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SENT: semantic features in text
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AI
DOI：10.1093/nar/gkp392.png)
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摘要
En 中文
We present SENT (semantic features in text), a functional interpretation tool based on literature analysis. SENT uses Non-negative Matrix Factorization to identify topics in the scientific articles related to a collection of genes or their products, and use them to group and summarize these genes. In addition, the application allows users to rank and explore the articles that best relate to the topics found, helping put the analysis results into context. This approach is useful as an exploratory step in the workflow of interpreting and understanding experimental data, shedding some light into the complex underlying biological mechanisms. This tool provides a user-friendly interface via a web site, and a programmatic access via a SOAP web server. SENT is freely accessible at http:\\sent.dacya.ucm.es.
Keyword:
NONNEGATIVE MATRIX FACTORIZATION
BIOMEDICAL LITERATURE
MICROARRAY DATA
GENE LISTS
INITIALIZATION
NETWORK
TOOL
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
IF:
13.1
论文数:
3.6W
被引数:
29.0W
机构
引用论文
Bioinformatics enrichment tools: paths toward the comprehensive functional analysis of large gene lists生物信息学富集工具: 通往大型基因列表综合功能分析的路径.png)
NUCLEIC ACIDS RESEARCHOAAI
NUCLEIC ACIDS RESEARCHIF13.1
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