NDLI: Incorporating Homology Using Multi-Instance Kernel for Protein Subcelluar Localization

Content Provider	IEEE Xplore Digital Library
Author	Suyu Mei Wang Fei
Copyright Year	2010
Abstract	Kernel method has witnessed many successful applications in computational biology in recent years, and thus kernel design is a key step to define the similarity between two protein sequences. This paper aims at designing a kernel to derive more accurate similarity between two protein sequences by incorporating homology. Here a homologous sequence is viewed as one evolutionary instance of the target sequence and all homologous sequences constitute one homology bag. K-mer based spectrum kernel is used to define the similarity between any two instances and multi-instance kernel is as the sum of instance-wise spectrum kernels, called homology-based multi-instance kernel (HoMIKernel). By varying k-mer size and compressing 20 amino acids, we can derive several HoMIKernels, which are combined as HoMIKernel+ to capture more contextual information and cover size-varying motifs. We evaluate HoMIKernel+ on three unbalanced eukaryotic benchmark dataset. The experiments show that HoMIKernel+ achieves better predictive performance than the baseline models; and the incorporation of homologous sequences does increase the predictive accuracy.
Starting Page	1
Ending Page	4
File Size	229000
Page Count	4
File Format	PDF
ISBN	9781424447121
ISSN	21517622
DOI	10.1109/ICBBE.2010.5515543
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2010-06-18
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Drugs Shape Laboratories Predictive models Amino acids Feature extraction Protein sequence Phylogeny Data mining Kernel
Content Type	Text
Resource Type	Article

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