NDLI: Few-shot genes selection: subset of PAM50 genes for breast cancer subtypes classification

Content Provider	Springer Nature : BioMed Central
Author	Okimoto, Leandro Y. S. Mendonca-Neto, Rayol Nakamura, Fabíola G. Nakamura, Eduardo F. Fenyö, David Silva, Claudio T.
Abstract	Background In recent years, researchers have made significant strides in understanding the heterogeneity of breast cancer and its various subtypes. However, the wealth of genomic and proteomic data available today necessitates efficient frameworks, instruments, and computational tools for meaningful analysis. Despite its success as a prognostic tool, the PAM50 gene signature’s reliance on many genes presents challenges in terms of cost and complexity. Consequently, there is a need for more efficient methods to classify breast cancer subtypes using a reduced gene set accurately. Results This study explores the potential of achieving precise breast cancer subtype categorization using a reduced gene set derived from the PAM50 gene signature. By employing a “Few-Shot Genes Selection” method, we randomly select smaller subsets from PAM50 and evaluate their performance using metrics and a linear model, specifically the Support Vector Machine (SVM) classifier. In addition, we aim to assess whether a more compact gene set can maintain performance while simplifying the classification process. Our findings demonstrate that certain reduced gene subsets can perform comparable or superior to the full PAM50 gene signature. Conclusions The identified gene subsets, with 36 genes, have the potential to contribute to the development of more cost-effective and streamlined diagnostic tools in breast cancer research and clinical settings.
Related Links	https://bmcbioinformatics.biomedcentral.com/counter/pdf/10.1186/s12859-024-05715-8.pdf
Ending Page	22
Page Count	22
Starting Page	1
File Format	HTM / HTML
ISSN	14712105
DOI	10.1186/s12859-024-05715-8
Journal	BMC Bioinformatics
Issue Number	1
Volume Number	25
Language	English
Publisher	BioMed Central
Publisher Date	2024-03-01
Access Restriction	Open
Subject Keyword	Bioinformatics Microarrays Computational Biology Computer Appl. in Life Sciences Algorithms Gene expression PAM50 Breast cancer subtypes EXplainable AI Computational Biology/Bioinformatics
Content Type	Text
Resource Type	Article
Subject	Molecular Biology Biochemistry Computer Science Applications Applied Mathematics Structural Biology
Journal Impact Factor	2.9/2023
5-Year Journal Impact Factor	3.6/2023

Sl.	Authority	Responsibilities	Communication Details
1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
5	Website/Portal (Helpdesk)	Queries regarding NDLI and its services	support@ndl.gov.in
6	Contents and Copyright Issues	Queries related to content curation and copyright issues	content@ndl.gov.in
7	National Digital Library of India Club (NDLI Club)	Queries related to NDLI Club formation, support, user awareness program, seminar/symposium, collaboration, social media, promotion, and outreach	clubsupport@ndl.gov.in
8	Digital Preservation Centre (DPC)	Assistance with digitizing and archiving copyright-free printed books	dpc@ndl.gov.in
9	IDR Setup or Support	Queries related to establishment and support of Institutional Digital Repository (IDR) and IDR workshops	idr@ndl.gov.in

Analysis of breast cancer subtypes by AP-ISA biclustering

Deep gene selection method to select genes from microarray datasets for cancer classification

Feature selection of gene expression data for Cancer classification using double RBF-kernels

Selection of optimal reference genes for normalization in quantitative RT-PCR

A network-assisted co-clustering algorithm to discover cancer subtypes based on gene expression

BCDForest: a boosting cascade deep forest model towards the classification of cancer subtypes based on gene expression data

The challenges of explainable AI in biomedical data science

Context-specific gene regulatory networks subdivide intrinsic subtypes of breast cancer

Improving accuracy for cancer classification with a new algorithm for genes selection

Few-shot genes selection: subset of PAM50 genes for breast cancer subtypes classification

Similar Documents

Analysis of breast cancer subtypes by AP-ISA biclustering

Deep gene selection method to select genes from microarray datasets for cancer classification

Feature selection of gene expression data for Cancer classification using double RBF-kernels

Selection of optimal reference genes for normalization in quantitative RT-PCR

A network-assisted co-clustering algorithm to discover cancer subtypes based on gene expression

BCDForest: a boosting cascade deep forest model towards the classification of cancer subtypes based on gene expression data

The challenges of explainable AI in biomedical data science

Context-specific gene regulatory networks subdivide intrinsic subtypes of breast cancer

Improving accuracy for cancer classification with a new algorithm for genes selection

Few-shot genes selection: subset of PAM50 genes for breast cancer subtypes classification