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Mol Inform. 2015 Nov;34(11-12):753-60. doi: 10.1002/minf.201500033. Epub 2015 Aug 6.

Inferring Association between Compound and Pathway with an Improved Ensemble Learning Method.

Song M1,2, Jiang Z3,4,5.

Author information

1
Shanghai Key Laboratory of Multidimensional Information Processing, East China Normal University, Shanghai 200241, China.
2
Department of Computer Science and Technology, East China Normal University, Shanghai 200241, China.
3
East China Normal University, Dept. of Computer Science & Technology, 500 Dong Chuan Road, Shanghai 200241, China;, Tel: +86-21-54345188;. zrjiang@cs.ecnu.edu.cn.
4
Shanghai Key Laboratory of Multidimensional Information Processing, East China Normal University, Shanghai 200241, China. zrjiang@cs.ecnu.edu.cn.
5
Department of Computer Science and Technology, East China Normal University, Shanghai 200241, China. zrjiang@cs.ecnu.edu.cn.

Abstract

Emergence of compound molecular data coupled to pathway information offers the possibility of using machine learning methods for compound-pathway associations' inference. To provide insights into the global relationship between compounds and their affected pathways, a improved Rotation Forest ensemble learning method called RGRF (Relief & GBSSL - Rotation Forest) was proposed to predict their potential associations. The main characteristic of the RGRF lies in using the Relief algorithm for feature extraction and regarding the Graph-Based Semi-Supervised Learning method as classifier. By incorporating the chemical structure information, drug mode of action information and genomic space information, our method can achieve a better precision and flexibility on compound-pathway prediction. Moreover, several new compound-pathway associations that having the potential for further clinical investigation have been identified by database searching. In the end, a prediction tool was developed using RGRF algorithm, which can predict the interactions between pathways and all of the compounds in cMap database.

KEYWORDS:

Compound-pathway interaction; Ensemble Learning; RGRF method; Rotation forest

PMID:
27491036
DOI:
10.1002/minf.201500033
[Indexed for MEDLINE]

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