水准点(测量)
计算机科学
蛋白质功能预测
机器学习
代表(政治)
人工智能
相似性(几何)
蛋白质测序
蛋白质法
任务(项目管理)
集合(抽象数据类型)
特征学习
编码
嵌入
特征向量
结构生物信息学
序列(生物学)
蛋白质功能
蛋白质结构
序列分析
肽序列
生物
图像(数学)
基因
政治
经济
管理
程序设计语言
法学
地理
生物化学
遗传学
政治学
大地测量学
作者
Emilio Fenoy,Alejando A Edera,Georgina Stegmayer
摘要
A representation method is an algorithm that calculates numerical feature vectors for samples in a dataset. Such vectors, also known as embeddings, define a relatively low-dimensional space able to efficiently encode high-dimensional data. Very recently, many types of learned data representations based on machine learning have appeared and are being applied to several tasks in bioinformatics. In particular, protein representation learning methods integrate different types of protein information (sequence, domains, etc.), in supervised or unsupervised learning approaches, and provide embeddings of protein sequences that can be used for downstream tasks. One task that is of special interest is the automatic function prediction of the huge number of novel proteins that are being discovered nowadays and are still totally uncharacterized. However, despite its importance, up to date there is not a fair benchmark study of the predictive performance of existing proposals on the same large set of proteins and for very concrete and common bioinformatics tasks. Therefore, this lack of benchmark studies prevent the community from using adequate predictive methods for accelerating the functional characterization of proteins. In this study, we performed a detailed comparison of protein sequence representation learning methods, explaining each approach and comparing them with an experimental benchmark on several bioinformatics tasks: (i) determining protein sequence similarity in the embedding space; (ii) inferring protein domains and (iii) predicting ontology-based protein functions. We examine the advantages and disadvantages of each representation approach over the benchmark results. We hope the results and the discussion of this study can help the community to select the most adequate machine learning-based technique for protein representation according to the bioinformatics task at hand.
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