prediction problem is binary classification: trying to discriminate between objects that belong to one of two categories—positive (+1) or negative (−1). SVMs use two key concepts to solve this problem: large margin separation and kernel functions. The idea of large margin separation can be motivated by classification of points in two dimensions (see Figure 1). A simple way to classify the points is to draw a straight line and call points lying on one side positive and on the other side negative. If the two sets are well separated, one would intuitively draw the separating line such that it is as far as possible away from the points in both sets (see Figures 2 and ​and3).3). This intuitive choice captures the idea of large margin separation, which is mathematically formulated in the section Classification with Large Margin. Open in a separate window Figure 1 A linear classifier separating two classes of points (squares and circles) in two dimensions. The decision boundary divides the space into two sets depending on the sign of f(x) = 〈w,x〉+b. The grayscale level represents the value of the discriminant function f(x): dark for low values and a light shade for high values.

Subfields: Molecular Biology

View Paper: https://www.semanticscholar.org/paper/2bbf2ab3a524399c0daff3bc938d466f05501ae0

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In Molecular Biology

1. K. Müller
2. Marius Kloft
3. Nico Görnitz
4. Marina M.-C. Vidovic
5. Gunnar Rätsch
6. P. Dupont
7. Thibault Helleputte
8. Y. Van de Peer

Citations in the corpus

• An Introduction to Machine Learning (2020, methodology): “47 Concretely in a more recent example, SVR was used for delineating cell compositions from bulk transcriptomics data.”
• Machine learning in cell biology – teaching computers to recognize phenotypes (2013, background): “…(Perlman et al., 2004; Slack et al., 2008; Loo et al., 2009; Castoreno et al., 2010; Murphy, 2011), DNA sequence analysis (Castelo and Guigo´, 2004; Ben-Hur et al., 2008) and proteomics (Yang and Chou, 2004; Datta and Pihur, 2010; Reiter et al., 2011), as well as in many other fields outside of…”
• Support vector machines in DSC‐based glioma imaging: Suggestions for optimal characterization (2010, background): “However, the choice of kernel function, a key concept of SVMs, influences prediction outcome and inappropriate kernels might result in poor performance (19,20).”
• Leveraging Sequence Classification by Taxonomy-Based Multitask Learning (2010, methodology): “3 It has been shown in previous work that SVMs using string kernels such as the Spectrum [6] or the Weighted Degree Kernel (WDK) [7], is well suited for nucleic and protein sequence analysis [1].”
• Mathematics for Machine Learning (2020, neutral): “, sets, sequences, strings, graphs, and distributions (Ben-Hur et al., 2008; Gärtner, 2008; Shi et al., 2009; Sriperumbudur et al., 2010; Vishwanathan et al., 2010).”
• Biological Sequence Modeling with Convolutional Kernel Networks (2017, neutral): “A large number of kernels have also been specifically designed for biological sequences (see [6] and references therein).”
• The SHOGUN Machine Learning Toolbox (2010, background): “Furthermore, linear SVMs can be efficiently trained using on the fly computedfeature spaces, even mixing sparse, dense and other data types.”