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arXiv:2203.04676 (stat)
[Submitted on 9 Mar 2022 ]

Title: SparseChem: Fast and accurate machine learning model for small molecules

Title: SparseChem:小分子的快速且准确的机器学习模型

Authors:Adam Arany, Jaak Simm, Martijn Oldenhof, Yves Moreau
Abstract: SparseChem provides fast and accurate machine learning models for biochemical applications. Especially, the package supports very high-dimensional sparse inputs, e.g., millions of features and millions of compounds. It is possible to train classification, regression and censored regression models, or combination of them from command line. Additionally, the library can be accessed directly from Python. Source code and documentation is freely available under MIT License on GitHub.
Abstract: 稀疏化学为生物化学应用提供了快速且准确的机器学习模型。 特别是,该软件包支持非常高维的稀疏输入,例如数百万个特征和数百万种化合物。 可以从命令行训练分类、回归和截断回归模型,或者它们的组合。 此外,可以直接从 Python 访问该库。 源代码和文档在 GitHub 上以 MIT 许可证免费提供。
Subjects: Machine Learning (stat.ML) ; Machine Learning (cs.LG)
Cite as: arXiv:2203.04676 [stat.ML]
  (or arXiv:2203.04676v1 [stat.ML] for this version)
  https://doi.org/10.48550/arXiv.2203.04676
arXiv-issued DOI via DataCite

Submission history

From: Martijn Oldenhof [view email]
[v1] Wed, 9 Mar 2022 12:40:35 UTC (13 KB)
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