Predoctoral Training in Bioinformatics and Computational Biology

项目摘要/摘要 生物医学研究作为新的高通量技术正在迅速发展，产生了大量的 数据，开辟了数据驱动预测能力的新前沿，提升了复杂数据的适用性 分析和机器学习算法，范围从个性化医疗和药物发现到 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 获取数据已变得至关重要。这一新的申请寻求对博士前培训的支持 波士顿大学生物信息学和计算生物学计划将培训年轻科学家 在这个变革的时代成为领导者。要求每年提供10个博士前培训时段，以资助5个 第一年和第二年的实习生。该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，以及 强调可重复性习惯，在研究中意识到算法和种族偏见，并广泛 发展科学交流技能的机会。该计划的特点包括：1)三次实验室旋转， 包括湿实验室体验，它向新学员介绍高通量实验方法，2) 挑战项目，用于对开放式、数据密集型生物学问题的第一年团队研究，具有 强调严谨性和可重复性，3)计算技能发展编程讲习班，4) 识别数据收集和使用中的偏差的算法偏差讲习班，5)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，举办 与日本和德国的合作伙伴计划联合举办的年度学生组织的研讨会，8)a 教学要求，以及9)年度计划务虚会。来自14个系的36名教师导师， 每一项研究都有很强的量化成分，提供了广泛的跨学科专业知识 实验、数学和计算方法。它们都使用严格且可重现的方法 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 导师培训由计划启动资金提供。三位共同工作了十多年的共同民主党人， 将提供领导力。每一项都带来了培训优势和管理经验。一位高管 委员会，包括五名额外的教员将监督该计划，并由受训人员直接提供意见 通过学生顾问委员会。该计划提供广泛的职业发展活动， 多管齐下的学生参与途径，以及持续评估的多维计划。

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.