Machine Learning Clustering for Cancer Analysis Employing Gene Expression Data

Camilo Andres Perez Ospino; Jorman Arbey Castro Rivera; Alvaro D. Orjuela-Canon

doi:10.1109/colcaci59285.2023.10226026

Machine Learning Clustering for Cancer Analysis Employing Gene Expression Data

Camilo Andres Perez Ospino
, Jorman Arbey Castro Rivera
, Alvaro D. Orjuela-Canon

Research output: Chapter in Book/Inform › Conference contribution

2 Scopus citations

Abstract

The idea that cancer types vary in their molecular structure (DNA, RNA, proteins, and epigenetics) depending on the origin and location of the cancer, has been worked on. The Cancer Genome Atlas (TCGA) has generated an initiative to carefully create a database to ensure quality data in the profiling of different tumors to promote research, a part of this large database was called Pan-Cancer, which has the genomic, epigenetic, transcriptional and proteomic profiling of 12 different types of cancer. In this research we took one of the profiling, RNA profiling, in 5 cancer types, in order to determine the possibility of segmenting in an unsupervised manner and to evaluate the difference of them by their origin. The results indicate that the number of clusters can vary from 5 to 7, with 5 clusters being established by the database labels, however, the division of 6 or 7 clusters is due to the clustering of breast cancer (BRCA) which has several origins.

Translated title of the contribution	Agrupación de aprendizaje automático para el análisis del cáncer mediante datos de expresión génica
Original language	English (US)
Title of host publication	2023 IEEE Colombian Conference on Applications of Computational Intelligence, ColCACI 2023 - Proceedings
Editors	Alvaro David Orjuela-Canon
Publisher	Institute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)	9798350316599
DOIs	https://doi.org/10.1109/colcaci59285.2023.10226026
State	Published - Jul 26 2023
Event	2023 IEEE Colombian Conference on Applications of Computational Intelligence, ColCACI 2023 - Bogota, Colombia Duration: Jul 26 2023 → Jul 28 2023

Publication series

Name	2023 IEEE Colombian Conference on Applications of Computational Intelligence (ColCACI)

Conference

Conference	2023 IEEE Colombian Conference on Applications of Computational Intelligence, ColCACI 2023
Country/Territory	Colombia
City	Bogota
Period	7/26/23 → 7/28/23

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 3 Good Health and Well-being

All Science Journal Classification (ASJC) codes

Artificial Intelligence
Computer Science Applications
Computer Vision and Pattern Recognition
Control and Optimization

Access to Document

10.1109/colcaci59285.2023.10226026

Cite this

Ospino, C. A. P., Rivera, J. A. C., & Orjuela-Canon, A. D. (2023). Machine Learning Clustering for Cancer Analysis Employing Gene Expression Data. In A. D. Orjuela-Canon (Ed.), 2023 IEEE Colombian Conference on Applications of Computational Intelligence, ColCACI 2023 - Proceedings (2023 IEEE Colombian Conference on Applications of Computational Intelligence (ColCACI)). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/colcaci59285.2023.10226026

Ospino, Camilo Andres Perez ; Rivera, Jorman Arbey Castro ; Orjuela-Canon, Alvaro D. / Machine Learning Clustering for Cancer Analysis Employing Gene Expression Data. 2023 IEEE Colombian Conference on Applications of Computational Intelligence, ColCACI 2023 - Proceedings. editor / Alvaro David Orjuela-Canon. Institute of Electrical and Electronics Engineers Inc., 2023. (2023 IEEE Colombian Conference on Applications of Computational Intelligence (ColCACI)).

@inproceedings{f2398115bf82498791f3c427c88114ec,

title = "Machine Learning Clustering for Cancer Analysis Employing Gene Expression Data",

abstract = "The idea that cancer types vary in their molecular structure (DNA, RNA, proteins, and epigenetics) depending on the origin and location of the cancer, has been worked on. The Cancer Genome Atlas (TCGA) has generated an initiative to carefully create a database to ensure quality data in the profiling of different tumors to promote research, a part of this large database was called Pan-Cancer, which has the genomic, epigenetic, transcriptional and proteomic profiling of 12 different types of cancer. In this research we took one of the profiling, RNA profiling, in 5 cancer types, in order to determine the possibility of segmenting in an unsupervised manner and to evaluate the difference of them by their origin. The results indicate that the number of clusters can vary from 5 to 7, with 5 clusters being established by the database labels, however, the division of 6 or 7 clusters is due to the clustering of breast cancer (BRCA) which has several origins.",

author = "Ospino, \{Camilo Andres Perez\} and Rivera, \{Jorman Arbey Castro\} and Orjuela-Canon, \{Alvaro D.\}",

note = "Funding Information: ACKNOWLEDGMENT Authors acknowledge the support of the Universidad del Rosario for funding this project. In addition, the contribution of research incubator team Semillero en Inteligencia Artificial en Salud: Semill-IAS. Publisher Copyright: {\textcopyright} 2023 IEEE.; 2023 IEEE Colombian Conference on Applications of Computational Intelligence, ColCACI 2023 ; Conference date: 26-07-2023 Through 28-07-2023",

year = "2023",

month = jul,

day = "26",

doi = "10.1109/colcaci59285.2023.10226026",

language = "English (US)",

series = "2023 IEEE Colombian Conference on Applications of Computational Intelligence (ColCACI)",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

editor = "Orjuela-Canon, \{Alvaro David\}",

booktitle = "2023 IEEE Colombian Conference on Applications of Computational Intelligence, ColCACI 2023 - Proceedings",

address = "United States",

}

Ospino, CAP, Rivera, JAC & Orjuela-Canon, AD 2023, Machine Learning Clustering for Cancer Analysis Employing Gene Expression Data. in AD Orjuela-Canon (ed.), 2023 IEEE Colombian Conference on Applications of Computational Intelligence, ColCACI 2023 - Proceedings. 2023 IEEE Colombian Conference on Applications of Computational Intelligence (ColCACI), Institute of Electrical and Electronics Engineers Inc., 2023 IEEE Colombian Conference on Applications of Computational Intelligence, ColCACI 2023, Bogota, Colombia, 7/26/23. https://doi.org/10.1109/colcaci59285.2023.10226026

Machine Learning Clustering for Cancer Analysis Employing Gene Expression Data. / Ospino, Camilo Andres Perez; Rivera, Jorman Arbey Castro; Orjuela-Canon, Alvaro D.
2023 IEEE Colombian Conference on Applications of Computational Intelligence, ColCACI 2023 - Proceedings. ed. / Alvaro David Orjuela-Canon. Institute of Electrical and Electronics Engineers Inc., 2023. (2023 IEEE Colombian Conference on Applications of Computational Intelligence (ColCACI)).

Research output: Chapter in Book/Inform › Conference contribution

TY - GEN

T1 - Machine Learning Clustering for Cancer Analysis Employing Gene Expression Data

AU - Ospino, Camilo Andres Perez

AU - Rivera, Jorman Arbey Castro

AU - Orjuela-Canon, Alvaro D.

N1 - Funding Information: ACKNOWLEDGMENT Authors acknowledge the support of the Universidad del Rosario for funding this project. In addition, the contribution of research incubator team Semillero en Inteligencia Artificial en Salud: Semill-IAS. Publisher Copyright: © 2023 IEEE.

PY - 2023/7/26

Y1 - 2023/7/26

N2 - The idea that cancer types vary in their molecular structure (DNA, RNA, proteins, and epigenetics) depending on the origin and location of the cancer, has been worked on. The Cancer Genome Atlas (TCGA) has generated an initiative to carefully create a database to ensure quality data in the profiling of different tumors to promote research, a part of this large database was called Pan-Cancer, which has the genomic, epigenetic, transcriptional and proteomic profiling of 12 different types of cancer. In this research we took one of the profiling, RNA profiling, in 5 cancer types, in order to determine the possibility of segmenting in an unsupervised manner and to evaluate the difference of them by their origin. The results indicate that the number of clusters can vary from 5 to 7, with 5 clusters being established by the database labels, however, the division of 6 or 7 clusters is due to the clustering of breast cancer (BRCA) which has several origins.

AB - The idea that cancer types vary in their molecular structure (DNA, RNA, proteins, and epigenetics) depending on the origin and location of the cancer, has been worked on. The Cancer Genome Atlas (TCGA) has generated an initiative to carefully create a database to ensure quality data in the profiling of different tumors to promote research, a part of this large database was called Pan-Cancer, which has the genomic, epigenetic, transcriptional and proteomic profiling of 12 different types of cancer. In this research we took one of the profiling, RNA profiling, in 5 cancer types, in order to determine the possibility of segmenting in an unsupervised manner and to evaluate the difference of them by their origin. The results indicate that the number of clusters can vary from 5 to 7, with 5 clusters being established by the database labels, however, the division of 6 or 7 clusters is due to the clustering of breast cancer (BRCA) which has several origins.

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U2 - 10.1109/colcaci59285.2023.10226026

DO - 10.1109/colcaci59285.2023.10226026

M3 - Conference contribution

AN - SCOPUS:85171621798

T3 - 2023 IEEE Colombian Conference on Applications of Computational Intelligence (ColCACI)

BT - 2023 IEEE Colombian Conference on Applications of Computational Intelligence, ColCACI 2023 - Proceedings

A2 - Orjuela-Canon, Alvaro David

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 2023 IEEE Colombian Conference on Applications of Computational Intelligence, ColCACI 2023

Y2 - 26 July 2023 through 28 July 2023

ER -

Ospino CAP, Rivera JAC, Orjuela-Canon AD. Machine Learning Clustering for Cancer Analysis Employing Gene Expression Data. In Orjuela-Canon AD, editor, 2023 IEEE Colombian Conference on Applications of Computational Intelligence, ColCACI 2023 - Proceedings. Institute of Electrical and Electronics Engineers Inc. 2023. (2023 IEEE Colombian Conference on Applications of Computational Intelligence (ColCACI)). doi: 10.1109/colcaci59285.2023.10226026

Machine Learning Clustering for Cancer Analysis Employing Gene Expression Data

Abstract

Publication series

Conference

UN SDGs

All Science Journal Classification (ASJC) codes

Access to Document

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