Bioinformatics algorithms

生物信息学算法

• 批准号: CRC-2017-00215
• 负责人: Hallett, Michael
• 金额: $ 14.57万
• 依托单位: Concordia University
• 依托单位国家: 加拿大
• 项目类别: Canada Research Chairs
• 财政年份: 2020
• 资助国家: 加拿大
• 起止时间: 2020-01-01 至 2021-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=695711
• 关键词: Bioinformatics; algorithms

Signatures are a modern type of biomarker arising from systems biology where high-throughput technologies provide genome-wide measurements of samples. They have many advantages over single biomarker approaches, and have already found utility in clinical and research contexts. This computational biology proposal will improve methodology related to signatures. Our approach uses probabilistic graphical models to build transcriptional signature networks that capture the fact that molecular processes are tightly inter-connected and dependent on many cellular signals. Using our high-throughput robotics facility, we will build a network for C albicans, an organism that can be commensal or pathogenic for humans.

签名是一种现代类型的生物标志物，源于系统生物学，其中高通量技术提供样品的全基因组测量。与单一生物标志物方法相比，它们具有许多优势，并且已经在临床和研究环境中发现了实用性。这一计算生物学提案将改进与签名相关的方法。我们的方法使用概率图形模型来构建转录签名网络，该网络捕获分子过程紧密相连并依赖于许多细胞信号的事实。利用我们的高通量机器人设备，我们将建立一个白色念珠菌的网络，白色念珠菌是一种对人类具有致病性或致病性的生物体。