Diet and dioxins: the need to cut back.

By Tony Håndstad, Arne J. H. Hestnes, Pål Sætrom. 2004

54 citations recorded by the source corpus.

BackgroundProtein remote homology detection is a central problem in computational biology. Most recent methods train support vector machines to discriminate between related and unrelated sequences and these studies have introduced several types of kernels. One successful approach is to base a kernel on shared occurrences of discrete sequence motifs. Still, many protein sequences fail to be classified correctly for a lack of a suitable set of motifs for these sequences.ResultsWe introduce the GPkernel, which is a motif kernel based on discrete sequence motifs where the motifs are evolved using genetic programming. All proteins can be grouped according to evolutionary relations and structure, and the method uses this inherent structure to create groups of motifs that discriminate between different families of evolutionary origin. When tested on two SCOP benchmarks, the superfamily and fold recognition problems, the GPkernel gives significantly better results compared to related methods of remote homology detection.ConclusionThe GPkernel gives particularly good results on the more difficult fold recognition problem compared to the other methods. This is mainly because the method creates motif sets that describe similarities among subgroups of both the related and unrelated proteins. This rich set of motifs give a better description of the similarities and differences between different folds than do previous motif-based methods.

Subfields: Molecular Biology

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In Molecular Biology

1. Shumin Li
2. Bin Liu
3. Junjie Chen
4. Xiaolong Wang
5. Manuele Bicego
6. Marco Cristani
7. Pietro Lovato
8. Mingyue Guo

Citations in the corpus

• Protein remote homology detection based on bidirectional long short-term memory (2017, methodology): “Several kernels have been proposed to calculate the similarity between protein samples, such as mismatch kernel [27], motif kernel [28], LA kernel [29], SW-PSSM [30], SVM-Pairwise [31], etc.”
• Soft Ngram Representation and Modeling for Protein Remote Homology Detection (2017, background): “67 [24], where homologous sequences are taken at a superfamily level rather than at a family level—making this dataset considerably harder than the SCOP 1.”
• A Kernel Perspective for Regularizing Deep Neural Networks (2018, unknown): “Our protein homology detection experiments consider the Structural Classification Of Proteins (SCOP) version 1.67 dataset ( Murzin et al., 1995), filtered and split following the procedures of (Håndstad et al., 2007).”
• A comprehensive review and comparison of different computational methods for protein remote homology detection (2018, background): “structure and function information of proteins into the predictors, the motif kernels [74] based on the occurrences of”