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Prediction of Immunogenic Response using Orange: A Machine Learning Tool
Unleash the Power of Machine Learning in Immunology with Orange
Virtual (Google Meet)
Mentor Based
Beginners
3 Days (1.5 Hours/Day)
25 – Jul – 24
08:00 PM onwards IST
About
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.
Workshop 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.
Workshop Structure
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
Participant’s Eligibility
- 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.
Important Dates
Registration Ends
2024-07-25
Indian Standard Timing 07:00 PM
Workshop Dates
2024-07-25 to 2024-07-27
Indian Standard Timing 08:00 PM onwards
Workshop 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.
Mentor Profile
Designation: Professor and Head
Affiliation: Department of Bioinformatics, Shalom New Life College, Bengaluru, Karnataka
Dr. Md Afroz Alam is a Professor and Head in the Department of Bioinformatics, at Shalom New Life College, Bengaluru, Karnataka. He received his Ph.D. Degree in Bioinformatics from Jaypee University of Information Technology, Solan, Himachal Pradesh in 2009. Then he has worked as Assistant Professor, Head and Program Coordinator in the Department of Bioinformatics at Karunya Institute of Technology and Sciences (Deemed University) for 11 Years. He is having 14 years of teaching and research experience in the field of Bioinformatics. His area of expertise includes: Computer Aided Drug Design, Molecular Modeling and Simulation, QSAR and Pharmacophore modeling, Biostatistics, R programming, Unix and Linux. He is the author of 24 research articles, 2 Book Chapter, recipient of short-term research grants, workshop grants, and National Youth leader award by Ministry of Youth Affairs and Sports under the National Service Scheme, Government of India.
Fee Structure
Student
INR. 1399
USD. 50
Ph.D. Scholar / Researcher
INR. 1699
USD. 55
Academician / Faculty
INR. 2199
USD. 60
Industry Professional
INR. 2699
USD. 85
List of Currencies
Certificate
- Access to Live Lectures
- Access to Recorded Sessions
- e-Certificate
- Query Solving Post Workshop
Future Career Prospects
- Bioinformatics Analyst
- Data Scientist in Biotechnology
- Research Scientist in Immunology
- Machine Learning Engineer in Healthcare
- Computational Biologist
- Pharmaceutical Data Analyst
Current Participants* Analytics
Country
Profession
Affiliation
Note: The information shown in the above-mentioned analytics is live and may include information that is not completely correct like spelling mistakes, grammatical mistakes , factual errors or even mis representation as this is what participants have entered, the information is currently not edited and or filtered , but at later stages they will be filtered to provide true data representation.
Enter the Hall of Fame!
Take your research to the next level!
Achieve excellence and solidify your reputation among the elite!
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Recent Feedbacks In Other Workshops
The course was more theoretical than practical.
nothing
