211104 @ BioC Asia 2021 Workshop
Introduction to Bioimage Analysis in R
This workshop covers basic methods of the image processing and image analysis in R using the Bioconductor package “EBImage” and the Orchestra platform. In addition, the image dataset is obtained from ExperimentHub using the “BioImageDbs” package. Using this dataset, we perform a supervised image segmentation using the U-NET model, one of deep learning models, provided by the rMiW package.
このワークショップでは、BioconductorパッケージであるEBImageを使って、Rでの画像処理?画像解析の基本的な方法を扱う。次に、BioImageDbsパッケージを用いて、ExperimentHubからの画像データの取得を行う。さらに、rMiWパッケージが提供する、Deep learningモデルの1つであるU-NETモデルを用いて、教師有り画像セグメンテーション(領域分割)を学び。このワークショップは、Orchestra環境にて実施する。
リポカリン型プロスタグランジンD合成酵素の脂溶性低分子に対する系統的相互作用解析
Systematic Interaction Analysis of Human Lipocalin-type Prostaglandin D Synthase with Small Lipophilic Ligands
This document discusses R packages related to TensorFlow and Keras, focusing on their applications in deep learning for bioinformatics, including regression, classification, and GANs. It provides a setup guide for using TensorFlow and Keras in R, along with examples of model building and transfer learning, particularly using the TensorFlow Hub with Keras. Additionally, it highlights various packages available on RStudio GitHub for TensorFlow integration and resources for working with datasets.
This document provides instructions for installing R and using shell commands from within R on a CentOS 7 system. It explains how to install R using yum by first installing the EPEL repository and enabling it. It then shows how to use the system() function to run bash, tcsh, or other shell scripts from within R. The document also provides an example csh script and shows how to make it executable. Finally, it describes how to optionally install the GNOME desktop environment.
This document discusses R packages related to TensorFlow and Keras, focusing on their applications in deep learning for bioinformatics, including regression, classification, and GANs. It provides a setup guide for using TensorFlow and Keras in R, along with examples of model building and transfer learning, particularly using the TensorFlow Hub with Keras. Additionally, it highlights various packages available on RStudio GitHub for TensorFlow integration and resources for working with datasets.
This document provides instructions for installing R and using shell commands from within R on a CentOS 7 system. It explains how to install R using yum by first installing the EPEL repository and enabling it. It then shows how to use the system() function to run bash, tcsh, or other shell scripts from within R. The document also provides an example csh script and shows how to make it executable. Finally, it describes how to optionally install the GNOME desktop environment.