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取生物系统中相关蛋白质或基因的信息的策略。除了收集有关不同基因或蛋白质在细胞中不同基因或蛋白质的作用的大量信息外，有关蛋白对或相关的基因的信息越来越多，要么是因为有证据表明它们在细胞中合作，也可以证明它们具有共同的属性。有关关联对的信息可以数学上描述为网络的异质集合，但是设计高效有效的机器学习以及计算数学算法，以整合各种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进行定制，他们希望使用网络分析来提高其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，以及在诸如II II型糖尿病和cro糖尿病等复杂疾病中重要的基因鉴定。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进行培训和合作，他们将参与更广泛的科学计算和生物学研究社区。有效，有效的推理和计算方法将通过挖掘多层PPI网络通过集成光谱方法以及基于差异基于基于距离的距离距离计数的挖掘方法开发，分析和实施。更确切地说，将设计基于尺寸缩小和多级优化方法的光谱多层分析方法，以提供多个网络的高质量集成工具，可用于开采此大量图形集合。这些方法将在与最近的梦想疾病模块识别挑战有关的新的生物网络测试台上进行基准测试和测试。此外，该工具的实施将普遍用于社区，用于开采一般的非均质网络收集，这将导致与计算生物学中的核心问题有关的新见解，包括识别数据集中的疾病模块。该奖项颁奖典礼反映了NSF的法定任务，并通过评估了该基金会的智力效果，并通过评估了CRARITAIL的支持。

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