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GraphCrowd: Using Crowdsourced Design to Visualize Effects of Environmental Chemicals on Signaling Networks

GraphCrowd：使用众包设计可视化环境化学品对信号网络的影响

基本信息

批准号：
9330822
负责人：
Kurt Luther
金额：
$ 30.19万
依托单位：
VIRGINIA POLYTECHNIC INST AND ST UNIV
依托单位国家：
美国
项目类别：
财政年份：
2016
资助国家：
美国
起止时间：
2016-08-11 至 2019-07-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/9330822
关键词：
Address Affect Algorithms Apoptosis Biological Biological Assay Biology Cell Cycle Cells Chemicals Complex Computational Technique Computer software Computing Methodologies Creativeness Crowding Cues Data Databases Effectiveness Exposure to Face Feedback Geography Graph Health Hepatocyte Human Imagery Intuition Knowledge Lead Life Location Long-Term Effects Manuals Mechanics Methods Molecular Online Systems Organ Output Pathway interactions Poison Positioning Attribute Process Proteins Reaction Research Scientist Seeds Signal Transduction Societies System Techniques Technology Testing Time Toxic effect United States Environmental Protection Agency Visual Work biomedical scientist citizen science college crowdsourcing design environmental chemical experience experimental study improved iterative design man prevent protein metabolite public health relevance receptor response volunteer

项目摘要

DESCRIPTION (provided by applicant): Humans are repeatedly exposed to many man-made chemicals, and the long-term effects of most of these chemical on human health are unknown. A starting point to improve our understanding is to determine how the addition of a chemical affects the processes that occur within cells. These processes are composed of tens of thousands of proteins and different types of interactions among them that form complex networks. Computational methods can analyze these networks in combination with experimental data on a chemical to determine the sub-network that is perturbed within the cell by that chemical. Such "chemical response networks" can be visualized in displays where each protein receives a location (x and y coordinates) such that proteins connected by an interaction are closely placed. There are many different ways to automatically assign these locations. Several software packages and web-based systems implement these graph layout algorithms. However, it is often very difﬁcult to interpret these networks since the algorithms that determine these locations have very little knowledge of the underlying biology. Biologists often manually manipulate initial, automatically determined positions until the network conﬁguration matches their knowledge and intuition, but this process is time-consuming and requires signiﬁcant expertise. In this application, we seek to develop an online system that enables large, geographically distributed groups of non-experts (crowd users) to manipulate chemical response networks in order to make their visualized layouts meaningful to expert scientists. Our system will use automatically generated biological cues to guide the users on where to place nodes and edges, how to group them, and which nodes/edges to ignore. It will also enable users to highlight sequences of interactions that suggest how a chemical may control speciﬁc proteins and processes that occur in the cell. Our system will develop layouts through an iterative design process where users serve as barnstormers (generate several layout ideas), critics (offer principled feedback on layouts), and synthesizers (combine the best ideas from multiple layouts). We will employ three different types of crowd users: paid workers on Amazon Mechanical Turk, volunteer citizen scientists from Zooniverse, and college biology majors. We will perform extensive studies to evaluate the effectiveness of each group in creating graph layouts. We will also conduct a review by an expert panel of biomedical scientists to evaluate the effectiveness of our system in creating visualizations of chemical response networks that are valuable to experts and can assist them in formulating biological hypotheses.

描述（由申请人提供）：人类反复暴露于许多人造化学品，这些化学品中的大多数对人类健康的长期影响尚不清楚。提高我们理解的一个起点是确定添加化学物质如何影响细胞内发生的过程。这些过程由成千上万的蛋白质和它们之间形成复杂网络的不同类型的相互作用组成。计算方法可以结合化学品的实验数据来分析这些网络，以确定细胞内被该化学品扰动的子网络。这种“化学反应网络”可以在显示器中可视化，其中每个蛋白质接收位置（x和y坐标），使得通过相互作用连接的蛋白质被紧密放置。有许多不同的方法可以自动分配这些位置。几个软件包和基于Web的系统实现这些图形布局算法。然而，解释这些网络通常非常困难，因为确定这些位置的算法对底层生物学知之甚少。生物学家经常手动操作初始的、自动确定的位置，直到网络配置与他们的知识和直觉相匹配，但这个过程非常耗时，需要大量的专业知识。在这个应用程序中，我们寻求开发一个在线系统，使大型的，地理分布的非专家（人群用户）组来操纵化学反应网络，以使他们的可视化布局有意义的专家科学家。我们的系统将使用自动生成的生物线索来指导用户在哪里放置节点和边，如何将它们分组，以及忽略哪些节点/边。它还将使用户能够突出显示相互作用的序列，这些序列表明化学物质如何控制细胞中发生的特定蛋白质和过程。我们的系统将通过一个迭代的设计过程来开发布局，在这个过程中，用户充当barnstormers（产生几个布局想法），评论家（提供关于布局的原则性反馈）和合成器（联合收割机从多个布局中组合最好的想法）。我们将雇佣三种不同类型的人群用户：亚马逊土耳其机器人的付费员工，Zooniverse的志愿公民科学家，以及大学生物学专业的学生。我们将进行广泛的研究，以评估每个小组在创建图形布局方面的有效性。我们还将由生物医学科学家组成的专家小组进行审查，以评估我们的系统在创建对专家有价值的化学反应网络可视化方面的有效性，并帮助他们制定生物学假设。