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CAREER: RUI: NetDA -- Protein Network-Based Software for Disease Analysis Using Cliques, Bipartite Graphs, and Diffusion Kernels

职业：RUI：NetDA——基于蛋白质网络的软件，使用派系、二分图和扩散核进行疾病分析

基本信息

批准号：
1901628
负责人：
Ananda Mondal
金额：
$ 27.5万
依托单位：
Florida International University
依托单位国家：
美国
项目类别：
Continuing Grant
财政年份：
2018
资助国家：
美国
起止时间：
2018-06-15 至 2024-05-31
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=1901628&HistoricalAwards=false
关键词：
CAREER RUI NetDA Protein Network

项目摘要

This project will investigate and analyze the complex phenomena of disease progression at the protein network level. A software tool, NetDA (Network-based Disease Analysis) to understand the essential mechanisms of disease progression as transitions between disease stages will be developed. The outcome of this study will help not only in early diagnosis of a disease but also in drug design specific for a disease stage. Undergraduate students will be involved in the research.The process of disease progression will be analyzed as an event-schedule-like structure, where a) each event representing a disease stage completed by a group of proteins will be analyzed in terms of clique and clique-like graphs, b) transfer of signals from one stage to the next will be analyzed using bipartite and bipartite-like graphs, and c) the strength of signals will be analyzed using diffusion kernels. The outcome of this study will help not only in early diagnosis of a disease but also in drug design specific for a disease stage. The project will integrate research into the undergraduate curriculum by assigning projects, in courses already developed by the PI, to solve problems in the proposed work. The project will provide research experience for undergraduate students using focused short projects during both academic year and summer. There is also a plan for the project to provide research experience for middle and high school students. The project will also develop approaches to introduce K-12 students to programming concepts as well as bioinformatics with the help of graphics. A summer institute is also planned to enhance student participation.

该项目将在蛋白质网络水平上研究和分析疾病进展的复杂现象。将开发一个软件工具，NetDA（基于网络的疾病分析），以了解疾病进展的基本机制，作为疾病阶段之间的过渡。这项研究的结果不仅有助于疾病的早期诊断，而且有助于针对疾病阶段的药物设计。本研究以本科生为对象，将疾病进展的过程作为事件时间表式的结构进行分析，a）用团图和团图分析代表一组蛋白质完成的疾病阶段的每个事件，B）用二分图和二分图分析从一个阶段到下一个阶段的信号传递，以及c）使用扩散核来分析信号的强度。这项研究的结果不仅有助于疾病的早期诊断，而且有助于针对疾病阶段的药物设计。该项目将通过分配项目，在PI已经开发的课程中，将研究融入本科课程，以解决拟议工作中的问题。该项目将为本科生提供研究经验，在学年和夏季使用重点短项目。该项目还计划为初中和高中学生提供研究经验。该项目还将开发方法，向K-12学生介绍编程概念以及图形帮助下的生物信息学。还计划开办暑期班，以提高学生的参与程度。

项目成果

期刊论文数量（18）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

Stage-Specific Co-expression Network Analysis for Cancer Biomarker Discovery

DOI：
10.1109/bibm49941.2020.9313242
发表时间：
2020-12
期刊：
2020 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)
影响因子：
0
作者：
R. Tanvir;A. Mondal
通讯作者：
R. Tanvir;A. Mondal

Long Non-coding RNA Based Cancer Classification using Deep Neural Networks

使用深度神经网络进行基于长非编码 RNA 的癌症分类

DOI：
10.1145/3307339.3343249
发表时间：
2019
期刊：
Computational Biology and Health Informatics
影响因子：
0
作者：
Mamun, Abdullah A.;Mondal, Ananda M.
通讯作者：
Mondal, Ananda M.

Graph Theoretic and Pearson Correlation-Based Discovery of Network Biomarkers for Cancer

DOI：
10.3390/data4020081
发表时间：
2019-06
期刊：
Data
影响因子：
2.6
作者：
R. Tanvir;Tasmia Aqila;Mona Maharjan;Abdullah Al Mamun;A. Mondal
通讯作者：
R. Tanvir;Tasmia Aqila;Mona Maharjan;Abdullah Al Mamun;A. Mondal

Deep Learning to Discover Genomic Signatures for Racial Disparity in Lung Cancer

深度学习发现肺癌种族差异的基因组特征

DOI：
10.1109/bibm49941.2020.9313426
发表时间：
2020
期刊：
2020 IEEE International Conference on Bioinformatics and Biomedicine (BIBM
影响因子：
0
作者：
Sobhan, Masrur;Mamun, Abdullah Al;Tanvir, Raihanul Bari;Alfonso, Mario Jacas;Valle, Pablo;Mondal, Ananda Mohan
通讯作者：
Mondal, Ananda Mohan

Feature Selection and Classification Reveal Key lncRNAs for Multiple Cancers