Support vector machine applications in computational biology

By William Noble. 2004

297 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/e3d79e5498cf5eff61b11db70c3e67c8f1ac6168

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In Molecular Biology

Citations in the corpus

Length-dependent prediction of protein intrinsic disorder (2006, background): “Due to its ability to handle complex, high-dimensional and noisy data, SVM has been widely used in various computational biology problems (for a review, please refer to [57]).”
Learning sparse models for a dynamic Bayesian network classifier of protein secondary structure (2011, methodology): “SVMs have been applied widely in bioinformatics [17].”
A review on machine learning principles for multi-view biological data integration (2016, background): “In accordance with when multi-view data are incorporated into a learning process, data fusion techniques can be classified as early, intermediate or late integration methods [20].”
Methodologies for Cross-Domain Data Fusion: An Overview (2015, methodology): “Noble [48] calls this method of combining kernels intermediate Fig.”
A statistical framework for genomic data fusion (2004, methodology): “The approach relies on the use of kernel-based statistical learning methods that have already proven to be very useful tools in bioinformatics (Noble, 2004).”
Hypergraph spectral learning for multi-label classification (2008, methodology): “We plan to extend the hypergraph spectral learning formulation to the kernelinduced feature space, and apply the kernel formulation to multi-label applications such as protein function prediction [17] where only kernels are available.”
Ten quick tips for machine learning in computational biology (2017, background): “Even though stating the level of simplicity of a machine learning method is not an easy task, we consider k-means and k-NN simple algorithms because they are easier to understand and to interpret than other models, such as artificial neural networks [27] or support vector machines [19].”
Kernel-Based Data Fusion and Its Application to Protein Function Prediction in Yeast (2003, methodology): “The approach relies on the use of kernel-based statistical learning methods that have already proven to be very useful tools in bioinformatics.(1) These methods represent the data by means of a kernel function, which denes similarities between pairs of genes, proteins, etc.”
Ranking predicted protein structures with support vector regression (2007, methodology): “SVMs have been used successfully in numerous bioinformatics applications,24 including remote protein homology detection,25 protein function classification,26 protein interaction prediction,27 protein secondary structure prediction,28 and microarray data analysis.”
Improvements on Twin Support Vector Machines (2011, background): “Support vector machines (SVMs), being computationally powerful tools for supervised learning [1]–[3], have already outperformed most other systems in a wide variety of applications [4]–[6].”
Kernel methods for predicting protein-protein interactions (2005, background): “Kernel methods, and in particular support vector machines (SVMs) (Schölkopf and Smola, 2002), have proven useful in many difficult classification problems in bioinformatics (Noble, 2004).”