Department Of Computational Mathematics, Science and Engineering, Michigan State University, East Lansing, MI 48824, USA.
Data Management and Analytics, IT Services, Michigan State University, East Lansing, MI 48824, USA.
Nucleic Acids Res. 2022 Jul 5;50(W1):W358-W366. doi: 10.1093/nar/gkac335.
Biomedical researchers take advantage of high-throughput, high-coverage technologies to routinely generate sets of genes of interest across a wide range of biological conditions. Although these technologies have directly shed light on the molecular underpinnings of various biological processes and diseases, the list of genes from any individual experiment is often noisy and incomplete. Additionally, interpreting these lists of genes can be challenging in terms of how they are related to each other and to other genes in the genome. In this work, we present GenePlexus (https://www.geneplexus.net/), a web-server that allows a researcher to utilize a powerful, network-based machine learning method to gain insights into their gene set of interest and additional functionally similar genes. Once a user uploads their own set of human genes and chooses between a number of different human network representations, GenePlexus provides predictions of how associated every gene in the network is to the input set. The web-server also provides interpretability through network visualization and comparison to other machine learning models trained on thousands of known process/pathway and disease gene sets. GenePlexus is free and open to all users without the need for registration.
生物医学研究人员利用高通量、高覆盖技术，常规性地生成一系列在广泛生物学条件下感兴趣的基因。尽管这些技术直接揭示了各种生物过程和疾病的分子基础，但来自任何单个实验的基因列表通常是嘈杂和不完整的。此外，解释这些基因列表可能具有挑战性，因为它们彼此以及与基因组中的其他基因之间的关系。在这项工作中，我们提出了 GenePlexus（https://www.geneplexus.net/），这是一个网络服务器，允许研究人员利用强大的基于网络的机器学习方法来深入了解他们感兴趣的基因集和其他功能相似的基因。一旦用户上传他们自己的一组人类基因，并在多种不同的人类网络表示之间进行选择，GenePlexus 就会提供网络中每个基因与输入集的关联程度的预测。该网络服务器还通过网络可视化和与数千个已知过程/途径和疾病基因集训练的其他机器学习模型的比较提供可解释性。GenePlexus 是免费的，对所有用户开放，无需注册。
