Probabilistic multi-class multi-kernel learning: on protein fold recognition and remote homology detection

By T. Damoulas, Mark A. Girolami. 2008

174 citations recorded by the source corpus.

Subfields: Computational Theory and Mathematics, Molecular Biology

How researchers see this paper

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

In Computational Theory and Mathematics

1. Badri Adhikari
2. Jie Hou
3. Yiming Ying
4. Colin Campbell
5. Xiaolong Wang
6. Lukasz Kurgan
7. Junjie Chen
8. Leila Homaeian

In Molecular Biology

1. Jianlin Cheng
2. Jie Hou
3. Badri Adhikari
4. Lukasz Kurgan
5. Bin Liu
6. Junjie Chen
7. Ke Chen
8. Xiaolong Wang

Citations in the corpus

• DeepSF: deep convolutional neural network for mapping protein sequences to folds (2017, methodology): “In order to better use sequence features, kernel-based learning was designed to classify protein folds (Damoulas and Girolami, 2008).”
• A comprehensive review and comparison of different computational methods for protein remote homology detection (2018, methodology): “Damoulas and Girolami [87] merge multiple feature groups (physicochemical properties, Smith–Waterman scores) together and combine these available feature groups by using a multiclass kernel function.”
• iFC2: an integrated web-server for improved prediction of protein structural class, fold type, and secondary structure content (2010, methodology): “Numerous computational methods have been proposed for sequence-based prediction of the family and superfamily (Enright et al. 2002; Melvin et al. 2007; Gewehr et al. 2007), the fold type (Ding and Dubchak 2001; Hvidsten et al. 2003; Shen and Chou 2006; Jeong et al. 2006; Taguchi and Gromiha 2007; Me”
• Multiple Kernel Learning Algorithms (2011, unknown): “Damoulas and Girolami (2008, 2009a) apply these methods to different bioinformatics problems, such as protein fold recognition and remote homology problems, and improve the prediction performances for these tasks.”
• Rademacher Chaos Complexities for Learning the Kernel Problem (2010, background): “Such MKL formulations have been successfully demonstrated in combining multiple heterogeneous data sources to enhance biological inference (Lanckriet et al, 2004 ; Damoulas & Girolami, 2008 ; Ying et al, 2009 ).”