推论
背景(考古学)
计算机科学
基因调控网络
数据挖掘
计算生物学
机器学习
人工智能
基因
生物
遗传学
基因表达
古生物学
作者
Adrián Segura-Ortiz,José García-Nieto,José F. Aldana‐Montes,Ismael Navas‐Delgado
标识
DOI:10.1016/j.compbiomed.2024.108850
摘要
MO-GENECI has not only demonstrated achieving more accurate results than individual techniques, but has also overcome the uncertainty associated with the initial choice due to its flexibility and adaptability. It is shown intelligently to select the most suitable techniques for each case. The source code is hosted in a public repository at GitHub under MIT license: https://github.com/AdrianSeguraOrtiz/MO-GENECI. Moreover, to facilitate its installation and use, the software associated with this implementation has been encapsulated in a Python package available at PyPI: https://pypi.org/project/geneci/.
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