OmicEra Diagnostics GmbH, 82152 Planegg, Germany.
Novo Nordisk Foundation Center for Protein Research, University of Copenhagen, 2200 Copenhagen, Denmark.
J Proteome Res. 2023 Feb 3;22(2):359-367. doi: 10.1021/acs.jproteome.2c00473. Epub 2022 Nov 25.
Biomarkers are of central importance for assessing the health state and to guide medical interventions and their efficacy; still, they are lacking for most diseases. Mass spectrometry (MS)-based proteomics is a powerful technology for biomarker discovery but requires sophisticated bioinformatics to identify robust patterns. Machine learning (ML) has become a promising tool for this purpose. However, it is sometimes applied in an opaque manner and generally requires specialized knowledge. To enable easy access to ML for biomarker discovery without any programming or bioinformatics skills, we developed "OmicLearn" (http://OmicLearn.org), an open-source browser-based ML tool using the latest advances in the Python ML ecosystem. Data matrices from omics experiments are easily uploaded to an online or a locally installed web server. OmicLearn enables rapid exploration of the suitability of various ML algorithms for the experimental data sets. It fosters open science via transparent assessment of state-of-the-art algorithms in a standardized format for proteomics and other omics sciences.
生物标志物对于评估健康状态和指导医学干预及其疗效至关重要；然而，它们在大多数疾病中都缺乏。基于质谱（MS）的蛋白质组学是一种强大的生物标志物发现技术，但需要复杂的生物信息学来识别稳健的模式。机器学习（ML）已成为一种有前途的工具。然而，它有时以不透明的方式应用，并且通常需要专门的知识。为了能够在无需任何编程或生物信息学技能的情况下轻松访问用于生物标志物发现的 ML，我们开发了“OmicLearn”（http://OmicLearn.org），这是一个基于浏览器的开源 ML 工具，使用了 Python ML 生态系统中的最新进展。来自组学实验的数据矩阵可以轻松地上传到在线或本地安装的 Web 服务器。OmicLearn 能够快速探索各种 ML 算法对实验数据集的适用性。它通过以标准化格式对蛋白质组学和其他组学科学的最先进算法进行透明评估，促进了开放科学。
