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

Título traducido de la contribución: Agrupación de aprendizaje automático para el análisis del cáncer mediante datos de expresión génica

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

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

Resumen

Se ha trabajado en la idea de que los tipos de cáncer varían en su estructura molecular (ADN, ARN, proteínas y epigenética) dependiendo del origen y localización del cáncer. El Atlas del Genoma del Cáncer (TCGA) ha generado una iniciativa para crear cuidadosamente una base de datos que garantice datos de calidad en el perfilado de los diferentes tumores para promover la investigación, una parte de esta gran base de datos se llamó Pan-Cancer, que tiene el perfilado genómico, epigenético, transcripcional y proteómico de 12 tipos diferentes de cáncer. En esta investigación tomamos uno de los perfiles, el de ARN, en 5 tipos de cáncer, para determinar la posibilidad de segmentar de forma no supervisada y evaluar la diferencia de los mismos por su origen. Los resultados indican que el número de clusters puede variar de 5 a 7, siendo 5 clusters los establecidos por las etiquetas de la base de datos, sin embargo, la división de 6 o 7 clusters se debe a la agrupación del cáncer de mama (BRCA) que tiene varios orígenes.

Título traducido de la contribución	Agrupación de aprendizaje automático para el análisis del cáncer mediante datos de expresión génica
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 - ago. 28 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

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10.1109/ColCACI59285.2023.10226026

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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).

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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",

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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/Informe › Contribución a la conferencia

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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 - 2023/8/28

Y1 - 2023/8/28

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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M3 - Conference contribution

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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.

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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

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ODS de las Naciones Unidas

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