<![CDATA[BOL: Related items]]> https://bioinformaticsonline.com/related/43681? https://bioinformaticsonline.com/bookmarks/view/43369/a-guide-to-machine-learning-for-biologists Wed, 15 Sep 2021 13:21:08 -0500 https://bioinformaticsonline.com/bookmarks/view/43369/a-guide-to-machine-learning-for-biologists <![CDATA[A guide to machine learning for biologists]]> We aim to provide readers with a gentle introduction to a few key machine learning techniques, including the most recently developed and widely used techniques involving deep neural networks. We describe how different techniques may be suited to specific types of biological data, and also discuss some best practices and points to consider when one is embarking on experiments involving machine learning. Some emerging directions in machine learning methodology are also discussed.

Address of the bookmark: https://www.nature.com/articles/s41580-021-00407-0

]]> LEGE https://bioinformaticsonline.com/pages/view/35805/python-learning-resources-for-bioinformatics-and-computational-biologist Fri, 02 Mar 2018 06:54:15 -0600 https://bioinformaticsonline.com/pages/view/35805/python-learning-resources-for-bioinformatics-and-computational-biologist <![CDATA[Python learning resources for bioinformatics and computational biologist !]]> Python is a general-purpose language, which means it can be used to build just about anything, which will be made easy with the right tools/libraries.

Professionally, Python is great for backend web development, data analysis, artificial intelligence, and scientific computing. Many developers have also used Python to build productivity tools, games, and desktop apps, so there are plenty of resources to help you learn how to do those as well.

For pros and cons visit http://www.bestprogramminglanguagefor.me/why-learn-python and http://bioinformaticsonline.com/discussion/view/459/python-vs-perl

More resources at https://github.com/CodementorIO/Python-Learning-Resources

Following are the list of useful python programming resources:

]]> Jit https://bioinformaticsonline.com/bookmarks/view/43831/ten-quick-tips-for-deep-learning-in-biology Fri, 25 Mar 2022 18:35:12 -0500 https://bioinformaticsonline.com/bookmarks/view/43831/ten-quick-tips-for-deep-learning-in-biology <![CDATA[Ten quick tips for deep learning in biology]]> By taking a comprehensive and careful approach to deep learning based on critical thinking about research questions, planning to maintain rigor, and discerning how work might have far-reaching consequences with ethical dimensions, the life science community can advance reproducible, interpretable, and high-quality science that is enriching and beneficial for both scientists and society.

Address of the bookmark: https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1009803

]]> Neel 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/40489/machine-learning-training-and-courses-in-bioinformatics Tue, 31 Dec 2019 19:33:07 -0600 https://bioinformaticsonline.com/bookmarks/view/40489/machine-learning-training-and-courses-in-bioinformatics <![CDATA[Machine learning training and courses in bioinformatics !]]> Machine learning techniques have been successful in analyzing biological data because of their capabilities in handling randomness and uncertainty of data noise and in generalization. In this class, we will learn basics about probabilistic models and machine learning techniques. We will focus on probabilistic models (Markov models, Hidden Markov models, and Bayesian networks) for biological sequence analysis and systems biology. Other machine learning techniques, such as Naive bayes, neural networks and SVMs will only be covered briefly.

More at http://homes.sice.indiana.edu/yye/lab/teaching/spring2017-I529/

More tutorial at 

http://calla.rnet.missouri.edu/cheng_courses/mlbioinfo/mlbioinfo.htm

http://www.raetschlab.org/lectures/MLBioinformatics

http://www.raetschlab.org/lectures/bertinoro08

Book at 

https://personal.utdallas.edu/~pradiptaray/teaching/7_deep_learning_bioinfo.pdf

Address of the bookmark: http://homes.sice.indiana.edu/yye/lab/teaching/spring2017-I529/

]]> Rahul Nayak https://bioinformaticsonline.com/bookmarks/view/10925/a-brief-bioinformatics-tutorial Wed, 21 May 2014 12:50:09 -0500 https://bioinformaticsonline.com/bookmarks/view/10925/a-brief-bioinformatics-tutorial <![CDATA[A Brief Bioinformatics Tutorial]]> This is about how to use a computer to find what is known about a gene of interest and also how to get new insights about it.

The tutorial is divided in three main parts:

  • In the Sequence part, you will see how to look efficiently for a particular protein sequence, how to blast it against the database of your choice to find homologues, how to perform a multiple alignment of the homologues you've selected and how to edit this alignment.
  • The Structure part is about molecular visualization, homology modeling and structural domain prediction.
  • In the Function part, you will be introduced to you 3 useful servers to investigate the function of a protein. i.e. finding interactors, co-expressed genes, see a phylogenetic profile, easily access papers citing your gene etc ...

During all the three parts, we will use the S. cerevisiae VPS36 protein as an example.

Address of the bookmark: http://www.mrc-lmb.cam.ac.uk/rlw/text/bioinfo_tuto/introduction.html

]]> Jit https://bioinformaticsonline.com/pages/view/36603/learning-python-programming-a-bioinformatician-perspective Mon, 14 May 2018 16:33:03 -0500 https://bioinformaticsonline.com/pages/view/36603/learning-python-programming-a-bioinformatician-perspective <![CDATA[Learning Python Programming - a bioinformatician perspective !]]> Python Programming is a general purpose programming language that is open source, flexible, powerful and easy to use. One of the most important features of python is its rich set of utilities and libraries for data processing and analytics tasks. In the current era of big biological data, python and biopython is getting more popularity due to its easy-to-use features which supports big data processing.

In this tutorial series article, I will explore features and packages of python which are widely used in the big data, NGS, and bioinformatics. I will also walk through a real biological example which shows NGS data processing with the help of python packages and programming.

Python has a couple of points to recommend it to biologists and scientists specifically:

  • It's widely used in the scientific community
  • It has a couple of very well designed libraries for doing complex scientific computing (although we won't encounter them in this book)
  • It lend itself well to being integrated with other, existing tools
  • It has features which make it easy to manipulate strings of characters (for example, strings of DNA bases and protein amino acid residues, which we as biologists are particularly fond of)

In general, following are some of the important features of python which makes it a perfect fit for rapid application development.

  • Python is interpreted language so the program does not need to be compiled. Interpreter parses the program code and generates the output.
  • Python is dynamically typed, so the variables types are defined automatically.
  • Python is strongly typed. So the developers need to cast the type manually.
  • Less code and more use makes it more acceptable.
  • Python is portable, extendable and scalable.

There are two major Python versions, Python 2 and Python 3. Python 2 and 3 are quite different. This tutorial uses Python 3, because it more semantically correct and supports newer features.

I will post tutorial on daily basis on this page. Check the sub-pages on right side.

]]> Rahul Nayak 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/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/blog/view/43008/list-of-useful-machine-ai-learning-resources Tue, 30 Mar 2021 08:56:06 -0500 https://bioinformaticsonline.com/blog/view/43008/list-of-useful-machine-ai-learning-resources <![CDATA[List of useful machine / ai learning resources !]]> ML cheatsheet !

https://github.com/remicnrd/ml_cheatsheet

Visual AI / ML

https://setosa.io/ev/

Simple and efficient tools for predictive data analysis

https://scikit-learn.org/stable/

]]> Rahul Nayak