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

全标题：使用机器学习改进对复杂活体数据集的数据分析：斑马鱼肌肉骨骼和健康关节老化的寿命细胞分辨率图像骨骼系统出人意料地动态，由于基因表达和/或负载的变化而在整个生命过程中进行重塑。变化是骨骼和关节疾病的基础，英国有超过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。由于它们的半透明性，以及使用荧光报告追踪整个动物细胞的潜力，它们提供了极好的遗传可操作性以及动态体内成像。Ourgroup已经制作了20个斑马鱼突变系，这些突变系携带导致人类疾病状态的基因突变，以及转基因系，让我们能够看到构成活着的鱼的肌肉、软骨、骨骼、肌腱和免疫系统的细胞。我们已经积累了大量的3D数据集，其中包含我们目前没有完全提取的数据。该项目专注于开发机器学习策略，以处理大型、复杂的3D活体数据集，目的是利用这些策略开发高通量系统，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，提供了将先进的活体技能(CRISPR基因组编辑、转基因记者的实时成像)与计算人工智能和机器学习方法相结合以可视化和分析数据的挑战。这个监督小组的成员来自学术界和工业界。该项目将为学生提供一套非常受欢迎的技能，而这种技能的需求量越来越大。这个项目特别适合对生物系统感兴趣并有一定编程经验的学生，最好是使用Python语言。

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.