Predoctoral Training in Bioinformatics and Computational Biology

项目概要/摘要 生物医学研究作为新的高通量技术正在迅速发展，产生大量的 数据，开辟数据驱动的预测能力的新领域，提高复杂数据的适用性 分析和机器学习算法，从个性化医疗和药物发现， 结构生物学和微生物生态学。同时，高标准的道德生产，使用和开放 获取数据变得至关重要。这个新的应用程序寻求支持的博士前培训 波士顿大学生物信息学和计算生物学项目，将培养年轻科学家， 成为这个转型时代的领导者。每年要求十个博士前培训名额，以资助五个 第一年和第二年的学员。课程包括生物领域的坚实基础 知识，定量科学（计算，数学和统计学）的先进方法， 强调重复性习惯，对研究中的算法和种族偏见的认识，以及广泛的 发展科学交流技能的机会。该计划的特点包括：1）三个实验室轮换， 包括湿实验室体验，向新学员介绍高通量实验方法，2） 挑战项目，为第一年的团队研究开放式，数据密集型生物问题，与 强调严谨性和可重复性，3）计算技能开发的编程研讨会，4） 关于认识数据收集和使用中的偏见的学术偏见讲习班，5）一项正在进行的研究 学生研讨会，6）生物信息学和系统生物学年度国际研讨会，进行 与日本和德国的合作伙伴项目联合举办，7）年度学生组织研讨会，8）a 教学要求，和9）年度计划务虚会。来自14个系的36位导师， 每个都有一个强大的定量组成部分，他们的研究，提供了广泛的跨学科的专业知识， 实验、数学和计算方法。所有这些都使用严格和可重复的方法， 研究，在计划活动中发挥积极作用，致力于积极指导，并将采取 导师培训的计划资金的开始。三个合作了十多年的共同检察官， 将发挥领导作用。每个人都带来了培训优势和行政经验。执行 委员会，包括五个额外的教师将监督该计划，从学员的直接投入 通过学生咨询理事会。该计划提供广泛的职业发展活动， 学生参与的多管齐下的途径，以及一个正在进行的评估的多维计划。

Project Summary/Abstract Biomedical research is evolving rapidly as new high-throughput technologies, producing massive quantities of data, open new frontiers in data-driven predictive capabilities, boosting the applicability of complex data analysis and machine learning algorithms in areas ranging from personalized medicine and drug discovery to structural biology and microbial ecology. Simultaneously, high standards for ethical production, use, and open access to data have become essential. This new application seeks support for the Predoctoral Training Program in Bioinformatics and Computational Biology at Boston University which will train young scientists to become leaders in this transformational era. Ten predoctoral training slots per year are requested to fund five trainees each in years one and two. The curriculum includes a strong foundation in biological domain knowledge, advanced methodologies in the quantitative sciences (computing, mathematics, and statistics), an emphasis on reproducibility habits, awareness of algorithmic and racial bias in research, and extensive opportunities for developing scientific communications skills. Program features include: 1) three lab rotations, including the Wet-Lab Experience, which introduces new trainees to high-throughput experimental methods, 2) the Challenge Project, for first-year team research on open-ended, data-intensive biological problems, with an emphasis on rigor and reproducibility, 3) Programming workshops for computational skills development, 4) Algorithmic Bias workshops for recognizing biases in data collection and use, 5) a research-in-progress Student Seminar, 6) the annual International Workshop in Bioinformatics and Systems Biology, undertaken jointly with partner programs in Japan and Germany, 7) the annual Student-Organized Symposium, 8) a teaching requirement, and 9) the annual Program Retreat. Thirty-six faculty mentors from 14 departments, each with a strong quantitative component to their research, offer a wide range of interdisciplinary expertise in experimental, mathematical and computational approaches. All use rigorous and reproducible methods in their research, take an active role in Program activities, are committed to active mentoring, and will have taken mentor training by the start of Program funding. Three co-PDs, who have worked together for over a decade, will provide leadership. Each brings training strengths and administrative experience. An Executive Committee, including five additional faculty will oversee the Program, with direct input from the trainees through a Student Advisory Council. The Program offers extensive career development activities, multipronged avenues for student engagement, and a multidimensional scheme for ongoing evaluation.