Instruct-ERIC Events

Protein Function Prediction with Machine Learning and Interactive Analytics

Registration Date: 21-Feb-2019 to 11-May-2019
Date: 29-May-2019 to 31-May-2019

Contact: Madalena Gallagher


Do you want to learn how to develop models to predict protein function? Do you want analyse and exploit the growing volume of biological data? Do you want to develop basic skills in novel machine learning approaches and big data technologies?
This workshop explores how to conduct functional annotation of proteins through machine learning (ML) approaches. Participants will gain an insight into existing public protein data resources; and how novel approaches can be used to analyse and explore these data to gain new understanding of protein function. The workshop will introduce Apache Spark and Apache Zeppelin; technologies for fast data processing and integrating analytics respectively.

This workshop is aimed at researchers and bioinformaticians from across industry and academia who are looking to leverage machine learning approaches in protein function prediction. It will guide participants through the use of big data to build analytical workflows on publically-available biological data.
Participants will require prior experience in the use of the command line interface and confidence in a programming language to fully benefit from the workshop. Please contact us if you have any questions about the course's suitability before you apply.
Syllabus, tools and resources
The workshop will cover the following topics:
UniProt knowledgebase
Apache Zeppelin framework
Apache Spark
Machine learning approaches
Protein functional annotation
After this course you should be able to:
Search and locate protein data of interest
Conduct interactive analytics and data transformation using machine learning approaches
Create simple analytical workflows using publically-available data
Discern new biological insights about protein function
Develop models for predicting protein function


To register and for more information click here

Cambridge, United Kingdom

Protein Function Prediction with Machine Learning and Interactive Analytics