BOL: Related items

Machine Learning for Genomics

Jit — Thu, 09 Sep 2021 11:26:32 -0500

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

Machine learning in Perl

Jit — Sun, 16 Feb 2020 15:32:03 -0600

this is a fourth blog post in the Machine learning in Perl series, focusing on the AI::MXNet, a Perl interface to Apache MXNet, a modern and powerful machine learning library.

If you're interested in refreshing your memory or just new to the series, please check previous entries over here: 1 2 3

https://metacpan.org/pod/AI::MXNet

Address of the bookmark: http://blogs.perl.org/users/sergey_kolychev/2018/07/machine-learning-in-perl-kyuubi-goes-to-a-modelzoo-during-the-starry-night.html

Bioinfo Lab

Fri, 04 Mar 2022 00:17:00 -0600

The Institute of Bioinformatics conducts internationally renowned research and provides profound education in bioinformatics. Its research focuses on development and application of machine learning and statistical methods in biology and medicine.

Contact:
Computer Science Building (Science Park 3)
Altenberger Str. 69, A-4040 Linz, Austria
Tel. +43 732 2468 4520 / Fax +43 732 2468 4539
E-mail secretary@bioinf.jku.at

http://www.bioinf.jku.at/

A guide to machine learning for biologists

Abhi — Tue, 28 Dec 2021 01:43:25 -0600

Because of the increasing size and inherent complexity of biological data, there has been an increase in the application of machine learning in biology to create useful and predictive models of the underlying biological processes. All machine learning techniques fit models to data; nevertheless, the specific methods are highly variable and can appear baffling at first glance. In this Review, we hope to give readers a moderate introduction to a few fundamental machine learning techniques, including the most recently created and frequently used deep neural network techniques. We illustrate how different algorithms may be adapted to specific types of biological data, as well as some best practises and points to consider when embarking on machine learning studies. There is also discussion of several upcoming directions in machine learning methodology.

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

Machine learning training and courses in bioinformatics !

Rahul Nayak — Tue, 31 Dec 2019 19:33:07 -0600

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/

modelStudio: a package automates the explanation of machine learning predictive models

Jit — Wed, 22 Jan 2020 23:58:11 -0600

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

CrowdGO: Machine learning and semantic similarity guided consensus Gene Ontology annotation

Shruti Paniwala — Thu, 26 May 2022 00:59:49 -0500

CrowdGO is a protein Gene Ontology predictor using a meta approach, analyzing the predictions of other tools in order to get an improved precision and recall.

Please note that the CrowdGO snakemake workflow is currently only tested on Ubuntu. It should work on OSX, but please report any errors to maarten.reijnders@unil.ch or create an issue.

https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1010075

Address of the bookmark: https://gitlab.com/mreijnders/crowdgo

Venn Diagrams on R Studio

Jitendra Prajapati — Mon, 25 Apr 2016 16:22:28 -0500

First step: Install & load “VennDiagram” package.

# install.packages('VennDiagram')
library(VennDiagram)

Second step: Load data

Add filepath if “catdoge.csv” is not in working-directory.

d <- read.csv("catdoge.csv")

Address of the bookmark: http://rstudio-pubs-static.s3.amazonaws.com/13301_6641d73cfac741a59c0a851feb99e98b.html

A guide to machine learning for biologists

LEGE — Wed, 15 Sep 2021 13:21:08 -0500

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

Powerful books for learning data analysis with R

LEGE — Tue, 28 May 2024 07:42:56 -0500

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/