DEVELOPMENT OF ONLINE COMPUTATIONAL GENOMICS SPECIALIZATION
在线计算基因组学专业的发展
基本信息
- 批准号:10161806
- 负责人:
- 金额:$ 15.1万
- 依托单位:
- 依托单位国家:美国
- 项目类别:
- 财政年份:2020
- 资助国家:美国
- 起止时间:2020-05-08 至 2025-02-28
- 项目状态:未结题
- 来源:
- 关键词:AddressAlgorithmsBig DataBioinformaticsBiologicalBiological SciencesBiomedical ResearchBypassCommunitiesComputational BiologyDNA sequencingData AnalysesData SetDevelopmentE-learningEducationEducational CurriculumEventExposure toFunding OpportunitiesGenomicsGoalsIndividualJournalsLearningMethodsModernizationPaperPatient RecruitmentsProblem SolvingPublishingResearch PersonnelResearch Project GrantsResourcesScientistSoftware ToolsStudentsTextbooksTimeUnderrepresented MinorityUnited States National Institutes of HealthUniversitiesWorkbiological researchcosteducation researchexperienceexperimental studygenomic datainnovationinstructoronline resourcepedagogyprofessorresponsescience educationskillssymposiumtool
项目摘要
Project Summary
In modern biological research, computing has become an integral component in Biological
Big Data (BBD) analysis, yet education in computing has not been fully incorporated into life
science education: many biologists are given a diluted treatment of computational genomics that
presents the methods central to the field as nothing more than a toolkit. The pedagogical
challenge facing the development of a computational genomics curriculum is the need to convey
the important ideas without assuming previous exposure to programming. Biologists would also
profit from knowing how to effectively apply various existing genomics software tools and, at the
same time, understand how these tools work, a condition that is often violated in existing
courses.
We believe that high-quality online computational genomics education offers a particularly
attractive solution to the problem because many universities have failed to address this
challenge. It offers a promising pedagogical innovation because it is not replacing anything, but
rather is fulfilling an important need. It bypasses the need for extensive curricular reform at the
level of individual universities and instead adapt to high-quality, open online resources that
lower the cost per student. We believe that our proposed online Computational Genomics
Specialization will contribute to various offline courses (e.g., by enabling a flipped course) that
will be developed in response to the same NIH Funding Opportunity Announcement “Initiative to
Maximize Research Education in Genomics.”
We have published popular bioinformatics and algorithms textbooks, have published papers
on various challenges of education in computational biology in reputable journals, delivered a
TEDx talk on online education, founded a conference specializing in bioinformatics education
(RECOMB-BE), developed multiple successful MOOCs in bioinformatics (including the first
bioinformatics MOOC), and advised the development of Rosalind, an open online platform for
learning bioinformatics through problem solving that has been used by over 100 professors.
Our goals are (1) to develop open, modular, extendable, and adaptable MOOCs covering a
broad range of topics in modern computational genomics, (2) use the developed MOOCs to
competitively recruit the participants into the proposed offline computational genomics short
courses and to bring underrepresented minorities to these events, and (3) establish the
Computational Genomics Education Alliance, a community of educators who will help develop
open, high-quality, modular online content and serve as instructors at our annual courses.
项目总结
项目成果
期刊论文数量(0)
专著数量(0)
科研奖励数量(0)
会议论文数量(0)
专利数量(0)
数据更新时间:{{ journalArticles.updateTime }}
{{
item.title }}
{{ item.translation_title }}
- DOI:
{{ item.doi }} - 发表时间:
{{ item.publish_year }} - 期刊:
- 影响因子:{{ item.factor }}
- 作者:
{{ item.authors }} - 通讯作者:
{{ item.author }}
数据更新时间:{{ journalArticles.updateTime }}
{{ item.title }}
- 作者:
{{ item.author }}
数据更新时间:{{ monograph.updateTime }}
{{ item.title }}
- 作者:
{{ item.author }}
数据更新时间:{{ sciAawards.updateTime }}
{{ item.title }}
- 作者:
{{ item.author }}
数据更新时间:{{ conferencePapers.updateTime }}
{{ item.title }}
- 作者:
{{ item.author }}
数据更新时间:{{ patent.updateTime }}
Pavel A Pevzner其他文献
Pavel A Pevzner的其他文献
{{
item.title }}
{{ item.translation_title }}
- DOI:
{{ item.doi }} - 发表时间:
{{ item.publish_year }} - 期刊:
- 影响因子:{{ item.factor }}
- 作者:
{{ item.authors }} - 通讯作者:
{{ item.author }}
{{ truncateString('Pavel A Pevzner', 18)}}的其他基金
DEVELOPMENT OF ONLINE COMPUTATIONAL GENOMICS SPECIALIZATION
在线计算基因组学专业的发展
- 批准号:
10576322 - 财政年份:2020
- 资助金额:
$ 15.1万 - 项目类别:
DEVELOPMENT OF ONLINE COMPUTATIONAL GENOMICS SPECIALIZATION
在线计算基因组学专业的发展
- 批准号:
10353428 - 财政年份:2020
- 资助金额:
$ 15.1万 - 项目类别:
Integrated Active Learning Framework for Biomedical BD2K
生物医学 BD2K 集成主动学习框架
- 批准号:
8830382 - 财政年份:2014
- 资助金额:
$ 15.1万 - 项目类别:
Integrated Active Learning Framework for Biomedical BD2K
生物医学 BD2K 集成主动学习框架
- 批准号:
9132271 - 财政年份:2014
- 资助金额:
$ 15.1万 - 项目类别:
PROTEOMIC ANALYSIS OF EXTINCT SPECIES TO VALIDATE EVOLUTIONARY LINKS
对已灭绝物种进行蛋白质组学分析以验证进化联系
- 批准号:
8171400 - 财政年份:2010
- 资助金额:
$ 15.1万 - 项目类别:
相似海外基金
Big Data Analytics: Optimization Models and Algorithms with Applications in Smart Food Supply Chains and Networks
大数据分析:优化模型和算法在智能食品供应链和网络中的应用
- 批准号:
RGPIN-2020-06792 - 财政年份:2022
- 资助金额:
$ 15.1万 - 项目类别:
Discovery Grants Program - Individual
Large Systems and Big Data: Models, Tools, Analysis, and Algorithms
大型系统和大数据:模型、工具、分析和算法
- 批准号:
RGPIN-2020-04075 - 财政年份:2022
- 资助金额:
$ 15.1万 - 项目类别:
Discovery Grants Program - Individual
Algorithms and Tools for Big Data Analysis and Automated Real Time Optimal or Near Optimal Decision Making for Industrial Systems
用于工业系统大数据分析和自动实时最佳或接近最佳决策的算法和工具
- 批准号:
RGPIN-2017-05785 - 财政年份:2022
- 资助金额:
$ 15.1万 - 项目类别:
Discovery Grants Program - Individual
Novel Learning-Based Visual Algorithms and Fusion Methods for High-Dimensional/Multi-Modality Big Data
基于学习的新型高维/多模态大数据视觉算法和融合方法
- 批准号:
RGPIN-2022-02948 - 财政年份:2022
- 资助金额:
$ 15.1万 - 项目类别:
Discovery Grants Program - Individual
(Re)designing Clustering Algorithms for Big Data
(重新)设计大数据聚类算法
- 批准号:
RGPIN-2017-05617 - 财政年份:2022
- 资助金额:
$ 15.1万 - 项目类别:
Discovery Grants Program - Individual
NCS-FO: Connectome mapping algorithms with application to community services for big data neuroscience
NCS-FO:连接组映射算法及其应用于大数据神经科学社区服务
- 批准号:
2203524 - 财政年份:2021
- 资助金额:
$ 15.1万 - 项目类别:
Standard Grant
Big Data Analytics: Optimization Models and Algorithms with Applications in Smart Food Supply Chains and Networks
大数据分析:优化模型和算法在智能食品供应链和网络中的应用
- 批准号:
RGPIN-2020-06792 - 财政年份:2021
- 资助金额:
$ 15.1万 - 项目类别:
Discovery Grants Program - Individual
Exploring Novel Mathematical Models and Efficient Algorithms to Discover Periodic Spatial Patterns in Irregular Spatiotemporal Big Data
探索新颖的数学模型和高效算法以发现不规则时空大数据中的周期性空间模式
- 批准号:
21K12034 - 财政年份:2021
- 资助金额:
$ 15.1万 - 项目类别:
Grant-in-Aid for Scientific Research (C)
A comprehensive study of big data clustering algorithms
大数据聚类算法综合研究
- 批准号:
571110-2018 - 财政年份:2021
- 资助金额:
$ 15.1万 - 项目类别:
Alexander Graham Bell Canada Graduate Scholarships - Master's
(Re)designing Clustering Algorithms for Big Data
(重新)设计大数据聚类算法
- 批准号:
RGPIN-2017-05617 - 财政年份:2021
- 资助金额:
$ 15.1万 - 项目类别:
Discovery Grants Program - Individual