An Improved Integrative Random Forest for Gene Regulatory Network Inference of Breast Cancer

• Main Authors: Chandran, Suntharaamurthi, Kohbalan, Moorthy, Mohd Arfian, Ismail, Mohd Zamri, Osman, Mohd Azwan Mohamad, Hamza, Ernawan, Ferda
• Format: Article
• Language: English
• Published: American Scientific Publisher 2018
• Subjects: QA76 Computer software
• Online Access: http://umpir.ump.edu.my/id/eprint/19971/1/43.%20An%20Improved%20Integrative%20Random%20Forest%20for%20Gene%20Regulatory%20Network%20Inference%20for%20Breast%20Cancer1.pdf; http://umpir.ump.edu.my/id/eprint/19971/; https://doi.org/10.1166/asl.2018.12979

Gene Regulatory Network (GRN) inference aims to capture the regulatory influences between the genes and regulatory events in the GRN. Integrative Random Forest for Gene Regulatory Network Inference (iRafNet) is a RF based algorithm which provides a great result in constructing GRN inference by integ...