An Empirical Analysis of Domain Adaptation Algorithms for Genomic Sequence Analysis

By Gabriele Beate Schweikert, Christian Widmer, Bernhard Schölkopf, Gunnar Rätsch. 2008

183 citations recorded by the source corpus.

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Citations in the corpus

On causal and anticausal learning (2012, background): “…in the problem of predicting splicing patterns from genomic sequences, the basic splicing mechanism (driven by the ribosome) may be assumed evolutionarily stable and thus independent of the species (Schweikert et al., 2009), even though the genomic sequences and their statistical properties differ.”
A Comprehensive Survey on Transfer Learning (2019, methodology): “[173] uses the mRNA splice site prediction problem as the example to analyze the effectiveness of transfer learning approaches.”
Feature-based transfer learning with real-world applications (2010, background): “In Bioinformatics, motivated by that different biological entities, such as organisms, genes, etc, may be related to each other from a biological point of view, some research works have been proposed to apply transfer learning techniques to solve various computational biological problems, such as id”
Inferring latent task structure for Multitask Learning by Multiple Kernel Learning (2010, methodology): “There has been previous work using Domain Adaptation (closely related to Multitask Learning) in the context of splice site prediction [3].”
Multi-Domain Adversarial Learning (2019, methodology): “Bioinformatics is increasingly appreciating the need for domain adaptation methods (Borgwardt et al., 2006; Schweikert et al., 2008; Xu & Yang, 2011; Vallania et al., 2017).”
Hierarchical Multitask Structured Output Learning for Large-scale Sequence Segmentation (2011, background): “, [8, 16]), where γ is a hyper-parameter controlling the strength of coupling of the solutions for both tasks.”
Multi-domain learning by confidence-weighted parameter combination (2010, background): “In computational biology, mRNA splicing depends on the organism, and information learned from one organism could be adapted to a related organism (Schweikert et al. 2008).”
Multi-source domain adaptation and its application to early detection of fatigue (2011, background): “In addition, there is a conditional probability difference with the test data, hence the brute-force use of knowledge without evaluating similarities between the source and the target data degrades the performance of the classifier in the target domain [25] related to negative transfer [23].”
Transfer Learning for Molecular Cancer Classification Using Deep Neural Networks (2019, background): “[18] presented a domain transfer learning approach across various organisms for splice-site recognition.”
Invariant Models for Causal Transfer Learning (2015, methodology): “A ﬁrst family of methods assumes that covariate shift holds (e.g., Quionero-Candela et al., 2009; Schweikert et al., 2009).”
Transfer String Kernel for Cross-Context DNA-Protein Binding Prediction (2016, background): “For example, one previous study [10] explored “domain adaptation” strategies for a task of sequence-based prediction for acceptor splice sites.”
Using convolutional features and a sparse autoencoder for land-use scene classification (2016, methodology): “The approach is inspired from the idea of using pre-learned classifiers (trained from auxiliary data) to learn a target classifier (Yang, Yan, and Hauptmann 2007; Schweikert et al. 2008; Duan, Tsang, and Xu 2012; Duan et al. 2012; Duan, Xu, and Tsang 2012).”