An Improved Integrative Random Forest for Gene Regulatory Network Inference of Breast Cancer
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...
| Main Authors: | , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
American Scientific Publisher
2018
|
| Subjects: | |
| 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 |
| _version_ | 1796992488881258496 |
|---|---|
| author | Chandran, Suntharaamurthi Kohbalan, Moorthy Mohd Arfian, Ismail Mohd Zamri, Osman Mohd Azwan Mohamad, Hamza Ernawan, Ferda |
| author_facet | Chandran, Suntharaamurthi Kohbalan, Moorthy Mohd Arfian, Ismail Mohd Zamri, Osman Mohd Azwan Mohamad, Hamza Ernawan, Ferda |
| author_sort | Chandran, Suntharaamurthi |
| collection | UMP |
| description | 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 integrating multiple data types. Most of the approaches did justify their duty but there are some limitations which don’t allow it to reach its optimal state and needs a very long computational time to construct a GRN inference. Other than that, they do not provide optimal performance. There are redundant genes in the dataset. GRN inference by existing methods has a lower accuracy on benchmark and real dataset. Furthermore, the computational time to produce the GRN inference is very long in the existing methods. To overcome these issues is proposed improved the existing method by adding a gene selection into it. To perform the improvement the existing methods was studied and analyzed on their performance in constructing GRN inference. Improved iRafNet was designed and developed to reduce the computational time to construct the GRN inference gene from the dataset. Finally, the accuracy and computational time of the proposed method was validated and verified with the benchmark and real dataset. Improved iRafNet has proven its performance by having a higher AUC and lower computational time. |
| first_indexed | 2024-03-06T12:20:51Z |
| format | Article |
| id | UMPir19971 |
| institution | Universiti Malaysia Pahang |
| language | English |
| last_indexed | 2024-03-06T12:20:51Z |
| publishDate | 2018 |
| publisher | American Scientific Publisher |
| record_format | dspace |
| spelling | UMPir199712018-11-21T04:52:35Z http://umpir.ump.edu.my/id/eprint/19971/ An Improved Integrative Random Forest for Gene Regulatory Network Inference of Breast Cancer Chandran, Suntharaamurthi Kohbalan, Moorthy Mohd Arfian, Ismail Mohd Zamri, Osman Mohd Azwan Mohamad, Hamza Ernawan, Ferda QA76 Computer software 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 integrating multiple data types. Most of the approaches did justify their duty but there are some limitations which don’t allow it to reach its optimal state and needs a very long computational time to construct a GRN inference. Other than that, they do not provide optimal performance. There are redundant genes in the dataset. GRN inference by existing methods has a lower accuracy on benchmark and real dataset. Furthermore, the computational time to produce the GRN inference is very long in the existing methods. To overcome these issues is proposed improved the existing method by adding a gene selection into it. To perform the improvement the existing methods was studied and analyzed on their performance in constructing GRN inference. Improved iRafNet was designed and developed to reduce the computational time to construct the GRN inference gene from the dataset. Finally, the accuracy and computational time of the proposed method was validated and verified with the benchmark and real dataset. Improved iRafNet has proven its performance by having a higher AUC and lower computational time. American Scientific Publisher 2018-11 Article PeerReviewed pdf en http://umpir.ump.edu.my/id/eprint/19971/1/43.%20An%20Improved%20Integrative%20Random%20Forest%20for%20Gene%20Regulatory%20Network%20Inference%20for%20Breast%20Cancer1.pdf Chandran, Suntharaamurthi and Kohbalan, Moorthy and Mohd Arfian, Ismail and Mohd Zamri, Osman and Mohd Azwan Mohamad, Hamza and Ernawan, Ferda (2018) An Improved Integrative Random Forest for Gene Regulatory Network Inference of Breast Cancer. Advanced Science Letters, 24 (10). pp. 7566-7571. ISSN 1936-6612. (Published) https://doi.org/10.1166/asl.2018.12979 doi: 10.1166/asl.2018.12979 |
| spellingShingle | QA76 Computer software Chandran, Suntharaamurthi Kohbalan, Moorthy Mohd Arfian, Ismail Mohd Zamri, Osman Mohd Azwan Mohamad, Hamza Ernawan, Ferda An Improved Integrative Random Forest for Gene Regulatory Network Inference of Breast Cancer |
| title | An Improved Integrative Random Forest for Gene Regulatory Network Inference of Breast Cancer |
| title_full | An Improved Integrative Random Forest for Gene Regulatory Network Inference of Breast Cancer |
| title_fullStr | An Improved Integrative Random Forest for Gene Regulatory Network Inference of Breast Cancer |
| title_full_unstemmed | An Improved Integrative Random Forest for Gene Regulatory Network Inference of Breast Cancer |
| title_short | An Improved Integrative Random Forest for Gene Regulatory Network Inference of Breast Cancer |
| title_sort | improved integrative random forest for gene regulatory network inference of breast cancer |
| topic | QA76 Computer software |
| url | http://umpir.ump.edu.my/id/eprint/19971/1/43.%20An%20Improved%20Integrative%20Random%20Forest%20for%20Gene%20Regulatory%20Network%20Inference%20for%20Breast%20Cancer1.pdf |
| work_keys_str_mv | AT chandransuntharaamurthi animprovedintegrativerandomforestforgeneregulatorynetworkinferenceofbreastcancer AT kohbalanmoorthy animprovedintegrativerandomforestforgeneregulatorynetworkinferenceofbreastcancer AT mohdarfianismail animprovedintegrativerandomforestforgeneregulatorynetworkinferenceofbreastcancer AT mohdzamriosman animprovedintegrativerandomforestforgeneregulatorynetworkinferenceofbreastcancer AT mohdazwanmohamadhamza animprovedintegrativerandomforestforgeneregulatorynetworkinferenceofbreastcancer AT ernawanferda animprovedintegrativerandomforestforgeneregulatorynetworkinferenceofbreastcancer AT chandransuntharaamurthi improvedintegrativerandomforestforgeneregulatorynetworkinferenceofbreastcancer AT kohbalanmoorthy improvedintegrativerandomforestforgeneregulatorynetworkinferenceofbreastcancer AT mohdarfianismail improvedintegrativerandomforestforgeneregulatorynetworkinferenceofbreastcancer AT mohdzamriosman improvedintegrativerandomforestforgeneregulatorynetworkinferenceofbreastcancer AT mohdazwanmohamadhamza improvedintegrativerandomforestforgeneregulatorynetworkinferenceofbreastcancer AT ernawanferda improvedintegrativerandomforestforgeneregulatorynetworkinferenceofbreastcancer |