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所在平台: Coursera |
课程主页: https://www.coursera.org/learn/ai-for-drug-discovery
课程评论：没有评论
课程名称: 毕业设计：用于药物发现的高级人工智能 课程概述: 本课程以一个实际项目为基础，比较新冠病毒变异的基因组序列，以识别药物治疗可针对的潜在区域。药物发现的第一步是识别基因组中的目标子序列。我们将首先比较病毒变异的基因组以寻找相似处。接下来，我们会进行主成分分析（PCA）来降低数据维度，并识别出最常见的特征。然后，我们将使用Python中的K均值聚类算法来找出最佳的群体数量，并追踪病毒的谱系。最后，我们将预测序列之间的相似性，并运用此信息来选择目标子序列。整个课程将包含每个模块的编程作业、指导视频和有用提示。到课程结束时，您将掌握基于基因组测序发现对抗疾病的方法。 课程大纲： 第一部分：基因组序列的主成分分析 描述：在本模块，我们将继续使用基因组序列数据，通过PCA识别组别和最重要的特征。在降低数据集的维度后，我们将使用K均值算法进行聚类，并可视化二维空间中的不同区域。 第二部分：使用K均值聚类进行特征分析 描述：在本模块中，我们将使用K均值算法对基因组序列进行聚类。通过比较多种输入的剪影得分来优化聚类数量，以识别最佳的分割点。最后，我们将为最后一个模块的预测管道做好准备，以预测比特分数和药物疗法。 第三部分：预测比特分数以寻找序列匹配 描述：在本模块中，我们将测试多种回归模型，以确定哪种模型在预测每个基因组序列的比特分数方面表现最好。然后，我们将使用所选模型找到最相关的基因组序列，并推导出可能的药物靶向子序列。
Part: 1
Title:Principal Component Analysis on Genome Sequences
Description:In this module, we'll continue to work with out genome sequence data - using PCA to identify groups and delicate the most important features. After reducing the number of dimensions in the dataset, we'll be able to use K-means to form clusters and visualize the different areas in 2-D space.
Part: 2
Title:Feature Analysis using K-Means Clustering
Description:In this module, we'll cluster the genome sequences using the K-means algorithm. We'll optimize the number of clusters by comparing silhouette scores across a wide variety of inputs to identify the greatest drop-off. Finally, we'll set ourselves up to using prediction pipelines to predict bit scores and drug therapies in the last module.
Part: 3
Title:Predicting Bit Score to Find Sequence Matches
Description:In this module, we'll test a variety of regressors to see which one performs best in predicting bit scores for each genome sequence. Then, we'll use our chosen model to find the genome equines that are most closely related and trace out a possible subsequence to target with a combative drug.
In this capstone project course, we'll compare genome sequences of COVID-19 mutations to identify potential areas a drug therapy can look to target. The first step in drug discovery involves identifying target subsequences of theirs genome to target. We'll start by comparing the genomes of virus mutations to look for similarities. Then, we'll perform PCA to cut down our number of dimensions and identify the most common features. Next, we'll use K-means clustering in Python to find the optimal number of groups and trace the lineage of the virus. Finally, we'll predict similarity between the sequences and use this to pick a target subsequence. Throughout the course, each section will consist of a programming assignment coupled with a guide video and helpful hints. By the end, you'll be well on your way to discovering ways to combat disease with genome sequencing.
