SSA:Using machine learning to improve data analysis from complex in vivo datasets:lifespan cellular resolution images of the zebrafish musculoskeletal

SSA：使用机器学习改进复杂体内数据集的数据分析：斑马鱼肌肉骨骼的寿命细胞分辨率图像

• 批准号: 2117425
• 金额: --
• 依托单位: University of Bristol
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2018
• 资助国家: 英国
• 起止时间: 2018 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2117425
• 关键词: SSA; Using; machine; learning; improve

Full title: Using machine learning to improve data analysis from complex in vivo datasets: lifespan cellular resolution images of the zebrafish musculoskeletal and healthy joint ageingThe skeletal system is surprisingly dynamic, undergoing remodelling due to changes in gene expressionand or loading throughout life. Changes to either underpin skeletal and joint diseases and over 15million people in the UK have a musculoskeletal disorder such as osteoporosis or osteoarthritis.Zebrafish are increasingly used as the animal model of choice to study developmental biology and cellbehaviour. They offer excellent genetic tractability along with dynamic in vivo imaging due to theirtranslucency and the potential to use fluorescent reporters to track cells in the whole animal. Ourgroup has made >20 mutant lines of zebrafish carrying mutations in genes that lead to disease statesin humans, along with transgenic lines that allow us to see the cells that make up muscle, cartilage,bone, tendons and the immune system in living fish. We have amassed a large number of 3D datasetsthat contain data that we currently do not fully extract. This project focuses on developing machinelearning strategies to process large, complex, 3D in vivo datasets with the aim of using these todevelop high throughput systems for testing of new clinically relevant genes and in vivo compoundscreening to test new pharmaceutical strategies. The project is highly interdisciplinary offering thechance to combine advanced in vivo skills (CRISPR genome editing, live imaging of transgenicreporters) with computational AI and machine learning approaches to visualise and analyse data. Thesupervisory team has members from both academia and industry. The project would give the studenta highly desirable skill set that is increasingly in huge demand. This project would particularly suit astudent with an interest in biological systems and some experience of programming, ideally in Python.

完整标题：使用机器学习来改善复杂体内数据集的数据分析：斑马鱼肌肉骨骼和健康关节衰老的寿命细胞分辨率图像骨骼系统是惊人的动态的，由于基因表达和/或负载的变化而经历重塑。在英国，超过1500万人患有骨质疏松症或骨关节炎等肌肉骨骼疾病。斑马鱼越来越多地被用作研究发育生物学和细胞行为的动物模型。由于它们的透明性和使用荧光报告分子跟踪整个动物细胞的潜力，它们提供了极好的遗传易处理性沿着动态体内成像。我们的团队已经制造了超过20种斑马鱼突变品系，这些品系携带的基因突变会导致人类疾病，沿着的还有转基因品系，这些品系让我们能够看到构成肌肉、软骨、骨骼、肌腱和活鱼免疫系统的细胞。我们已经积累了大量的3D数据集，其中包含我们目前无法完全提取的数据。该项目的重点是开发机器学习策略，以处理大型，复杂的3D体内数据集，目的是利用这些数据集开发高通量系统，用于测试新的临床相关基因和体内化合物筛选，以测试新的药物策略。该项目是高度跨学科的，提供了将先进的体内技能（CRISPR基因组编辑、转基因报告者的实时成像）与计算人工智能和机器学习方法相结合的机会，以可视化和分析数据。联合收割机。该研究小组的成员来自学术界和工业界。该项目将给学生一个非常理想的技能，这是越来越多的巨大需求。这个项目特别适合对生物系统感兴趣的学生和一些编程经验，最好是Python。

项目成果

NCOA3 identified as a new candidate to explain autosomal dominant progressive hearing loss.

• DOI: 10.1093/hmg/ddaa240
• 发表时间: 2021-01-21
• 期刊: Human molecular genetics
• 影响因子: 3.5
• 作者: Salazar-Silva R;Dantas VLG;Alves LU;Batissoco AC;Oiticica J;Lawrence EA;Kawafi A;Yang Y;Nicastro FS;Novaes BC;Hammond C;Kague E;Mingroni-Netto RC
• 通讯作者: Mingroni-Netto RC