发帖
雷达卡
摘要翻译：
现代生物学经常依赖机器学习来提供预测和改进决策过程。最近有人呼吁对机器学习的性能和可能的限制进行更多的审查。在这里，我们提出了一组社区范围的建议，旨在帮助建立生物学中有监督机器学习验证的标准。采用基于数据、优化、模型、评估(DOME)的结构化机器学习方法描述，将有助于评论者和读者更好地理解和评估方法或结果的性能和局限性。这些建议是向任何希望实现机器学习算法的人提出的问题。这些问题的答案可以很容易地包含在已发表论文的补充材料中。
---
英文标题：
《DOME: Recommendations for supervised machine learning validation in
biology》
---
作者：
Ian Walsh, Dmytro Fishman, Dario Garcia-Gasulla, Tiina Titma, Gianluca
Pollastri, The ELIXIR Machine Learning focus group, Jen Harrow, Fotis E.
Psomopoulos and Silvio C.E. Tosatto
---
最新提交年份：
2021
---
分类信息：
一级分类：Quantitative Biology 数量生物学
二级分类：Other Quantitative Biology 其他定量生物学
分类描述：Work in quantitative biology that does not fit into the other q-bio classifications
不适合其他q-bio分类的定量生物学工作
--
一级分类：Computer Science 计算机科学
二级分类：Machine Learning 机器学习
分类描述：Papers on all aspects of machine learning research (supervised, unsupervised, reinforcement learning, bandit problems, and so on) including also robustness, explanation, fairness, and methodology. cs.LG is also an appropriate primary category for applications of machine learning methods.
关于机器学习研究的所有方面的论文（有监督的，无监督的，强化学习，强盗问题，等等），包括健壮性，解释性，公平性和方法论。对于机器学习方法的应用，CS.LG也是一个合适的主要类别。
--
---
英文摘要：
Modern biology frequently relies on machine learning to provide predictions and improve decision processes. There have been recent calls for more scrutiny on machine learning performance and possible limitations. Here we present a set of community-wide recommendations aiming to help establish standards of supervised machine learning validation in biology. Adopting a structured methods description for machine learning based on data, optimization, model, evaluation (DOME) will aim to help both reviewers and readers to better understand and assess the performance and limitations of a method or outcome. The recommendations are formulated as questions to anyone wishing to pursue implementation of a machine learning algorithm. Answers to these questions can be easily included in the supplementary material of published papers.
---
PDF链接：
https://arxiv.org/pdf/2006.16189
京公网安备 11010802022788号
