Inferring Association between Compound and Pathway with an Improved Ensemble Learning Method.
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.
['Song|Meiyue|M|', 'Jiang|Zhenran|Z|']
D000465:Algorithms D000970:Antineoplastic Agents D030541:Databases, Genetic D055808:Drug Discovery D006801:Humans D061986:MCF-7 Cells D015394:Molecular Structure D020411:Oligonucleotide Array Sequence Analysis
Ensemble Learning Rotation forest Compound-pathway interaction RGRF method