<![CDATA[BOL: Related items]]> https://bioinformaticsonline.com/related/43831?offset=200 https://bioinformaticsonline.com/videolist/watch/14218/pimp-your-brain-bioinformatics Wed, 20 Aug 2014 22:09:21 -0500 https://bioinformaticsonline.com/videolist/watch/14218/pimp-your-brain-bioinformatics <![CDATA[Pimp your brain: Bioinformatics]]> Jan Lisec from the Max Planck Institute of Molecular Plant Physiology explains, in this "pimp your brain" episode, what bioinformatics is and why bioinformatics is so important and indispensable for biological research. In the video serial "Pimp your brain" scientists from the Max Planck Institute of Molecular Plant Physiology describe their research. More videos from the 'Pimp your brain' serial are available on www.youtube.com/playlist?list=PL-l9VItC9Gn2Ur2Xj6PTOAkjLUlVPbIOO More videos are available on www.mpimp-golm.mpg.de]]> https://bioinformaticsonline.com/bookmarks/view/43362/machine-learning-for-genomics Thu, 09 Sep 2021 11:26:32 -0500 https://bioinformaticsonline.com/bookmarks/view/43362/machine-learning-for-genomics <![CDATA[Machine Learning for Genomics]]> Module 1: Statistics for genomics (2-8 August 2021)
  • A simple intro to statistical distributions
  • hypothesis testing
  • linear models.

reading: http://compgenomr.github.io/book/stats.html

slides: https://github.com/BIMSBbioinfo/compgen2021/tree/main/week1/compgen2021_stats.pdf

exercises+code: https://github.com/BIMSBbioinfo/compgen2021/tree/main/week1/

Module 2: Unsupervised learning for genomics (9-15 August 2021)

  • Understanding basic intuition behind machine learning approaches.
  • Using unsupervised learning to cluster and visualise data points
  • Dimension reduction techniques for visualisation and as input to clustering methods

reading: http://compgenomr.github.io/book/unsupervisedLearning.html

slides: https://github.com/BIMSBbioinfo/compgen2021/tree/main/week2/compgen2021_unsupervisedLearning.pdf

exercises+code: https://github.com/BIMSBbioinfo/compgen2021/tree/main/week2/

Module 3: Supervised learning for genomics (16-22 August 2021)

  • Understanding and using supervised learning methods for predictive purposes
  • How to measure prediction performance
  • Understand and use cross-validation and related concepts

reading: http://compgenomr.github.io/book/supervisedLearning.html

slides: https://github.com/BIMSBbioinfo/compgen2021/tree/main/week3/compgen2021_supervisedLearning.pdf

exercises+code: https://github.com/BIMSBbioinfo/compgen2021/tree/main/week3/

https://github.com/BIMSBbioinfo/compgen2021

Address of the bookmark: https://github.com/BIMSBbioinfo/compgen2021

]]> Jit https://bioinformaticsonline.com/bookmarks/view/40591/modelstudio-a-package-automates-the-explanation-of-machine-learning-predictive-models Wed, 22 Jan 2020 23:58:11 -0600 https://bioinformaticsonline.com/bookmarks/view/40591/modelstudio-a-package-automates-the-explanation-of-machine-learning-predictive-models <![CDATA[modelStudio: a package automates the explanation of machine learning predictive models]]> The modelStudio package automates the explanation of machine learning predictive models. This package generates advanced interactive and animated model explanations in the form of a serverless HTML site.

It combines R with D3.js to produce plots and descriptions for various local and global explanations. Tools for model exploration unite with tools for EDA (Exploratory Data Analysis) to give a broad overview of the model behavior. modelStudio is a fast and condensed way to get all the answers without much effort. Break down your model and look into its ingredients with only a few lines of code.

Address of the bookmark: https://modeloriented.github.io/modelStudio/index.html

]]> Jit https://bioinformaticsonline.com/bookmarks/view/44541/powerful-books-for-learning-data-analysis-with-r Tue, 28 May 2024 07:42:56 -0500 https://bioinformaticsonline.com/bookmarks/view/44541/powerful-books-for-learning-data-analysis-with-r <![CDATA[Powerful books for learning data analysis with R]]> R is powerful tool for data analysis, visualization, and machine learning. And it costs $0 to use! Here are six FREE books you can use to learn R today:

https://csgillespie.github.io/efficientR/

https://r-graphics.org/

https://rstudio-education.github.io/hopr/

https://r-pkgs.org/

https://r4ds.had.co.nz/

 

Address of the bookmark: https://r-graphics.org/

]]> LEGE https://bioinformaticsonline.com/pages/view/41220/a-quick-guide-to-phred-scaling Sat, 22 Feb 2020 02:57:19 -0600 https://bioinformaticsonline.com/pages/view/41220/a-quick-guide-to-phred-scaling <![CDATA[A quick guide to Phred scaling]]> A quick guide to Phred scaling

• Phred value = −10 * log10(ε)

• Examples:
• 90% confidence (10% error rate) = Q10
• 99% confidence (1% error rate) = Q20
• 99.9% confidence (.1% error rate) = Q30

• SAM encoding adds 33 to the value (because
ASCII 33 is the first visible character)

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