Engineering Support Vector Machine Kerneis That Recognize Translation Initialion Sites

By Alexander Zien, Gunnar Rätsch, Sebastian Mika, Bernhard Schölkopf, Christian Lemmen, Alex Smola, Thomas Lengauer, K. Müller. German Conference on Bioinformatics, 2000

477 citations recorded by the source corpus.

Subfields: Molecular Biology

View Paper: https://www.semanticscholar.org/paper/bdb4a67b8e83de538965181f27bc070e68ced84c

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Molecular Biology

Citations in the corpus

Support Vector Machines and Kernels for Computational Biology (2008, background): “For instance, the polynomial kernel of degree d combined with the , -spectrum kernel, i.e., can model up to d co-occurrences of , - mers (similarly proposed in [32]).”
Insights from Classifying Visual Concepts with Multiple Kernel Learning (2011, methodology): “We note that [7] and [8] were the ﬁrst to exploit prior and domain knowledge for the kernel construction.”
Understanding Machine-learned Density Functionals (2014, background): “[51] Alexander Zien, Gunnar Rätsch, Sebastian Mika, Bernhard Schölkopf, Thomas Lengauer, and K-R Müller, “Engineering support vector machine kernels that recognize translation initiation sites,” Bioinformatics 16, 799– 807 (2000).”
Secondary structure prediction with support vector machines (2003, background): “SVMs have been successfully * To whom correspondence should be addressed. applied to recognition of protein translation–initiation sites in DNA sequences (Zien et al., 2000) and functional annotation of genes from expression profiles (Brown et al., 2000).”
Classification of real and pseudo microRNA precursors using local structure-sequence features and support vector machine (2005, methodology): “SVM has been widely applied to the prediction and classification of important biology signals such as promoters [16], translation initiation sites [17], splicing sites [18] and proteins [19].”
Support vector machines: hype or hallelujah? (2000, methodology): “SVMs have been successfully applied to a number of applications ranging from particle identification [1], face detection [40] and text categorization [17][19][29][31] to engine knock detection [46], bioinformatics [7][24][28][ 71 ][38] and database marketing [5].”
Nonlinear gradient denoising: Finding accurate extrema from inaccurate functional derivatives (2015, background): “Many forms of kernels have been tried and the choice is often designed to work with speciﬁc types of data [22].”
Real time facial expression recognition in video using support vector machines (2003, methodology): “SVMs have been successfully employed for a number of classification tasks such as text categorization [14], genetic analysis [28] and face detection [19].”
Comparison of machine learning and traditional classifiers in glaucoma diagnosis (2002, methodology): “It has also been applied to data from gene expression [30], DNA and protein analysis [31], [32].”
Support vector machine classification and validation of cancer tissue samples using microarray expression data (2000, background): “…machine learning technique, have been shown to perform well in multiple areas of biological analysis including evaluating microarray expression data (Brown et al., 2000), detecting remote protein homologies (Jaakkola et al., 1999), and recognizing translation initiation sites (Zien et al., 2000).”