Computational Genomic Epidemiology of Cancer (CoGEC) Training Program

癌症计算基因组流行病学 (CoGEC) 培训计划

基本信息

批准号：
9114026
负责人：
Li Li
金额：
$ 51.2万
依托单位：
CASE WESTERN RESERVE UNIVERSITY
依托单位国家：
美国
项目类别：
财政年份：
2002
资助国家：
美国
起止时间：
2002-09-09 至 2017-07-31
项目状态：
已结题

项目摘要

DESCRIPTION (provided by applicant): Undertaking innovative cancer research can require input from teams of scientists with a mixture of backgrounds, including molecular biology, oncology, medicine, epidemiology, biostatistics, genomics/genetics, and computer science. Researchers with interdisciplinary training across these fields are extremely valuable to such teams, as they can act as conduits for the integrated work necessary to accomplish some of the most promising and forward-looking cancer research. Due to the exclusive nature of training within these fields, however, there are limited opportunities for investigators to obtain the knowledge that bridges these disciplines. To help remedy this problem, we propose here the continuation of this R25T program to provide postdoctoral training in the computational genetic epidemiology of cancer. This program defines a novel, transdisciplinary area of training at the intersection of cancer research, epidemiology, biostatistics, genetics, and computer science. The program's structure is defined by three key requirements. First, all trainees will take a specialized core curriculum of five courses that cover the individual disciplines as well as their intersections. Second, the trainees will undertake additional didactic experiences selected to complement their educational and research background. Third, all trainees will obtain research experience by collaborating with multiple mentors on high-level computational genetic epidemiology of cancer projects. As an extension of this research experience, each trainee will be required to write and defend a mock NIH proposal. Cancer researchers obtaining training in this program will have the skills vital to deciphering the complex pathways comprising genetic and environmental risk factors for disease. In doing so, they will ultimately be able to provide clinicians and their patients with invaluable information for the prevention and treatment of cancer.

描述（由申请人提供）：进行创新性癌症研究可能需要具有多种背景的科学家团队的投入，包括分子生物学、肿瘤学、医学、流行病学、生物统计学、基因组学/遗传学和计算机科学。在这些领域接受过跨学科培训的研究人员对于这些团队来说非常有价值，因为他们可以充当完成一些最有前途和前瞻性的癌症研究所需的综合工作的渠道。然而，由于这些领域培训的排他性，研究人员获得连接这些学科的知识的机会有限。为了帮助解决这个问题，我们在此建议继续开展 R25T 计划，提供癌症计算遗传流行病学方面的博士后培训。该项目在癌症研究、流行病学、生物统计学、遗传学和计算机科学的交叉领域定义了一个新颖的跨学科培训领域。该计划的结构由三个关键要求定义。首先，所有学员都将学习由五门课程组成的专业核心课程，涵盖各个学科及其交叉学科。其次，学员将接受额外的教学经验，以补充他们的教育和研究背景。第三，所有学员将通过与多位导师合作进行癌症高水平计算遗传流行病学项目来获得研究经验。作为这项研究经验的延伸，每位学员都需要撰写并捍卫一份模拟 NIH 提案。接受该计划培训的癌症研究人员将拥有破译包含疾病遗传和环境风险因素的复杂途径至关重要的技能。通过这样做，他们最终将能够为临床医生及其患者提供预防和治疗癌症的宝贵信息。

项目成果

期刊论文数量（29）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

DB2: a probabilistic approach for accurate detection of tandem duplication breakpoints using paired-end reads.

DOI：
10.1186/1471-2164-15-175
发表时间：
2014-03-05
期刊：
BMC genomics
影响因子：
4.4
作者：
Yavaş G;Koyutürk M;Gould MP;McMahon S;LaFramboise T
通讯作者：
LaFramboise T

Comparing a knowledge-driven approach to a supervised machine learning approach in large-scale extraction of drug-side effect relationships from free-text biomedical literature.

DOI：
10.1186/1471-2105-16-s5-s6
发表时间：
2015
期刊：
BMC bioinformatics
影响因子：
3
作者：
Xu R;Wang Q
通讯作者：
Wang Q

Large-scale combining signals from both biomedical literature and the FDA Adverse Event Reporting System (FAERS) to improve post-marketing drug safety signal detection.

DOI：
10.1186/1471-2105-15-17
发表时间：
2014-01-15
期刊：
BMC bioinformatics
影响因子：
3
作者：
Xu R;Wang Q
通讯作者：
Wang Q

Germline and somatic SDHx alterations in apparently sporadic differentiated thyroid cancer.

DOI：
10.1530/erc-14-0537
发表时间：
2015-04
期刊：
Endocrine-related cancer
影响因子：
3.9
作者：
Ni Y;Seballos S;Ganapathi S;Gurin D;Fletcher B;Ngeow J;Nagy R;Kloos RT;Ringel MD;LaFramboise T;Eng C
通讯作者：
Eng C

Network-based gene prediction for Plasmodium falciparum malaria towards genetics-based drug discovery.