Prediction of Immunogenic Response using Orange: A Machine Learning Tool
Unleash the Power of Machine Learning in Immunology with Orange
Skills you will gain:
About Program:
Orange is an open-source data visualization, machine learning and data mining toolkit. It features a visual programming front-end for explorative qualitative data analysis and interactive data visualization. Orange. Developer(s) University of Ljubljana. Orange is a visual programming environment for data science and machine learning projects. The machine learning based prediction algorithms such as tree, logistic regression, random forest, and SVM may be used and validate with leave one out (LOO), random sampling and cross validation test-scoring methods to identify the immunogenic response.
Aim: To develop an effective prediction model by using a large number of feature selection and classification methods.
Program Objectives:
- To make computational predictions about antigenicity of peptides by developing a computational model using the training and testing data set.
- To predict the features that are effective in identifying the immunogenic response.
What you will learn?
Day 1:
- Orange 3 introduction
- Overview of Orange3 and simulated data set of protein
- Overview of machine learning algorithm
Day 2:
- Machine learning methods and prediction model
- Prediction model based on Tree Classification, Logistics Regression
- Prediction model based on Random Forest, SVM
Day 3:
- Feature Ranking and Visualization
- PCA, Hierarchical Clustering
- Feature Ranking and Scoring
Mentor Profile
Fee Plan
Get an e-Certificate of Participation!
Intended For :
- Undergraduate degree in Bioinformatics, Biotechnology, Computer Science, or related fields.
- Professionals in the pharmaceutical or biotechnology industries.
- Individuals with a keen interest in machine learning and immunology.
Career Supporting Skills
Program Outcomes
- Ability to develop computational models for predicting immunogenic response.
- Proficiency in using Orange for data visualization and machine learning.
- Knowledge of various machine learning algorithms and their applications.
- Enhanced skills in feature selection and classification methods.
- Practical experience in validating prediction models.