AI-Powered RNA-Seq Data Analysis Using R
Differential Gene Expression Analysis of RNA Sequencing Data Using Machine Learning/AI in R is a intermediate-level, 3 Days (1.5 hours per day) online course by NSTC.
- ModeRecorded Lectures
- LevelIntermediate
- Duration3 Days (1.5 hours per day)
| Attribute | Detail |
|---|---|
| Format | Recorded Lectures |
| Level | Intermediate |
| Duration | 3 Days (1.5 hours per day) |
| Certification | e-Certification + e-Marksheet |
| Fee | Free |
| Tools | Python Scikit-learn TensorFlow Keras Pandas NumPy Matplotlib XGBoost |
About the AI-Powered RNA-Seq Data Analysis Using R Course
Machine learning/AI is an influential tool in the analysis of RNA-Seq gene expression data. ML methods are broadly used to identify new biomarkers for disease diagnosis and treatment monitoring and to learn unseen patterns in gene expression that boost our understanding of the fundamental biological pathways. The success of an ML/AI model depends heavily on the input data.
Identifying an appropriate dataset can be a challenge, and the data must be selected carefully, as a predictive model trained on the unreliable or inappropriate data will produce unreliable predictions. The rations of machine learning analyses in public functional genomics repositories are encountered by rare curated ML/AI-ready datasets. Therefore, it is important to study a data set sensibly to confirm its reputation for a machine learning job. Bioconductor packages are used to show all differential gene expressions by generating the volcano map, Euclidean distances, and heatmap.
Program Highlights
• Comprehensive coverage of Differential Gene Expression Analysis of RNA Sequencing Data Using Machine Learning/AI in R from fundamentals to advanced applications
• Hands-on projects and real-world case studies in Machine Learning
• Expert-curated curriculum aligned with current industry standards
• Access to recorded lectures and e-LMS platform for flexible, self-paced learning
• e-Certification and e-Marksheet upon successful completion
• Dedicated mentor support and interactive doubt-clearing sessions
• Exposure to industry-standard tools and platforms used in Machine Learning
• Career-oriented training for academic and professional growth in Machine Learning
Course Curriculum
Module 1: Introduction to Differential Gene Expression Analysis of RNA Sequencing Data Using Machine Learning/AI in R
- Overview and historical evolution of Differential Gene Expression Analysis of RNA Sequencing Data Using Machine Learning/AI in R
- Key terminology, definitions, and core concepts in Machine Learning
- Current industry landscape, trends, and career opportunities
- Setting up the learning environment and essential tools
Module 2: Fundamentals and Theoretical Foundations
- Core principles and scientific/theoretical underpinnings of Differential Gene Expression Analysis of RNA Sequencing Data Using Machine Learning/AI in R
- Mathematical and analytical frameworks relevant to Machine Learning
- Comparative analysis of major approaches and methodologies
- Understanding key standards, guidelines, and best practices
Module 3: Supervised Learning
- Introduction to Supervised Learning concepts and methodologies
- Step-by-step practical implementation of Supervised Learning techniques
- Tools and platforms commonly used for Supervised Learning
- Troubleshooting, optimization, and best practices
Module 4: Unsupervised Learning
- Introduction to Unsupervised Learning concepts and methodologies
- Step-by-step practical implementation of Unsupervised Learning techniques
- Tools and platforms commonly used for Unsupervised Learning
- Troubleshooting, optimization, and best practices
Module 5: Feature Engineering
- Introduction to Feature Engineering concepts and methodologies
- Step-by-step practical implementation of Feature Engineering techniques
- Tools and platforms commonly used for Feature Engineering
- Troubleshooting, optimization, and best practices
Module 6: Advanced Topics and Emerging Trends in Machine Learning
- Cutting-edge research and innovations in Differential Gene Expression Analysis of RNA Sequencing Data Using Machine Learning/AI in R
- Integration with AI, automation, and modern technologies
- Industry case studies and real-world problem solving
- Future directions and career pathways in Machine Learning
Module 7: Capstone Project and Assessment
- End-to-end project implementation using Differential Gene Expression Analysis of RNA Sequencing Data Using Machine Learning/AI in R skills
- Peer review, collaborative exercises, and expert feedback
- Portfolio-ready project documentation and presentation
- Final assessment and course completion evaluation
