Automated Machine Learning Strategies to Damage Identification of Neurofibromatosis Mutations-Reference-Cited by-同舟云学术

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Automated Machine Learning Strategies to Damage Identification of Neurofibromatosis Mutations

Published:2021-12 Issue: Volume: Page:
ISSN:
Container-title:2021 20th IEEE International Conference on Machine Learning and Applications (ICMLA)
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Author:

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

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/9679834/9679948/09680043.pdf?arnumber=9680043

Reference30 articles.

1. A machine learning classifier trained on cancer transcriptomes detects NF1 inactivation signal in glioblastoma

2. DeepCLIP: predicting the effect of mutations on protein–RNA binding with deep learning

3. Tunability: importance of hyperparameters of machine learning algorithms;probst;J Mach Learn Res,2019

4. Hyperparameter tuning and performance assessment of statistical and machine-learning algorithms using spatial data

5. AutoGenome: an autoML tool for genomic research;liu;BioRxiv,2019

Cited by 2 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Escherichia coli: Analysis of Features for Protein Localization Classification Employing Fusion Data;Applications of Computational Intelligence;2023

2. Data Fusion Analysis for Determining Localization of Proteins Associated to Escherichia coli;2022 IEEE Colombian Conference on Applications of Computational Intelligence (ColCACI);2022-07-27

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