Constructing benchmark test sets for biological sequence analysis using independent set algorithms

Author:

Petti Samantha N.ORCID,Eddy Sean R.ORCID

Abstract

AbstractStatistical inference and machine learning methods are benchmarked on test data independent of the data used to train the method. Biological sequence families are highly non-independent because they are related by evolution, so the strategy for splitting data into separate training and test sets is a nontrivial choice in benchmarking sequence analysis methods. A random split is insufficient because it will yield test sequences that are closely related or even identical to training sequences. Adapting ideas from independent set graph algorithms, we describe two new methods for splitting sequence data into dissimilar training and test sets. These algorithms input a sequence family and produce a split in which each test sequence is less than p% identical to any individual training sequence. These algorithms successfully split more families than a previous approach, enabling construction of more diverse benchmark datasets.

Publisher

Cold Spring Harbor Laboratory

Reference21 articles.

1. Protein sequence comparison and fold recognition: progress and good-practice benchmarking

2. Correct Machine Learning on Protein Sequences: A Peer-Reviewing Perspective;Brief Bioinform,2015

3. Setting the Standards for Machine Learning in Biology;Nat Rev Mol Cell Bio,2019

4. Walsh I , Fishman D , Garcia-Gasulla D , Titma T , Pollastri G , ELIXIR Machine Learning Focus Group, et al. DOME: Recommendations for Supervised Machine Learning Validation in Biology. Nat Methods. 2021;p. https://doi.org/10.1038/s41592-021-01205-4.

5. Arpit D , Jastrzebski S , Ballas N , Krueger D , Bengio E , Kanwal MS , et al. A closer look at memorization in deep networks. In: Proc Int Conf Mach Learn. Proc Mach Learn Res; 2017. p. 233–242.

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

同舟云学术

1.学者识别学者识别

2.学术分析学术分析

3.人才评估人才评估

"同舟云学术"是以全球学者为主线,采集、加工和组织学术论文而形成的新型学术文献查询和分析系统,可以对全球学者进行文献检索和人才价值评估。用户可以通过关注某些学科领域的顶尖人物而持续追踪该领域的学科进展和研究前沿。经过近期的数据扩容,当前同舟云学术共收录了国内外主流学术期刊6万余种,收集的期刊论文及会议论文总量共计约1.5亿篇,并以每天添加12000余篇中外论文的速度递增。我们也可以为用户提供个性化、定制化的学者数据。欢迎来电咨询!咨询电话:010-8811{复制后删除}0370

www.globalauthorid.com

TOP

Copyright © 2019-2024 北京同舟云网络信息技术有限公司
京公网安备11010802033243号  京ICP备18003416号-3