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@unknown
Apr 16 • 4 tweets • 2 min read Twitter logo Read on Twitter
Today, I want to share 3 Bioconductor/R packages that are useful for chemoinformatics - the field that applies computational methods to solve problems in drug discovery, molecular design, and other areas. Let's get started! #rstats #datascience #bioinformatics
1/ The first package on our list is "ChemmineR". With ChemmineR, you can manipulate, visualize, and analyze large chemical datasets. It includes functions for clustering compounds, predicting properties, and more. Check it out at bioconductor.org/packages/relea…
2/ "ChemmineOB" is a package that extends ChemmineR with tools for working with Open Babel, a software that converts chemical data between different formats. With ChemmineOB, you can perform tasks such as generating 3D coordinates, converting file formats, and more.
3/ "Rcpi" is another package that provides a wide range of tools for chemoinformatics. It includes functions for calculating molecular descriptors, building models, and more. Check it out at bioconductor.org/packages/relea…

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Apr 17
1/ Meta-analysis is a powerful statistical technique for synthesizing data from multiple studies. In R, there are several packages available for conducting meta-analyses. Let's take a look at some of them! #RStats #MetaAnalysis #DataScience Graphical summary of a meta...
2/ There are several R packages available to conduct meta-analysis, but some of the most popular include metafor, meta, and netmeta. Each package has its own strengths and weaknesses, so it's important to choose the right one for your specific analysis. #rstats
3/ metafor is a powerful package that can handle complex meta-analyses with multiple predictors and moderators. It also has robust methods for dealing with publication bias. However, it can be a bit tricky to use for beginners. #rstats
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Apr 17
1/ Epidemiology is a critical field in public health, and R is a popular programming language for epidemiologists to analyze and visualize data. In this thread, I'll highlight some essential epidemiology packages in R that you should know. #rstats #datascience #epidemiology Image
2/ The first package that comes to mind is "epitools." This package provides a suite of functions for descriptive epidemiology, including measures of disease frequency, tests for independence, and outbreak detection. #rstats cran.r-project.org/web/packages/e…
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Twitter author Profile picture
Apr 17
1/ Clinical trials are an essential part of the drug development process. R offers a wide range of packages to handle clinical trial data analysis efficiently. In this thread, I will discuss some of the important R packages for clinical trial data analysis. #rstats #datascience
2/ The first package that comes to mind is "tidyverse." It's a collection of packages that make data wrangling, visualization easy. It includes packages like "dplyr", "tidyr", "ggplot2", "purrr." These packages can help in cleaning and transforming clinical trial data. #rstats
3/ Another package for clinical trial data analysis is "randomizeR." This package provides functions to generate randomization schemes for clinical trials. It offers different randomization methods, including simple, stratified, and block randomization. #rstats
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Twitter author Profile picture
Apr 17
1/ Bayesian inference is a powerful statistical framework that allows us to estimate the probability distribution of parameters based on data and prior knowledge. And R has a variety of packages for implementing Bayesian analysis! #rstats #datascience #Bayesian
2/ First up, there's 'rstan', which provides R interfaces to the popular Stan modeling language. It includes a suite of powerful algorithms for Bayesian inference, including Markov Chain Monte Carlo (MCMC) sampling and Variational Bayes (VB). #rstats #stan
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Apr 16
1/ If you're looking for new options beyond Shiny for building web applications with R, this thread is for you! Let's explore some of the best Shiny alternatives out there. #rstats #datascience #python
2/ First up is Dash by Plotly. Dash is a Python framework for building analytical web applications. With its reactive components and easy-to-use syntax, it's a great alternative to Shiny. plotly.com/dash/
3/ Next on our list is Streamlit. Similar to Dash, Streamlit is a Python framework for building data-driven web applications. Its clean API and intuitive interface make it a popular choice among developers. streamlit.io
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Apr 16
Did you know that R is widely used in the pharmaceutical industry for data analysis, modeling, and visualization? Here are some ways that R is making a big impact in pharma: #rstats #datascience #pharma
1/ Clinical trials: R is used to analyze and visualize data from clinical trials, which are a critical component of the drug development process. R's flexibility and powerful statistical analysis capabilities make it an ideal tool for this task.
2/ Pharmacokinetics: R is used to model the concentration of drugs in the body over time, which is important for determining the correct dosage and frequency of administration. R's ability to handle complex models and large datasets makes it well-suited for this task.
Read 6 tweets

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