Novel Machine Learning Methods for MHC Class I Binding Prediction

By Christian Widmer, Nora C. Toussaint, Yasemin Altun, Oliver Kohlbacher, Gunnar Rätsch. 2010

39 citations recorded by the source corpus.

Subfields: Immunology, Molecular Biology, Radiology, Nuclear Medicine and Imaging

How researchers see this paper

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

In Immunology

1. Qian Xu
2. Qiang Yang
3. Wouter M. Kouw

In Molecular Biology

1. Qiang Yang
2. Wouter M. Kouw
3. Qian Xu
4. Marco Loog
5. Yu Zhang

In Radiology, Nuclear Medicine and Imaging

1. Marco Loog
2. Qian Xu
3. Qiang Yang
4. Wouter M. Kouw

Citations in the corpus

• An introduction to domain adaptation and transfer learning (2018, background): “Other examples of domain adaptation and transfer learning in fields that employ machine learning include: in bioinformatics, adaptive approaches have been successful in sequence classification [205,149], gene expression analysis [38,210], and biological network reconstruction [153,118].”
• A Survey of Transfer and Multitask Learning in Bioinformatics (2011, methodology): “[34] in 2010 aimed to improve the predictive power of the multitask kernel method for MHC class I binding prediction by developing an advanced kernel based on Jacob and Vert’s orginal.”
• A Review of Domain Adaptation without Target Labels (2019, methodology): “In bioinformatics, adaptive approaches have been successful in sequence classification [58], [59] and biological network reconstruction [60].”