Automated Machine Learning Strategies to Damage Identification of Neurofibromatosis Mutations

0556734 - ÚI 2023 RIV US eng C - Conference Paper (international conference)
Orjuela-Cañón, A. D. - Figueroa-Garcia, J.C. - Neruda, Roman
Automated Machine Learning Strategies to Damage Identification of Neurofibromatosis Mutations.
Proceedings of 20th IEEE International Conference on Machine Learning and Applications ICMLA 2021. Piscataway: IEEE, 2021 - (Wani, M.; Sethi, I.; Shi, W.; Qu, G.; Raicu, D.; Jin, R.), s. 1341-1344. ISBN 978-1-6654-4338-8.
[ICMLA 2021: IEEE International Conference on Machine Learning and Applications /20./. Pasadena / Virtual (US), 13.12.2021-16.12.2021]
Institutional support: RVO:67985807
Keywords : automatic machine learning * protein sequence * neurofibromatosis * amino-acids
OECD category: Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)
http://dx.doi.org/10.1109/ICMLA52953.2021.00217

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.
Permanent Link: http://hdl.handle.net/11104/0330895