Publications by authors named "Rayol Mendonca-Neto"
Article Synopsis
- Researchers have advanced the understanding of breast cancer diversity but face challenges with the complex PAM50 gene signature due to its high cost and complexity.
- This study investigates the effectiveness of using smaller gene subsets from the PAM50 gene signature, applying a method called "Few-Shot Genes Selection" to evaluate their classification performance with a Support Vector Machine (SVM) model.
- Results indicate that certain reduced gene sets, containing only 36 genes, can match or exceed the performance of the full PAM50 signature, suggesting they could lead to more efficient and affordable diagnostic tools in breast cancer research and clinics.
A Gene Selection Method Based on Outliers for Breast Cancer Subtype Classification.
IEEE/ACM Trans Comput Biol Bioinform
October 2022
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.
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