Semi-supervised Protein Classification Using Cluster Kernels

By Jason Aaron Edward Weston, Christina S. Leslie, Dengyong Zhou, Andre´ Elisseeﬀ, William Stafford Noble. 2003

259 citations recorded by the source corpus.

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In Molecular Biology

Citations in the corpus

Evaluating Protein Transfer Learning with TAPE (2019, background): “Semi-supervised learning for protein problems has been explored for decades, with lots of work on kernel-based pretraining [28, 29].”
Machine Learning and Its Applications to Biology (2007, background): “In such cases, semi-supervised techniques can be applied to obtain a better classifier than could be obtained if only the labeled samples were used [5].”
Multiobjective Optimization in Bioinformatics and Computational Biology (2007, background): “Data sets with both labeled and unlabeled elements are frequently encountered in application domains where the categorization of individual data items is accompanied by high computational, analytical, or experimental costs, as is the case, e.g., in protein classification [ 76 ].”
Large Scale Transductive SVMs (2006, methodology): “Some of the main methods include (Chapelle et al., 2002; Chapelle and Zien, 2005; Sindhwani et al., 2005; Szummer and Jaakkola, 2001b); and (Weston et al., 2003).”
Mean Map Kernel Methods for Semisupervised Cloud Classification (2010, background): “2 Intuitively, this corresponds to smoothing K attending to the cluster structure in Kμ, a similar approach to that followed in [47] and [48].”
Neural methods for non-standard data (2004, methodology): “Further improvement of the efficiency and accuracy of the approaches can be obtained when using words instead of single symbols for document classification [14] or extending the methods to transduction tasks [ 103 ].”
Multiple sequence alignment. (2006, unknown): “Recently, protein classification methods have improved dramatically [36].”
A survey of kernel and spectral methods for clustering (2008, methodology): “The protein sequence clustering problem has been faced using spectral techniques in [61] and kernel methods in [84].”
Generalized Expectation Criteria for Semi-Supervised Learning with Weakly Labeled Data (2010, methodology): “Zhu and Lafferty (2005) propose alternative methods for reducing the time compleity to O(m3),m < n, but these may also impact performance.”
RolX: structural role extraction & mining in large graphs (2012, methodology): “Previous approaches include using cluster structure for predicting class labels on graphs [16], and using cluster kernels for semi-supervised classification [30].”
A brief survey on sequence classification (2010, methodology): “Weston et al. [60] propose a semi-supervised protein classi-ﬁcation method by using SVM with a cluster kernel.”