| Full text | |
| Author(s): |
Mendonca-Neto, Rayol
;
Li, Zhi
;
Fenyo, David
;
Silva, Claudio T.
;
Nakamura, Fabiola G.
;
Nakamura, Eduardo F.
Total Authors: 6
|
| Document type: | Journal article |
| Source: | IEEE-ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS; v. 19, n. 5, p. 13-pg., 2022-09-01. |
| Abstract | |
Breast cancer is the second most common cancer type and is the leading cause of cancer-related deaths worldwide. Since it is a heterogeneous disease, subtyping breast cancer plays an important role in performing a specific treatment. Gene expression data is a viable alternative to be employed on cancer subtype classification, as they represent the state of a cell at the molecular level, but generally has a relatively small number of samples compared to a large number of genes. Gene selection is a promising approach that addresses this uneven high-dimensional matrix of genes versus samples and plays an important role in the development of efficient cancer subtype classification. In this work, an innovative outlier-based gene selection (OGS) method is proposed to select relevant genes for efficiently and effectively classify breast cancer subtypes. Experiments show that our strategy presents an F-1 score of 1.0 for basal and 0.86 for her 2, the two subtypes with the worst prognoses, respectively. Compared to other methods, our proposed method outperforms in the F-1 score using 80% less genes. In general, our method selects only a few highly relevant genes, speeding up the classification, and significantly improving the classifier's performance. (AU) | |
| FAPESP's process: | 20/05173-4 - A multimodal approach to identify bias in digital social media |
| Grantee: | Altigran Soares da Silva |
| Support Opportunities: | Regular Research Grants |
| FAPESP's process: | 20/09866-4 - Artificial Intelligence Innovation Center for Health (CIIA-Health) |
| Grantee: | Virgilio Augusto Fernandes Almeida |
| Support Opportunities: | Research Grants - Research Centers in Engineering Program |