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Automated Machine Learning Strategies to Damage Identification of Neurofibromatosis Mutations
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SYSNO ASEP 0556734 Document Type C - Proceedings Paper (int. conf.) R&D Document Type Conference Paper Title Automated Machine Learning Strategies to Damage Identification of Neurofibromatosis Mutations Author(s) Orjuela-Cañón, A. D. (CO)
Figueroa-Garcia, J.C. (CO)
Neruda, Roman (UIVT-O) SAI, RID, ORCIDNumber of authors 3 Source Title Proceedings of 20th IEEE International Conference on Machine Learning and Applications ICMLA 2021. - Piscataway : IEEE, 2021 / Wani M. A. ; Sethi I. ; Shi W. ; Qu G. ; Raicu D. S. ; Jin R. - ISBN 978-1-6654-4338-8 Pages s. 1341-1344 Number of pages 4 s. Publication form Print - P Action ICMLA 2021: IEEE International Conference on Machine Learning and Applications /20./ Event date 13.12.2021 - 16.12.2021 VEvent location Pasadena / Virtual Country US - United States Event type WRD Language eng - English Country US - United States 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) Institutional support UIVT-O - RVO:67985807 UT WOS 000779208200209 EID SCOPUS 85125839620 DOI 10.1109/ICMLA52953.2021.00217 Annotation 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. Workplace Institute of Computer Science Contact Tereza Šírová, sirova@cs.cas.cz, Tel.: 266 053 800 Year of Publishing 2023 Electronic address http://dx.doi.org/10.1109/ICMLA52953.2021.00217
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