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- W2104857116 abstract "Distant evolutionary relationships between proteins with low sequence similarity are difficult to recognise by computational methods. Consequently, many sequences obtained from large-scale sequencing projects cannot be assigned to any known proteins or families despite being evolutionarily related. To boost sensitivity, various sequence-based methods have been modified to make use of the better conserved secondary structure. Most of these methods are instance-based or generative. Here, we introduce a kernel-based remote homology detection method that allows for a combination of sequence and secondary-structure similarity scores in a discriminative approach. We studied the ability of the method to predict superfamily membership as defined by the SCOP database. We show that a kernel method that combined sequence similarity scores with predicted secondary-structure similarity scores performed similar to a classifier that used scores calculated from sequences and true secondary structures, but performed better than a sequence-only based classifier and achieved a better mean than recently published results on the same data-set. It can be concluded that SVM classifiers trained to predict homology between distantly related proteins, become more accurate, if a joint sequence/secondary-structure similarity score approach is used." @default.
- W2104857116 created "2016-06-24" @default.
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- W2104857116 date "2009-01-01" @default.
- W2104857116 modified "2023-09-28" @default.
- W2104857116 title "Remote Homology Detection Using a Kernel Method that Combines Sequence and Secondary-Structure Similarity Scores" @default.
- W2104857116 doi "https://doi.org/10.3233/isb-2009-0390" @default.
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