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.

To develop an effective prediction model by using a large number of feature selection and classification methods.

• 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.

• 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.

• 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.