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

Escuela de Medicina y Ciencias de la Salud

Producción científica: Capítulo en Libro/Reporte › Contribución a la conferencia

Resumen

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.

Idioma original	Inglés estadounidense
Título de la publicación alojada	2023 IEEE Colombian Conference on Applications of Computational Intelligence, ColCACI 2023 - Proceedings
Editores	Alvaro David Orjuela-Canon
Editorial	Institute of Electrical and Electronics Engineers Inc.
ISBN (versión digital)	9798350316599
DOI	https://doi.org/10.1109/ColCACI59285.2023.10226026
Estado	Publicada - 2023
Evento	2023 IEEE Colombian Conference on Applications of Computational Intelligence, ColCACI 2023 - Bogota, Colombia Duración: jul. 26 2023 → jul. 28 2023

Serie de la publicación

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

Conferencia

Conferencia	2023 IEEE Colombian Conference on Applications of Computational Intelligence, ColCACI 2023
País/Territorio	Colombia
Ciudad	Bogota
Período	7/26/23 → 7/28/23

Áreas temáticas de ASJC Scopus

Inteligencia artificial
Informática aplicada
Visión artificial y reconocimiento de patrones
Control y optimización

ODS de las Naciones Unidas

Este resultado contribuye a los siguientes Objetivos de Desarrollo Sostenible

Acceder al documento

10.1109/ColCACI59285.2023.10226026

Otros archivos y enlaces

Citar esto

Ospino, C. A. P., Rivera, J. A. C., & Orjuela-Canon, A. D. (2023). Machine Learning Clustering for Cancer Analysis Employing Gene Expression Data. En 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 2023 - Proceedings). 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 2023 - Proceedings).

@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",

doi = "10.1109/ColCACI59285.2023.10226026",

language = "English (US)",

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

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Ospino, CAP, Rivera, JAC & Orjuela-Canon, AD 2023, Machine Learning Clustering for Cancer Analysis Employing Gene Expression Data. En 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 2023 - Proceedings, 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 2023 - Proceedings).

Producción científica: Capítulo en Libro/Reporte › Contribución a la conferencia

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

Y1 - 2023

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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DO - 10.1109/ColCACI59285.2023.10226026

M3 - Conference contribution

AN - SCOPUS:85171621798

T3 - 2023 IEEE Colombian Conference on Applications of Computational Intelligence, ColCACI 2023 - Proceedings

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. En 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 2023 - Proceedings). doi: 10.1109/ColCACI59285.2023.10226026

Machine Learning Clustering for Cancer Analysis Employing Gene Expression Data

Resumen

Serie de la publicación

Conferencia

Áreas temáticas de ASJC Scopus

ODS de las Naciones Unidas

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