Classification of Biological Sequences with Kernel Methods

Support vector machines and kernel methods are increasingly popular in genomics and computational biology due to their good performance in real-world applications and strong modularity that makes them suitable to a wide range of problems, from the classification of tumors to the automatic annotation of proteins. Their ability to work in a high dimension and process nonvectorial data, and the natural framework they provide to integrate heterogeneous data are particularly relevant to various problems arising in computational biology. In this chapter, we survey some of the most prominent applications published so far, highlighting the particular developments in kernel methods triggered by problems in biology, and mention a few promising research directions likely to expand in the future.

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Protein Family Classification using Deep Learning

10.1101/414128 ◽

2018 ◽

Cited By ~ 2

Author(s):

K S Naveenkumar ◽

Babu R Mohammed Harun ◽

R Vinayakumar ◽

KP Soman

Keyword(s):

Deep Learning ◽

Learning Algorithm ◽

Protein Classification ◽

Biological Sequences ◽

Biological Sequence ◽

Deep Learning Algorithm ◽

N Gram ◽

Family Classification ◽

Selection Of

AbstractProtein classification is responsible for the biological sequence, we came up with an idea which deals with the classification of proteomics using deep learning algorithm. This algorithm focuses mainly to classify sequences of protein-vector which is used for the representation of proteomics. Selection of the type protein representation is challenging based on which output in terms of accuracy is depended on, The protein representation used here is n-gram i.e. 3-gram and Keras embedding used for biological sequences like protein. In this paper we are working on the Protein classification to show the strength and representation of biological sequence of the proteins.

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autoBioSeqpy: A Deep Learning Tool for the Classification of Biological Sequences

Journal of Chemical Information and Modeling ◽

10.1021/acs.jcim.0c00409 ◽

2020 ◽

Vol 60 (8) ◽

pp. 3755-3764

Author(s):

Runyu Jing ◽

Yizhou Li ◽

Li Xue ◽

Fengjuan Liu ◽

Menglong Li ◽

...

Keyword(s):

Deep Learning ◽

Biological Sequences ◽

Learning Tool

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Kernel methods for the detection and classification of fish schools in single-beam and multibeam acoustic data

ICES Journal of Marine Science ◽

10.1093/icesjms/fsp004 ◽

2009 ◽

Vol 66 (6) ◽

pp. 1130-1135 ◽

Cited By ~ 9

Author(s):

Bart Buelens ◽

Tim Pauly ◽

Raymond Williams ◽

Arthur Sale

Keyword(s):

Kernel Methods ◽

Kernel Method ◽

Age Groups ◽

Routine Data ◽

Acoustic Properties ◽

Multibeam Sonar ◽

Single Beam ◽

Fish Schools ◽

Acoustic Data

Abstract Buelens, B., Pauly, T., Williams, R., and Sale, A. 2009. Kernel methods for the detection and classification of fish schools in single-beam and multibeam acoustic data. – ICES Journal of Marine Science, 66: 1130–1135. A kernel method for clustering acoustic data from single-beam echosounder and multibeam sonar is presented. The algorithm is used to detect fish schools and to classify acoustic data into clusters of similar acoustic properties. In a preprocessing routine, data from single-beam echosounder and multibeam sonar are transformed into an abstracted representation by multidimensional nodes, which are datapoints with spatial, temporal, and acoustic features as components. Kernel methods combine these components to determine clusters based on joint spatial, temporal, and acoustic similarities. These clusters yield a classification of the data in groups of similar nodes. Including the spatial components results in clusters for each school and effectively detects fish schools. Ignoring the spatial components yields a classification according to acoustic similarities, corresponding to classes of different species or age groups. The method is described and two case studies are presented.

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