MLFSdel: An accurate approach to discover genome deletions

DOI

10.2991/icmmct-17.2017.150How to use a DOI?

Keywords

Sequence Analysis; Feature; Model;

Abstract

Genome deletions are one of the common types of structural variations. The discovery of deletions has become an important research field in SVs detection of genome sequences. At present, the existing methods have their own limitations, and these methods are also insufficient in precision and sensitivity. Hence, improving the detecting efficiency has become a critical target in subsequent research. In this paper, we developed a method, namely MLFSdel. Essentially, MLFSdel employs four machine learning models and implements a novel feature selection strategy. By eliminating the features having the negative effect on the overall classification results, the proposed method improves the precision and sensitivity in comparison to four previous methods for detecting deletions. In addition, it further proves that the feature-based machine learning methods are applicable to detect genome deletions.

Copyright

© 2017, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - CONF
AU  - Yao Zhang
AU  - JingYang Gao
PY  - 2017/04
DA  - 2017/04
TI  - MLFSdel: An accurate approach to discover genome deletions
BT  - Proceedings of the 2017 5th International Conference on Machinery, Materials and Computing Technology (ICMMCT 2017)
PB  - Atlantis Press
SP  - 745
EP  - 749
SN  - 2352-5401
UR  - https://doi.org/10.2991/icmmct-17.2017.150
DO  - 10.2991/icmmct-17.2017.150
ID  - Zhang2017/04
ER  -