.png){kind=icon}
返回
BayesPiles: Visualisation Support for Bayesian Network Structure Learning
.png){kind=icon}
OA
AI
DOI：10.1145/3230623.png)
.png){kind=icon}
.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
We address the problem of exploring, combining, and comparing large collections of scored, directed networks for understanding inferred Bayesian networks used in biology. In this field, heuristic algorithms explore the space of possible network solutions, sampling this space based on algorithm parameters and a network score that encodes the statistical fit to the data. The goal of the analyst is to guide the heuristic search and decide how to determine a final consensus network structure, usually by selecting the top-scoring network or constructing the consensus network from a collection of high-scoring networks. BayesPiles, our visualisation tool, helps with understanding the structure of the solution space and supporting the construction of a final consensus network that is representative of the underlying dataset. BayesPiles builds upon and extends MultiPiles to meet our domain requirements. We developed BayesPiles in conjunction with computational biologists who have used this tool on data sets used in their research. The biologists found our solution provides them with new insights and helps them achieve results that are representative of the underlying data.
Keyword:
Visualisation
graphs
bioinformatics
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:
6.6
论文数:
1.5K
被引数:
6.2K
机构
引用论文
分享收藏分享收藏分享收藏In situ direct observation of photocorrosion in ZnO crystals in ionic liquid using a laser-equipped high-voltage electron microscope使用配备激光的高压电子显微镜原位直接观察离子液体中ZnO晶体的光腐蚀.png)
AIP AdvancesOAAI
AIP AdvancesIF0
OAAI分享收藏
查看更多内容