NDLI: Community detection-based features for sequence classification

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Community detection-based features for sequence classification

Content Provider	ACM Digital Library
Author	Caragea, Doina Tangirala, Karthik
Abstract	Machine learning algorithms are widely used to annotate biological sequences. Most learning algorithms require data to be represented using a vector of features. In the absence of biologically informative features, k-mers generated using the sliding window-based approach are commonly used to represent biological sequences. However, the number of features generated using the sliding window-based approach increases exponentially with the length of the k-mer, k, and as a consequence, the computational time of the learning algorithms also increases. As an alternative to k-mers, we propose to use a community detection approach to identify informative features for sequence classification. The community detection approach is run on a network constructed based on k-mer similarity, and it has the advantage that class labels are not needed when constructing the network. It works by identifying sets of nodes that are densely connected when compared to other nodes of the network. Community detection approaches are computationally expensive, especially for large or densely connected networks. To address this challenge, we propose an efficient strategy that involves running the community detection approach on a set of samples, where each sample is a small subset of the total set of sequences. A set of features is obtained from each sample, and the union of all feature sets is used to represent sequences for classification purposes. Experimental results in both supervised and semi-supervised settings show that the features generated with the community detection approach are more informative than k-mers. Furthermore, we observe that running the community detection approach on a set of small samples of sequences produces better features as compared to running the community detection approach on a set of large samples of sequences.
Starting Page	559
Ending Page	568
Page Count	10
File Format	PDF
ISBN	9781450328944
DOI	10.1145/2649387.2649446
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2014-09-20
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	Dimensionality reduction Motifs Semi-supervised learning Supervised learning Feature generation Machine learning Sequence classification
Content Type	Text
Resource Type	Article

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