Survival analysis for the identified cancer gene subtype from the co-clustering algorithm
Cancer gene subtype information is significant for understanding tumour heterogeneity. The early detection of cancer and subsequent treatment can be lifesaving. However, it is hard clinically and computationally to detect cancer and its subtypes in their early stages. Therefore, we extend the analys...
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| Format: | Conference or Workshop Item |
| Language: | English English |
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Institute of Electrical and Electronics Engineers Inc.
2022
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| Online Access: | http://umpir.ump.edu.my/id/eprint/39408/1/Survival%20analysis%20for%20the%20identified%20cancer%20gene%20subtype%20from%20the%20co.pdf http://umpir.ump.edu.my/id/eprint/39408/2/Survival%20analysis%20for%20the%20identified%20cancer%20gene%20subtype%20from%20the%20co-clustering%20algorithm_ABS.pdf |
| _version_ | 1825815291461894144 |
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| author | Logenthiran, Machap Kohbalan, Moorthy |
| author_facet | Logenthiran, Machap Kohbalan, Moorthy |
| author_sort | Logenthiran, Machap |
| collection | UMP |
| description | Cancer gene subtype information is significant for understanding tumour heterogeneity. The early detection of cancer and subsequent treatment can be lifesaving. However, it is hard clinically and computationally to detect cancer and its subtypes in their early stages. Therefore, we extend the analysis and results from Machap et al. (2019), to include the KaplanMeier survival analysis with the integration of gene expression and clinical features data. There are two cancer datasets used for the analysis : breast cancer and glioblastoma multiforme. The luminal type was the common subtype of breast cancer, showing a higher survival rate. Whereas the Proneural subtype in glioblastoma multiforme has a little longer survival rate than the other three subtypes. These molecular differences between subtypes have been shown to correlate very well with clinical features and survival parameters to help understand the disease and develop better therapeutic targets. |
| first_indexed | 2024-03-06T13:11:19Z |
| format | Conference or Workshop Item |
| id | UMPir39408 |
| institution | Universiti Malaysia Pahang |
| language | English English |
| last_indexed | 2024-03-06T13:11:19Z |
| publishDate | 2022 |
| publisher | Institute of Electrical and Electronics Engineers Inc. |
| record_format | dspace |
| spelling | UMPir394082023-11-28T04:14:12Z http://umpir.ump.edu.my/id/eprint/39408/ Survival analysis for the identified cancer gene subtype from the co-clustering algorithm Logenthiran, Machap Kohbalan, Moorthy QA75 Electronic computers. Computer science QA76 Computer software T Technology (General) TA Engineering (General). Civil engineering (General) Cancer gene subtype information is significant for understanding tumour heterogeneity. The early detection of cancer and subsequent treatment can be lifesaving. However, it is hard clinically and computationally to detect cancer and its subtypes in their early stages. Therefore, we extend the analysis and results from Machap et al. (2019), to include the KaplanMeier survival analysis with the integration of gene expression and clinical features data. There are two cancer datasets used for the analysis : breast cancer and glioblastoma multiforme. The luminal type was the common subtype of breast cancer, showing a higher survival rate. Whereas the Proneural subtype in glioblastoma multiforme has a little longer survival rate than the other three subtypes. These molecular differences between subtypes have been shown to correlate very well with clinical features and survival parameters to help understand the disease and develop better therapeutic targets. Institute of Electrical and Electronics Engineers Inc. 2022 Conference or Workshop Item PeerReviewed pdf en http://umpir.ump.edu.my/id/eprint/39408/1/Survival%20analysis%20for%20the%20identified%20cancer%20gene%20subtype%20from%20the%20co.pdf pdf en http://umpir.ump.edu.my/id/eprint/39408/2/Survival%20analysis%20for%20the%20identified%20cancer%20gene%20subtype%20from%20the%20co-clustering%20algorithm_ABS.pdf Logenthiran, Machap and Kohbalan, Moorthy (2022) Survival analysis for the identified cancer gene subtype from the co-clustering algorithm. In: International Conference on Electrical, Computer, and Energy Technologies, ICECET 2022 , 20-22 July 2022 , Prague-Czech Republic. pp. 1-6. (182630). ISBN 978-166547087-2 (Published) https://doi.org/10.1109/ICECET55527.2022.9872811 |
| spellingShingle | QA75 Electronic computers. Computer science QA76 Computer software T Technology (General) TA Engineering (General). Civil engineering (General) Logenthiran, Machap Kohbalan, Moorthy Survival analysis for the identified cancer gene subtype from the co-clustering algorithm |
| title | Survival analysis for the identified cancer gene subtype from the co-clustering algorithm |
| title_full | Survival analysis for the identified cancer gene subtype from the co-clustering algorithm |
| title_fullStr | Survival analysis for the identified cancer gene subtype from the co-clustering algorithm |
| title_full_unstemmed | Survival analysis for the identified cancer gene subtype from the co-clustering algorithm |
| title_short | Survival analysis for the identified cancer gene subtype from the co-clustering algorithm |
| title_sort | survival analysis for the identified cancer gene subtype from the co clustering algorithm |
| topic | QA75 Electronic computers. Computer science QA76 Computer software T Technology (General) TA Engineering (General). Civil engineering (General) |
| url | http://umpir.ump.edu.my/id/eprint/39408/1/Survival%20analysis%20for%20the%20identified%20cancer%20gene%20subtype%20from%20the%20co.pdf http://umpir.ump.edu.my/id/eprint/39408/2/Survival%20analysis%20for%20the%20identified%20cancer%20gene%20subtype%20from%20the%20co-clustering%20algorithm_ABS.pdf |
| work_keys_str_mv | AT logenthiranmachap survivalanalysisfortheidentifiedcancergenesubtypefromthecoclusteringalgorithm AT kohbalanmoorthy survivalanalysisfortheidentifiedcancergenesubtypefromthecoclusteringalgorithm |