CIF: Small: Novel biologically inspired methods for analyzing multilayer networks

CIF：小型：用于分析多层网络的受生物学启发的新颖方法

• 批准号: 2111679
• 负责人: Tamer Kahveci
• 金额: $ 33.12万
• 依托单位: University of Florida
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-10-01 至 2024-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2111679&HistoricalAwards=false
• 关键词: CIF; Small; Novel; biologically; inspired

Complex systems are often organized as multiple networks of interactions, where different characteristics such as interaction mechanisms or external perturbations govern the topology of each network. Civil infrastructures, social networks, producer/consumer/retailer networks are just a few examples to these systems. Each network in such a system shows how entities interact with each other under certain premises and conditions, while the interactions across different networks show how different conditions affect the entire system. The investigator calls such complex systems multilayer networks. To understand how these systems work, it is of utmost importance to consider the entire system of networks holistically, rather than each network at different layers independently as they collectively describe the functionality of the underlying system. The main objective of this project is to develop the fundamental tools needed to study multilayer networks, which requires creation of novel computational techniques for motif identification and comparative analysis of such networks. Biological networks, such as cellular networks, are naturally organized in multiple layers, where each layer describes the interaction topology among a set of molecules under a unique set of restrictions, such as external or internal stress condition, cell type, developmental stage, and interaction type. Thus, using biological systems as a network model provides opportunities to describe and study complex network systems.Computational analysis of interactions among different molecules organized as a biological network is a computationally interesting and difficult problem. Studying collections of such networks through multilayer networks introduces further challenges as (i) the interaction topologies as well as the interaction types among molecules may vary across different layers, and (ii) networks at different layers of a multilayer network may interact as they may share some molecules or the molecules at different layers may affect each other. Following from these observations, the PI is tackling the below two goals to achieve the main objective. (1) Identify building blocks in multilayer networks. (2) Develop tools for comparative analysis of multilayer networks. Both motif identification and comparative network analysis problems for classical single layer networks have been considered in the literature for over a decade. There are however very limited studies which address these challenges for complex multilayer systems. The methods developed in this project are combining and extending the theory and the algorithms for fundamental graph theory, computational biology, bioinformatics, and machine learning to achieve these objectives.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

复杂的系统通常被组织为多个相互作用网络，其中不同的特征（例如相互作用机制或外部扰动）控制着每个网络的拓扑。民用基础设施，社交网络，生产者/消费者/零售商网络只是这些系统的几个示例。这样的系统中的每个网络都显示了实体在某些前提和条件下如何相互作用，而不同网络之间的交互表示不同条件如何影响整个系统。研究人员称这种复杂的系统多层网络。要了解这些系统的工作原理，最重要的是，整体上考虑整个网络的整个系统，而不是独立地在不同层上的每个网络，因为它们共同描述了基础系统的功能。该项目的主要目的是开发研究多层网络所需的基本工具，该工具需要创建新颖的计算技术，以识别图案识别和对此类网络的比较分析。生物网络（例如细胞网络）是自然组织的，在多层中，每一层都在一组唯一的限制集中描述一组分子之间的相互作用拓扑，例如外部或内部应力条件，细胞类型，发育阶段和相互作用类型。因此，使用生物系统作为网络模型提供了描述和研究复杂网络系统的机会。对作为生物网络组织的不同分子之间的相互作用进行分析是一个计算有趣且困难的问题。通过多层网络研究此类网络的集合引入了进一步的挑战，因为（i）相互作用拓扑以及分子之间的相互作用类型可能会在不同层的不同层上有所不同，并且（ii）多层网络的不同层的网络可能会相互作用，因为它们可能共享某些分子或不同层的分子在不同层上可能会影响彼此。从这些观察结果来看，PI正在解决以下两个目标以实现主要目标。 （1）确定多层网络中的构建块。 （2）开发用于多层网络比较分析的工具。十多年来，文献中已经考虑了经典单层网络的基础标识和比较网络分析问题。但是，对于复杂的多层系统，有非常有限的研究解决了这些挑战。 该项目开发的方法是结合和扩展基本图理论，计算生物学，生物信息学和机器学习的理论和算法，以实现这些目标。该奖项反映了NSF的法定任务，并被认为是值得通过基金会的知识智能和更广泛的影响来通过评估来支持的。

项目成果

Optimal Supervised Reduction of High Dimensional Transcription Data

• DOI: 10.1109/tcbb.2023.3280557
• 发表时间: 2023-09-01
• 期刊: IEEE-ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS
• 影响因子: 4.5
• 作者: Bailey，Richard;Sarkar，Aisharjya;Kahveci，Tamer
• 通讯作者: Kahveci，Tamer

FiT: fiber-based tensor completion for drug repurposing

• DOI: 10.1145/3535508.3545527
• 发表时间: 2022-08
• 期刊: Proceedings of the 13th ACM International Conference on Bioinformatics, Computational Biology and Health Informatics
• 作者: Aysegül Bumin;Anna M. Ritz;D. Slonim;Tamer Kahveci;Kejun Huang

Pattern Discovery in Multilayer Networks

• DOI: 10.1109/tcbb.2021.3105001
• 发表时间: 2022-03-01
• 期刊: IEEE-ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS
• 影响因子: 4.5
• 作者: Ren, Yuanfang;Sarkar, Aisharjya;Kahveci, Tamer
• 通讯作者: Kahveci, Tamer

Identification of co-existing embeddings of a motif in multilayer networks

• DOI: 10.1145/3535508.3545528
• 发表时间: 2022-08
• 期刊: Proceedings of the 13th ACM International Conference on Bioinformatics, Computational Biology and Health Informatics
• 作者: Yuanfang Ren;Aisharjya Sarkar;Aysegül Bumin;Kejun Huang;P. Veltri;Alin Dobra;Tamer Kahveci

Vulture: VULnerabilities in impuTing drUg REsistance

• DOI: 10.1145/3584371.3612993
• 发表时间: 2023-09
• 期刊: Proceedings of the 14th ACM International Conference on Bioinformatics, Computational Biology, and Health Informatics
• 作者: Aysegül Bumin;Megan Shah;Kejun Huang;Tamer Kahveci