|
所在平台: Coursera |
课程主页: https://www.coursera.org/learn/data-genes-medicine
课程评论：没有评论
课程名称：大数据、基因与医学 课程概述：本课程为您提供健康大数据科学和生物信息学领域专业人士所掌握的知识和技能。您将学习与大数据科学相结合的人体生物学和化学、遗传学及医学的精彩事实，掌握如何利用触手可及的数据来理解和分析生物医学相关信息。课程将涵盖掌握真实数据集（包括下一代测序数据）的大数据分析所需的不同步骤，包括数据预处理、分析、结果解释、可视化及结果分享。 通过掌握这些高需求的技能，您将能够申请或转到生物医学数据分析和生物信息学相关的职位。无论您在生物医学或技术领域的技能水平如何，您都将获得非常有价值的新技能或提升现有技能，使您在职业中脱颖而出，并激发您深入了解生物医学大数据的兴趣。希望本课程能够点燃您对利用公开大数据更好理解、预防和治疗疾病的无限可能的兴趣。 课程大纲： 1. **基因与数据**： - 学习如何定位和下载与基因和医学相关的数据文件。 - 使用R语言打开文件并预处理数据。 - 编写R脚本以替换缺失值、标准化数据、离散化数据和采样数据。 2. **准备数据集进行分析**： - 学习如何定位和下载与基因和医学相关的数据文件。 - 使用R语言打开文件并预处理数据。 - 编写R脚本以替换缺失值、标准化数据、离散化数据和采样数据。 3. **寻找差异表达基因**： - 从高维数据集中选择特征。 - 评估特征选择方法的性能。 - 编写R脚本从基因表达数据集中选择特征。 4. **根据基因预测疾病**： - 构建分类和预测模型。 - 评估分类和预测方法的性能。 - 编写R脚本从基因表达中分类和预测疾病。 5. **确定基因变化**： - 列出不同类型的基因变化。 - 比较检测基因突变的方法。 - 比较检测甲基化的方法。 - 比较检测拷贝数变化的方法。 - 定量基因组变化。 - 将基因组变化与基因的差异表达联系起来。 - 编写R程序以确定基因变化及其与基因表达的关系。 6. **聚类与通路分析**： - 在涉及基因的生物医学数据中寻找聚类。 - 分析和可视化生物通路。 - 编写R脚本进行聚类和通路分析。
Name:Genes and Data
Description:After this module, you will be able to 1. Locate and download files for data analysis involving genes and medicine. 2. Open files and preprocess data using R language. 3. Write R scripts to replace missing values, normalize data, discretize data, and sample data.
Name:Preparing Datasets for Analysis
Description:After this module, you will be able to: 1. Locate and download files for data analysis involving genes and medicine. 2. Open files and preprocess data using R language. 3. Write R scripts to replace missing values, normalize data, discretize data, and sample data.
Name:Finding Differentially Expressed Genes
Description:After this module, you will be able to 1. Select features from highly dimensional datasets. 2. Evaluate the performance of feature selection methods. 3. Write R scripts to select features from datasets involving gene expressions.
Name:Predicting Diseases from Genes
Description:After this module, you will be able to 1. Build classification and prediction models. 2. Evaluate the performance of classification and prediction methods. 3. Write R scripts to classify and predict diseases from gene expressions.
Name:Determining Gene Alterations
Description:After this module, you will be able to 1. List different types of gene alterations. 2. Compare and contrast methods for detecting gene mutations. 3. Compare and contrast methods for detecting methylation. 4. Compare and contrast methods for detecting copy number variations. 5. Quantify genomic alterations. 6. Connect genomic alterations to differential expression of genes. 7. Write programs in R for determining gene alterations and their relationship with gene expression.
Name:Clustering and Pathway Analysis
Description:After this module, you will be able to 1. Find clusters in biomedical data involving genes.2. Analyze and visualize biological pathways. 3. Write R scripts for clustering and for pathway analysis.
This course distills for you expert knowledge and skills mastered by professionals in Health Big Data Science and Bioinformatics. You will learn exciting facts about the human body biology and chemistry, genetics, and medicine that will be intertwined with the science of Big Data and skills to harness the avalanche of data openly available at your fingertips and which we are just starting to make sense of. We’ll investigate the different steps required to master Big Data analytics on real datasets, including Next Generation Sequencing data, in a healthcare and biological context, from preparing data for analysis to completing the analysis, interpreting the results, visualizing them, and sharing the results. Needless to say, when you master these high-demand skills, you will be well positioned to apply for or move to positions in biomedical data analytics and bioinformatics. No matter what your skill levels are in biomedical or technical areas, you will gain highly valuable new or sharpened skills that will make you stand-out as a professional and want to dive even deeper in biomedical Big Data. It is my hope that this course will spark your interest in the vast possibilities offered by publicly available Big Data to better understand, prevent, and treat diseases.
