Data Fusion Analysis for Determining Localization of Proteins Associated to Escherichia coli

Alvaro David Orjuela-Canon; Diana C. Rodriguez Burbano; Oscar Perdomo

doi:10.1109/colcaci56938.2022.9905354

Data Fusion Analysis for Determining Localization of Proteins Associated to Escherichia coli

Alvaro David Orjuela-Canon, Diana C. Rodriguez Burbano, Oscar Perdomo

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

Resumen

In recent years, the interest in protein analysis based on biomolecular features has rapidly grown. This has led to explore the use of machine learning models, as they could hold an important alternative to contribute to the problems associated to these analyses. Models as support vector machines, artificial neural networks and random forest were compared for the prediction of protein localization. Two main sources of data were used to train the models: the information from targeting signal and from the protein sequences to determine the localization sites of the protein. A third scenario with a fusion of both sources of data was employed. Four classes were established according to the subcellular localization of the protein: cytoplasm, periplasmatic space, outer and inner membranes. Results reached values between 77% and 92% in terms of balanced accuracy. The models with better performance were based on random forest and support vector machines.

Idioma original	Inglés estadounidense
Título de la publicación alojada	2022 IEEE Colombian Conference on Applications of Computational Intelligence, ColCACI 2022 - Proceedings
Editores	Alvaro David Orjuela-Canon
Editorial	Institute of Electrical and Electronics Engineers Inc.
ISBN (versión digital)	9781665474702, 978-166547470-2
ISBN (versión impresa)	9781665474702
DOI	https://doi.org/10.1109/colcaci56938.2022.9905354
Estado	Publicada - jul. 27 2022
Evento	2022 IEEE Colombian Conference on Applications of Computational Intelligence, ColCACI 2022 - Cali, Colombia Duración: jul. 27 2022 → jul. 29 2022

Serie de la publicación

Nombre	2022 IEEE Colombian Conference on Applications of Computational Intelligence (ColCACI)

Conferencia

Conferencia	2022 IEEE Colombian Conference on Applications of Computational Intelligence, ColCACI 2022
País/Territorio	Colombia
Ciudad	Cali
Período	7/27/22 → 7/29/22

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/colcaci56938.2022.9905354

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Orjuela-Canon, A. D., Rodriguez Burbano, D. C., & Perdomo, O. (2022). Data Fusion Analysis for Determining Localization of Proteins Associated to Escherichia coli. En A. D. Orjuela-Canon (Ed.), 2022 IEEE Colombian Conference on Applications of Computational Intelligence, ColCACI 2022 - Proceedings (2022 IEEE Colombian Conference on Applications of Computational Intelligence (ColCACI)). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/colcaci56938.2022.9905354

Orjuela-Canon, Alvaro David ; Rodriguez Burbano, Diana C. ; Perdomo, Oscar. / Data Fusion Analysis for Determining Localization of Proteins Associated to Escherichia coli. 2022 IEEE Colombian Conference on Applications of Computational Intelligence, ColCACI 2022 - Proceedings. editor / Alvaro David Orjuela-Canon. Institute of Electrical and Electronics Engineers Inc., 2022. (2022 IEEE Colombian Conference on Applications of Computational Intelligence (ColCACI)).

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abstract = "In recent years, the interest in protein analysis based on biomolecular features has rapidly grown. This has led to explore the use of machine learning models, as they could hold an important alternative to contribute to the problems associated to these analyses. Models as support vector machines, artificial neural networks and random forest were compared for the prediction of protein localization. Two main sources of data were used to train the models: the information from targeting signal and from the protein sequences to determine the localization sites of the protein. A third scenario with a fusion of both sources of data was employed. Four classes were established according to the subcellular localization of the protein: cytoplasm, periplasmatic space, outer and inner membranes. Results reached values between 77% and 92% in terms of balanced accuracy. The models with better performance were based on random forest and support vector machines.",

author = "Orjuela-Canon, {Alvaro David} and {Rodriguez Burbano}, {Diana C.} and Oscar Perdomo",

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Orjuela-Canon, AD , Rodriguez Burbano, DC & Perdomo, O 2022, Data Fusion Analysis for Determining Localization of Proteins Associated to Escherichia coli. En AD Orjuela-Canon (ed.), 2022 IEEE Colombian Conference on Applications of Computational Intelligence, ColCACI 2022 - Proceedings. 2022 IEEE Colombian Conference on Applications of Computational Intelligence (ColCACI), Institute of Electrical and Electronics Engineers Inc., 2022 IEEE Colombian Conference on Applications of Computational Intelligence, ColCACI 2022, Cali, Colombia, 7/27/22. https://doi.org/10.1109/colcaci56938.2022.9905354

Data Fusion Analysis for Determining Localization of Proteins Associated to Escherichia coli. / Orjuela-Canon, Alvaro David ; Rodriguez Burbano, Diana C.; Perdomo, Oscar.
2022 IEEE Colombian Conference on Applications of Computational Intelligence, ColCACI 2022 - Proceedings. ed. / Alvaro David Orjuela-Canon. Institute of Electrical and Electronics Engineers Inc., 2022. (2022 IEEE Colombian Conference on Applications of Computational Intelligence (ColCACI)).

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

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Y1 - 2022/7/27

N2 - In recent years, the interest in protein analysis based on biomolecular features has rapidly grown. This has led to explore the use of machine learning models, as they could hold an important alternative to contribute to the problems associated to these analyses. Models as support vector machines, artificial neural networks and random forest were compared for the prediction of protein localization. Two main sources of data were used to train the models: the information from targeting signal and from the protein sequences to determine the localization sites of the protein. A third scenario with a fusion of both sources of data was employed. Four classes were established according to the subcellular localization of the protein: cytoplasm, periplasmatic space, outer and inner membranes. Results reached values between 77% and 92% in terms of balanced accuracy. The models with better performance were based on random forest and support vector machines.

AB - In recent years, the interest in protein analysis based on biomolecular features has rapidly grown. This has led to explore the use of machine learning models, as they could hold an important alternative to contribute to the problems associated to these analyses. Models as support vector machines, artificial neural networks and random forest were compared for the prediction of protein localization. Two main sources of data were used to train the models: the information from targeting signal and from the protein sequences to determine the localization sites of the protein. A third scenario with a fusion of both sources of data was employed. Four classes were established according to the subcellular localization of the protein: cytoplasm, periplasmatic space, outer and inner membranes. Results reached values between 77% and 92% in terms of balanced accuracy. The models with better performance were based on random forest and support vector machines.

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Orjuela-Canon AD , Rodriguez Burbano DC, Perdomo O. Data Fusion Analysis for Determining Localization of Proteins Associated to Escherichia coli. En Orjuela-Canon AD, editor, 2022 IEEE Colombian Conference on Applications of Computational Intelligence, ColCACI 2022 - Proceedings. Institute of Electrical and Electronics Engineers Inc. 2022. (2022 IEEE Colombian Conference on Applications of Computational Intelligence (ColCACI)). doi: 10.1109/colcaci56938.2022.9905354

Data Fusion Analysis for Determining Localization of Proteins Associated to Escherichia coli

Resumen

Serie de la publicación

Conferencia

Áreas temáticas de ASJC Scopus

ODS de las Naciones Unidas

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