Journal
MOLECULAR INFORMATICS
Volume 34, Issue 11-12, Pages 753-760Publisher
WILEY-V C H VERLAG GMBH
DOI: 10.1002/minf.201500033
Keywords
Ensemble Learning; RGRF method; Rotation forest; Compound-pathway interaction
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Funding
- National Basic Research Program of China [2012CB910400]
- Fundamental Research Funds for the Central Universities [78260026]
- Scientific Innovation Act Project of Shanghai [14511106803]
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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.
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