Culturally Augmented Learning In Biomedical Informatics Research (CALIBIR) Program

生物医学信息学研究中的文化增强学习 (CALIBIR) 计划

基本信息

项目摘要

PROJECT SUMMARY/ABSTRACT The Culturally Augmented Learning In Biomedical Informatics Research (CALIBIR) program will address the clear need to enhance diversity within the biomedical informatics and data science field, particularly the dearth of underrepresented individuals pursuing graduate programs and careers in different areas of this field. The CALIBIR program builds on the research strengths in biomedical informatics and data science and the existing didactic training in biomedical informatics, engineering, statistics, and computer science topics at Wake Forest School of Medicine and Wake Forest University. The Program Faculty (including the Program Directors) have vast academic and professional experiences; a history of administration and collaboration on diversity-focused research and training programs; and ongoing research and funding in the five research domains proposed for CALIBIR training: Clinical Research Informatics, Artificial Intelligence, Healthcare Informatics, Imaging Informatics, and Translational Bioinformatics. Thus, we are well-positioned to offer a 10-week summer research immersion experience to train and educate undergraduate and master’s level underrepresented students and promote greater interest, exposure, and research experiences in biomedical informatics and data science. The CALIBIR program will support mentored research training activities of 60 scholars (12 students per summer). The program's overall goals are to: 1) increase underrepresented students' skills, preparation, and commitment to participate in cutting-edge biomedical informatics research; 2) offer lectures and hands-on demonstrations on state-of-the-art biomedical informatics techniques via a “Biomedical Informatics Boot Camp”; 3) provide group seminars and networking events covering responsible conduct of research, scientific dissemination, and professional development topics; 4) build trainee experience in scientific communication via a journal club, writing workshop, and an abstract and oral presentation of their research; 5) cultivate influential and long-lasting collaborative relationships between trainees and mentors to highlight the value of teamwork in research and provide an accepting and inclusive environment that will positively impact students from diverse backgrounds; and 6) encourage underrepresented students to pursue doctoral studies and careers in biomedical informatics and data science fields through an experience that exemplifies the excitement and challenges of clinically relevant investigations. The overarching goal of the CALIBIR program is to expand the pool of underrepresented students who successfully complete a graduate degree in a biomedical informatics- related field up to a PhD and increase the diversity of the biomedical informatics and data science workforce. In sum, the CALIBIR program takes advantage of unique strengths, existing infrastructure, and long-standing collaborations among diverse program faculty to ensure the successful creation and implementation of the program. It offers several innovative approaches to provide both technical and professional preparation to recruit and retain underrepresented students in the biomedical informatics and data science fields.
项目概要/摘要 生物医学信息学研究中的文化增强学习 (CALIBIR) 计划将解决 显然需要增强生物医学信息学和数据科学领域的多样性,特别是缺乏 代表性不足的个人在该领域的不同领域攻读研究生课程和职业。 CALIBIR 项目建立在生物医学信息学和数据科学的研究优势以及现有的基础上 维克森林大学生物医学信息学、工程、统计学和计算机科学主题的教学培训 医学院和维克森林大学的项目教师(包括项目主任)。 丰富的学术和专业经验;注重多样性的管理和合作历史; 研究和培训计划;以及拟议的五个研究领域的持续研究和资助 CALIBIR 培训:临床研究信息学、人工智能、医疗保健信息学、成像 因此,我们有能力提供为期 10 周的暑期课程。 培养和教育本科生和硕士水平的研究沉浸式经验不足 学生并提高对生物医学信息学和数据的兴趣、接触和研究经验 CALIBIR 计划将支持 60 名学者(12 名学生)的指导研究培训活动。 每个夏天)该计划的总体目标是:1)提高代表性不足的学生的技能,准备, 并致力于参与前沿的生物医学信息学研究2)提供讲座和实践; 通过“生物医学信息学引导”演示最先进的生物医学信息学技术 3) 提供小组研讨会和社交活动,涵盖负责任的研究行为、科学 传播和专业发展主题;4)通过以下方式培养学员的科学传播经验: 5)培养影响力 以及学员和导师之间持久的合作关系,以强调团队合作的价值 研究并提供一个接受和包容的环境,这将对来自不同背景的学生产生积极影响 6) 鼓励代表性不足的学生攻读博士学位并从事职业生涯 通过体现兴奋和体验的生物医学信息学和数据科学领域 CALIBIR 计划的总体目标是扩大临床相关研究的挑战。 成功完成生物医学信息学研究生学位的少数学生 相关领域直至博士学位,并增加生物医学信息学和数据科学劳动力的多样性。 总之,CALIBIR 计划利用了独特的优势、现有的基础设施和长期的 不同项目教师之间的合作,以确保成功创建和实施 它提供了几种创新方法来提供技术和专业准备。 招募并留住生物医学信息学和数据科学领域代表性不足的学生。

项目成果

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Metin Nafi Gurcan其他文献

Analysis of gene expression dynamics and differential expression in viral infections using generalized linear models and quasi-likelihood methods
使用广义线性模型和拟似然方法分析病毒感染中的基因表达动态和差异表达
  • DOI:
    10.3389/fmicb.2024.1342328
  • 发表时间:
    2024-04-09
  • 期刊:
  • 影响因子:
    5.2
  • 作者:
    Mostafa Rezapour;Stephen J. Walker;David A. Ornelles;P. M. McNutt;Anthony Atala;Metin Nafi Gurcan
  • 通讯作者:
    Metin Nafi Gurcan
Employing machine learning to enhance fracture recovery insights through gait analysis.
利用机器学习通过步态分析增强骨折恢复洞察力。
  • DOI:
    10.1002/jor.25837
  • 发表时间:
    2024-04-10
  • 期刊:
  • 影响因子:
    0
  • 作者:
    Mostafa Rezapour;Rachel B. Seymour;Stephen H. Sims;M. Karunakar;Nahir A. Habet;Metin Nafi Gurcan
  • 通讯作者:
    Metin Nafi Gurcan
A comparative analysis of RNA-Seq and NanoString technologies in deciphering viral infection response in upper airway lung organoids
RNA-Seq 和 NanoString 技术在破译上呼吸道肺类器官病毒感染反应方面的比较分析
  • DOI:
    10.3389/fgene.2024.1327984
  • 发表时间:
    2024-06-18
  • 期刊:
  • 影响因子:
    3.7
  • 作者:
    Mostafa Rezapour;Stephen J. Walker;David A. Ornelles;Muhammad Khalid Khan Niazi;Patrick M. McNutt;Anthony Atala;Metin Nafi Gurcan
  • 通讯作者:
    Metin Nafi Gurcan
Gene PointNet for Tumor Classification
用于肿瘤分类的 Gene PointNet
  • DOI:
    10.1101/2024.06.02.597020
  • 发表时间:
    2024-06-03
  • 期刊:
  • 影响因子:
    0
  • 作者:
    Hao Lu;Mostafa Rezapour;Haseebullah Baha;M. K. K. Niazi;Aarthi Narayanan;Metin Nafi Gurcan
  • 通讯作者:
    Metin Nafi Gurcan

Metin Nafi Gurcan的其他文献

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{{ truncateString('Metin Nafi Gurcan', 18)}}的其他基金

Efficient and cost-effective breast cancer risk stratification using whole slide histopathology images
使用全玻片组织病理学图像进行高效且经济的乳腺癌风险分层
  • 批准号:
    10649978
  • 财政年份:
    2023
  • 资助金额:
    $ 13.5万
  • 项目类别:
Computer-assisted diagnosis of ear pathologies by combining digital otoscopy with complementary data using machine learning
通过使用机器学习将数字耳镜与补充数据相结合来计算机辅助诊断耳部病变
  • 批准号:
    10564534
  • 财政年份:
    2023
  • 资助金额:
    $ 13.5万
  • 项目类别:
Culturally Augmented Learning In Biomedical Informatics Research (CALIBIR) Program
生物医学信息学研究中的文化增强学习 (CALIBIR) 计划
  • 批准号:
    10701848
  • 财政年份:
    2022
  • 资助金额:
    $ 13.5万
  • 项目类别:
Analytics & Machine-learning for Maternal-health Interventions (AMMI): A Cross-CTSA Collaboration
分析
  • 批准号:
    10670448
  • 财政年份:
    2022
  • 资助金额:
    $ 13.5万
  • 项目类别:
Auto-Scope Software-Automated Otoscopy to Diagnose Ear Pathology
Auto-Scope 软件 - 用于诊断耳部病理的自动耳镜检查
  • 批准号:
    9790958
  • 财政年份:
    2018
  • 资助金额:
    $ 13.5万
  • 项目类别:
Pathology Image Informatics Platform for visualization, analysis and management
用于可视化、分析和管理的病理图像信息学平台
  • 批准号:
    9341177
  • 财政年份:
    2015
  • 资助金额:
    $ 13.5万
  • 项目类别:
Computer-based assessment of tumor microenvironment (TME) in Follicular Lymphoma
基于计算机的滤泡性淋巴瘤肿瘤微环境 (TME) 评估
  • 批准号:
    8758963
  • 财政年份:
    2009
  • 资助金额:
    $ 13.5万
  • 项目类别:
Computer-based assessment of tumor microenvironment (TME) in Follicular Lymphoma
基于计算机的滤泡性淋巴瘤肿瘤微环境 (TME) 评估
  • 批准号:
    9277412
  • 财政年份:
    2009
  • 资助金额:
    $ 13.5万
  • 项目类别:
Computer-assisted Grading and Risk Stratification of Follicular Lymphoma
滤泡性淋巴瘤的计算机辅助分级和风险分层
  • 批准号:
    7812178
  • 财政年份:
    2009
  • 资助金额:
    $ 13.5万
  • 项目类别:
Computer-based assessment of tumor microenvironment (TME) in Follicular Lymphoma
基于计算机的滤泡性淋巴瘤肿瘤微环境 (TME) 评估
  • 批准号:
    9070324
  • 财政年份:
    2009
  • 资助金额:
    $ 13.5万
  • 项目类别:

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