Automated Machine Learning Strategies to Damage Identification of Neurofibromatosis Mutations

Alvaro David Orjuela-Canon, Juan Carlos Figueroa-Garcia, Roman Neruda

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Machine learning tools have been employed for problem solutions in bioinformatics. However, the parameters tuning of these models cam imply additional difficulties around the specific technique used to classify. In this work data from protein sequences was applied to three auto machine learning strategies to determine the type of mutation for the Neurofibromatosis disease. Results show that the parameters in the machine learning models were found automatically. In addition, these tools were relevant to determine relations between the amino-acids in the protein sequence.

Original languageEnglish (US)
Title of host publicationProceedings - 20th IEEE International Conference on Machine Learning and Applications, ICMLA 2021
EditorsM. Arif Wani, Ishwar K. Sethi, Weisong Shi, Guangzhi Qu, Daniela Stan Raicu, Ruoming Jin
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages1341-1344
Number of pages4
ISBN (Electronic)9781665443371
DOIs
StatePublished - Jan 25 2021
Event20th IEEE International Conference on Machine Learning and Applications, ICMLA 2021 - Virtual, Online, United States
Duration: Dec 13 2021Dec 16 2021

Publication series

NameProceedings - 20th IEEE International Conference on Machine Learning and Applications, ICMLA 2021

Conference

Conference20th IEEE International Conference on Machine Learning and Applications, ICMLA 2021
Country/TerritoryUnited States
CityVirtual, Online
Period12/13/2112/16/21

All Science Journal Classification (ASJC) codes

  • Safety, Risk, Reliability and Quality
  • Health Informatics
  • Artificial Intelligence
  • Computer Science Applications

Author Keywords

  • Concept

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