**Scientific topics**:
Statistics and probability

The questions this addresses are:

- How to use Jupyterlab and it several features?

- How to use it for creating input datasets and writing artificial intelligence (AI) algorithms?

\nThe objectives are:

- Learn to use Jupyterlab - an online Python editor designed for developing AI algorithms

...

The questions this addresses are:

- How to predict aberrant pathway activity in The Cancer Genome Atlas (TCGA) using Machine learning approaches?

\nThe objectives are:

- Learn to predict aberrant pathway activity using RNA-Seq data, mutational status and copy number variation data from TCGA.

...

The questions this addresses are:

- What is classification and how we can use classification techniques?

\nThe objectives are:

- Learn classification background

- Learn what a quantitative structure-analysis relationship (QSAR) model is and how it can be constructed in Galaxy

- Learn to...

The questions this addresses are:

- How to use regression techniques to create predictive models from biological datasets?

\nThe objectives are:

- Learn regression background

- Apply regression based machine learning algorithms

- Learn ageing biomarkers and predict age from DNA methylation...

The questions this addresses are:

- How can I automatically collect PubMed data for a set of biomedical entities such as genes?

- How can I analyze similarities among biomedical entities based on PubMed data on large-scale?

\nThe objectives are:

- Learn how to use the SimText toolset

-...

The questions this addresses are:

- what are classification and regression techniques?

- How they can be used for prediction?

- How visualizations can be used to analyze predictions?

\nThe objectives are:

- Explain the types of supervised machine learning - classification and regression.

-...

The questions this addresses are:

- How to use machine learning to create predictive models from biological datasets (RNA-seq and DNA methylation)?

\nThe objectives are:

- Learn ageing biomarkers from RNA-seq and DNA methylation datasets

- Apply regression based machine learning algorithms

...

The questions this addresses are:

- How to visualize high-resolution omics data in different groups of genomic regions?

- How to evaluate differences in high-resolution omics data between groups of genomic regions?

- How to detect locations and scales at which the significant effects...

The questions this addresses are:

- What are the main categories in Machine Learning algorithms?

- How can I perform exploratory data analysis?

- What are the main part of a clustering process?

- How can a create a decision tree?

- How can I assess a linear regression model?

\nThe...

The questions this addresses are:

- What is machine learning?

- Why is it useful?

- What are its different approaches?

\nThe objectives are:

- Provide the basics of machine learning and its variants.

- Learn how to do classification using the training and test data.

- Learn how to use...

The questions this addresses are:

- How to solve an image classification problem using convolutional neural network (CNN)?

\nThe objectives are:

- Learn how to create a CNN using Galaxy's deep learning tools

- Solve an image classification problem on fruit 360 dataset using CNN in Galaxy

The questions this addresses are:

- How to use clustering algorithms to categorize data in different clusters

\nThe objectives are:

- Learn clustering background

- Learn hierarchical clustering algorithm

- Learn k-means clustering algorithm

- Learn DBSCAN clustering algorithm

- Apply...

The questions this addresses are:

- What is a recurrent neural network (RNN)?

- What are some applications of RNN?

\nThe objectives are:

- Understand the difference between feedforward neural networks (FNN) and RNN

- Learn various RNN types and architectures

- Learn how to create a neural...

The questions this addresses are:

- What is a recurrent neural network (RNN)?

- What are some applications of RNN?

\nThe objectives are:

- Understand the difference between feedforward neural networks (FNN) and RNN

- Learn various RNN types and architectures

- Learn how to create a neural...

The questions this addresses are:

- What is a feedforward neural network (FNN)?

- What are some applications of FNN?

\nThe objectives are:

- Understand the inspiration for neural networks

- Learn various activation functions, and classification/regression problems solved by neural networks

...

The questions this addresses are:

- What is a convolutional neural network (CNN)?

- What are some applications of CNN?

\nThe objectives are:

- Understand the inspiration behind CNN and learn the CNN architecture

- Learn the convolution operation and its parameters

- Learn how to create a...

The questions this addresses are:

- What is a convolutional neural network (CNN)?

- What are some applications of CNN?

\nThe objectives are:

- Understand the inspiration behind CNN and learn the CNN architecture

- Learn the convolution operation and its parameters

- Learn how to create a...

The questions this addresses are:

- How to solve an image classification problem using convolutional neural network (CNN)?

\nThe objectives are:

- Learn how to create a CNN using Galaxy's deep learning tools

- Solve an image classification problem on fruit 360 dataset using CNN in Galaxy

The questions this addresses are:

- What are deep learning and neural networks?

- Why is it useful?

- How to create a neural network architecture for classification?

\nThe objectives are:

- Learn basic principles of deep learning

- Learn about how to create an end-to-end neural network...

The questions this addresses are:

- What is a feedforward neural network (FNN)?

- What are some applications of FNN?

\nThe objectives are:

- Understand the inspiration for neural networks

- Learn activation functions & various problems solved by neural networks

- Discuss various...

Materials created at the Machine Learning and BioStatistics hackathon organised by ELIXIR-GR (CERTH) in October and November 2020.

Boolean modelling uses a simple representation of biological entities as either active or inactive, and describes their relations with logical formulas. MaBoSS extends Boolean modelling by adding a notion of continuous time, with the introduction of rates of (in)activation. This enable the...

This one-week intensive summer school in bioinformatics will focus on data analysis and high throughput biology, with a special focus on R/Bioconductor and its application to a wide range of topics across bioinformatics and computational biology. The course is intended for researchers who are...

Bioconductor provides tools for the analysis and comprehension of high-throughput genomic data.

Bioconductor uses the R statistical programming language, and is open source and open development.

It has two releases each year, 1560 software packages, and an...

Statistics are an important part of most modern studies and being able to effectively use a statistics package can help you to understand your results. This course provides an introduction to statistics illustrated though the use of the friendly SPSS package.

**Course Content**

*...

## Overview

R is a programming language and associated environment developed for statistical computing and data analysis. It provides many powerful tools for statistics, data visualisation and bioinformatics.

## Learning outcomes

- What R is suitable for.
- How to use R...

The aim of this course is to teach you how to perform basic statistical analysis using R. First we review the foundations (sampling theory, discrete and continuous distributions), then we focus on classical hypothesis testing. This course will improve your generic statistics knowledge....