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Mining Multi-Layer Protein-Protein Association Networks: An Integrated Spectral Approach

挖掘多层蛋白质-蛋白质关联网络：综合光谱方法

基本信息

批准号：
1812503
负责人：
Lenore Cowen
金额：
$ 21万
依托单位：
Tufts University
依托单位国家：
美国
项目类别：
Standard Grant
财政年份：
2018
资助国家：
美国
起止时间：
2018-08-01 至 2022-07-31
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=1812503&HistoricalAwards=false
关键词：
Mining Multi Layer Protein Association

项目摘要

This project is focused on strategies which help us to obtain information for associated pairs of proteins or genes in biological systems. In addition to the collection of lots of information about the role of different genes or proteins in the cell, there is also increasing information about pairs of proteins or genes that are related, either because there is evidence that they cooperate in the cell, or evidence that they otherwise have common attributes. The information about associated pairs can be mathematically described as a heterogeneous collection of networks, but designing efficient and effective machine learning and computational mathematics algorithms to integrate the diverse information sources to explore and make sense of these networks is a difficult unsolved problem. The new mathematical methods that will be developed in this project will be customized for the computational biologists and systems biologists who would like to use network analysis to boost the statistical significance of the signal of important genes and pathways in their data, with applications to gene function prediction, and the identification of sets of genes that are important in complex diseases such as type II diabetes and Crohn's disease. The project supports one graduate student and two undergraduate students. Through training and collaborating with investigators and other experts in the field, they will become involved in the broader research communities of scientific computing and biology.Effective and efficient inference and computational methods will be developed, analyzed, and implemented for mining multi-layer PPI networks via an integrated spectral approach and the generalizations of diffusion-based distance metrics. More precisely, spectral multilayer analysis methods based on dimension reduction and multilevel optimization methods will be designed in order to provide high-quality integration tools of multiple networks that can be used to mine this massive graph collection. The methods will be benchmarked and tested on a substantial new biological network testbed, connected with the recent DREAM disease module identification challenge. Furthermore, implementations of the tools will be made generally available to the community, for mining heterogeneous network collections in general, which will lead to new insights related to core problems in computational biology, including the identification of disease modules within the datasets.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.

该项目的重点是帮助我们获得生物系统中相关蛋白质或基因对的信息的策略。除了收集大量关于不同基因或蛋白质在细胞中的作用的信息之外，还有越来越多的关于相关蛋白质或基因对的信息，因为有证据表明它们在细胞中合作，或者有证据表明它们具有共同的属性。关于关联对的信息可以在数学上描述为网络的异构集合，但是设计高效和有效的机器学习和计算数学算法来整合不同的信息源以探索和理解这些网络是一个困难的未解决的问题。该项目将开发的新数学方法将为计算生物学家和系统生物学家定制，他们希望使用网络分析来提高数据中重要基因和途径信号的统计意义，并应用于基因功能预测，以及识别在复杂疾病中重要的基因组，如II型糖尿病和克罗恩病。该项目支持一名研究生和两名本科生。通过与该领域的研究人员和其他专家的培训和合作，他们将参与科学计算和生物学的更广泛的研究社区。有效和高效的推理和计算方法将被开发，分析和实施，用于通过集成的光谱方法和基于扩散的距离度量的推广来挖掘多层PPI网络。更准确地说，基于降维和多级优化方法的光谱多层分析方法将被设计，以提供高质量的多个网络的集成工具，可以用来挖掘这个庞大的图集合。这些方法将在一个新的生物网络测试平台上进行基准测试和测试，该平台与最近的DREAM疾病模块识别挑战有关。此外，这些工具的实现将普遍提供给社区，用于挖掘一般的异构网络集合，这将导致与计算生物学中的核心问题相关的新见解，该奖项反映了NSF的法定使命，并被认为值得通过利用基金会的知识价值和更广泛的影响进行评估来支持审查标准。

项目成果

期刊论文数量（13）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

WELL-POSEDNESS AND DISCRETIZATION FOR A CLASS OF MODELS FOR MIXED-DIMENSIONAL PROBLEMS WITH HIGH-DIMENSIONAL GAP

DOI：
10.1137/20m1362541
发表时间：
2021-01-01
期刊：
SIAM JOURNAL ON APPLIED MATHEMATICS
影响因子：
1.9
作者：
Hodneland, Erlend;Hu, Xiaozhe;Nordbotten, Jan M.
通讯作者：
Nordbotten, Jan M.

Diffusion State Distances: Multitemporal Analysis, Fast Algorithms, and Applications to Biological Networks

DOI：
10.1137/20m1324089
发表时间：
2021-01-01
期刊：
SIAM JOURNAL ON MATHEMATICS OF DATA SCIENCE
影响因子：
3.6
作者：
Cowen, Lenore;Devkota, Kapil;Wu, Kaiyi
通讯作者：
Wu, Kaiyi

GLIDE: combining local methods and diffusion state embeddings to predict missing interactions in biological networks

DOI：
10.1093/bioinformatics/btaa459
发表时间：
2020-07-01
期刊：
BIOINFORMATICS
影响因子：
5.8
作者：
Devkota, Kapil;Murphy, James M.;Cowen, Lenore J.
通讯作者：
Cowen, Lenore J.

Neighborhood embedding and re-ranking of disease genes with ADAGIO

DOI：
10.1145/3535508.3545542
发表时间：
2022-08
期刊：
Proceedings of the 13th ACM International Conference on Bioinformatics, Computational Biology and Health Informatics
影响因子：
0
作者：
Mert Erden;Megan Gelement;Sarrah Hakimjee;Kyla Levin;Mary-Joy Sidhom;K. Devkota;L. Cowen
通讯作者：
Mert Erden;Megan Gelement;Sarrah Hakimjee;Kyla Levin;Mary-Joy Sidhom;K. Devkota;L. Cowen

Random-Walk Based Approximate k-Nearest Neighbors Algorithm for Diffusion State Distance

基于随机游走的扩散状态距离近似 k 最近邻算法