LEXAS: a web application for life science experiment search and suggestion
LEXAS: a web application for life science experiment search and suggestion
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LEXAS：生命科学实验搜索和建议的网络应用程序
DOI：
10.1101/2021.12.05.471323
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发表时间：
2021
期刊：
影响因子：
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通讯作者：
Daiju Kitagawa
中科院分区：
文献类型：
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作者：
Kei K Ito;Yoshimasa Tsuruoka;Daiju Kitagawa
动机在细胞生物学中，研究人员通过阅读相关文章并考虑所描述的实验和结果来设计湿实验。今天，研究人员花了很长时间探索文献，以计划experiments.ResultsTo加速实验规划，我们已经开发了一个Web应用程序名为LEXAS（生命科学实验seArch和建议）。LEXAS策划了生物医学实验的描述，并建议下一步可以进行的基因实验。为了开发LEXAS，我们首先从PubMed Central中存档的全文生物医学文章中检索实验描述。使用这些检索到的实验和生物医学知识库和数据库，我们训练了一个机器学习模型，该模型建议了接下来的实验。该模型不仅可以建议合理的基因，而且还可以建议新的基因作为下一个实验的目标，只要它们与感兴趣的基因共享一些关键特征。可用性和实施LEXAS可在https://lexas.f.u-tokyo.ac.jp/上获得，并为用户提供两个界面：搜索和建议。搜索界面允许用户找到实验描述的综合列表，并且建议界面允许用户找到可以与可能的实验方法一起沿着分析的基因列表。源代码可在https://github.com/lexas-f-utokyo/lexas.Contactito-delightfully-kei@g.ecc.u-tokyo.ac.jpSupplementary上获得。补充数据可在生物信息学在线上获得。
MotivationIn cellular biology, researchers design wet experiments by reading the relevant articles and considering the described experiments and results. Today, researchers spend a long time exploring the literature in order to plan experiments.ResultsTo accelerate experiment planning, we have developed a web application named LEXAS (Life-science EXperiment seArch and Suggestion). LEXAS curates the description of biomedical experiments and suggests the experiments on genes that could be performed next. To develop LEXAS, we first retrieved the descriptions of experiments from full-text biomedical articles archived in PubMed Central. Using these retrieved experiments and biomedical knowledgebases and databases, we trained a machine learning model that suggests the next experiments. This model can suggest not only reasonable genes but also novel genes as targets for the next experiment as long as they share some critical features with the gene of interest.Availability and implementationLEXAS is available at https://lexas.f.u-tokyo.ac.jp/ and provides users with two interfaces: search and suggestion. The search interface allows users to find a comprehensive list of experiment descriptions, and the suggestion interface allows users to find a list of genes that could be analyzed along with possible experiment methods. The source code is available at https://github.com/lexas-f-utokyo/lexas.Contactito-delightfully-kei@g.ecc.u-tokyo.ac.jpSupplementary informationSupplementary data are available at Bioinformatics online.