Graduate Diploma in AI in Cancer Genetics
Program Overview
The Graduate Diploma in AI in Cancer Genetics is a 9-month, full-time, intensive, and research-oriented program designed to equip students with practical, industry-relevant skills at the intersection of cancer genetics and artificial intelligence. This program is ideal for students, recent graduates, and professionals aiming to enter cancer research, precision medicine, and AI-driven healthcare.
The program is structured into three modules, progressively building expertise:
Module 1: Genetics & Genomics (Months 1–3, Online)
This module provides a comprehensive foundation in cancer genetics and genomics. Students will learn the molecular basis of cancer, the role of mutations, and gene-level mechanisms driving disease. The courses include:
- Basics of Genetics
- Human Genomics
- Genetic Engineering
- Mutations and Mutational Analysis
- Cancer Genetics
- Gene Testing and Therapy
Students engage in interactive exercises, case studies, and assignments to gain confidence in interpreting gene-level data, understanding hereditary cancer syndromes, and applying foundational knowledge to real-world cancer scenarios.
Module 2: Fundamentals of AI (Months 4–6, Online)
This module equips students with practical AI and machine learning skills essential for analyzing gene-level cancer data. Key topics include:
- Introduction to AI & Machine Learning
- Supervised Learning (Random Forest, SVM, Decision Trees, Logistic Regression)
- Unsupervised Learning (Clustering, PCA)
- Neural Networks and Deep Learning Basics
- Data Preprocessing and Feature Engineering
- Model Evaluation and Validation
Students apply AI techniques to mutation classification, gene expression analysis, and cancer subtype prediction, completing hands-on mini-projects to build proficiency in real-world applications.
Module 3: AI in Cancer Genetics (Months 7–9, Online/Project-Based)
The final module focuses on practical AI applications in cancer genetics. Students integrate knowledge from Modules 1 and 2 to conduct end-to-end analysis on gene-level cancer datasets. Courses include:
- AI in Tumor Genetics
- Variant Classification Using AI
- Cancer Subtype Prediction
- Biomarker Discovery Using AI Tools
- AI Workflow on Real Cancer Genetics Datasets
- Capstone Project: Comprehensive AI Application
The capstone project enables students to demonstrate competency in gene-level data analysis, AI model development, and interpretation, preparing them for careers or further research in cancer genetics and precision medicine.
Learning Outcomes
By the end of the program, graduates will be able to:
- Genetic Knowledge
- Understand the molecular and genetic basis of cancer at the gene level.
- Explain types of mutations, their mechanisms, and their impact on tumor development.
- Interpret genetic testing results and apply knowledge to cancer risk assessment
- AI & Computational Skills
- Apply supervised and unsupervised machine learning techniques to cancer genetics datasets.
- Develop, train, and validate AI models for variant classification and cancer subtype prediction.
- Conduct data preprocessing, feature engineering, and quality control on gene-level datasets
- Practical Application
- Implement AI workflows to analyze tumor genomics datasets.
- Identify potential biomarkers and gene signatures relevant for diagnostics and therapeutic strategies.
- Design and execute projects integrating AI and cancer genetics insights.
- Research Competency
- Conduct independent, project-based research in cancer genetics using AI tools.
- Critically evaluate results and interpret findings in the context of precision medicine.
- Produce a portfolio-ready capstone project demonstrating applied AI in cancer genetics
Phase 1 - Genetics & Genomics - 3 months online
BASICS OF GENETICS
GENETIC ENGINEERING
GENETIC ENGINEERING
- Introduction to DNA, RNA, and protein synthesis
- Mendelian and non-Mendelian inheritance patterns
- Chromosome structure, gene function, and gene expression
- Overview of genetic variation and polymorphisms
GENETIC ENGINEERING
GENETIC ENGINEERING
GENETIC ENGINEERING
- Principles and tools of genetic manipulation (CRISPR, TALENs, RNAi)
- Applications in research, diagnostics, and therapeutics
- Ethical considerations and regulatory aspects of genome editin
- Hands-on project: designing a gene-editing experiment for research purposes
HUMAN GENOMICS
GENETIC ENGINEERING
MUTATIONS AND MUTATIONAL ANALYSIS
- Structure and function of the human genome
- Genome organization, epigenetics, and regulatory elements
- Understanding genomic databases and reference genomes
- Introduction to population genomics and genome-wide association studies (GWAS)
- Practical: exploring human genome datasets online
MUTATIONS AND MUTATIONAL ANALYSIS
MUTATIONS AND MUTATIONAL ANALYSIS
MUTATIONS AND MUTATIONAL ANALYSIS
- Types of mutations: point mutations, insertions, deletions, and chromosomal aberrations
- Mutation detection methods: PCR, sequencing, and in silico approaches
- Understanding mutation effects on protein function and disease
- Practical exercises: analyzing mutation data using sample datasets
CANCER GENETICS
MUTATIONS AND MUTATIONAL ANALYSIS
GENETIC TESTING & GENE THERAPY
- Genetic basis of tumorigenesis and cancer development
- Oncogenes, tumor suppressor genes, and signaling pathways
- Hereditary cancer syndromes and germline mutations
- Case studies: linking mutations to specific cancer types
GENETIC TESTING & GENE THERAPY
MUTATIONS AND MUTATIONAL ANALYSIS
GENETIC TESTING & GENE THERAPY
- Principles of genetic testing and diagnostics
- Molecular assays for cancer predisposition and somatic mutations
- Basics of gene therapy approaches in cancer treatment
- Regulatory, ethical, and clinical considerations
Phase 2 - AI & its applications - 3 months online
AI & MACHINE LEARNING
UNSUPERVISED LEARNING ALGORITHMS
SUPERVISED LEARNING ALGORITHMS
- Fundamentals of Artificial Intelligence and its relevance to genomics
- Overview of machine learning types: supervised, unsupervised, and reinforcement learning
- AI in healthcare and cancer research applications
- Hands-on: basic AI workflow using example datasets
SUPERVISED LEARNING ALGORITHMS
UNSUPERVISED LEARNING ALGORITHMS
SUPERVISED LEARNING ALGORITHMS
- Overview of Random Forest, Decision Trees, Support Vector Machines, and Logistic Regression
- Application to classification problems in genetics
- Model building, evaluation, and performance metrics
- Hands-on: classifying cancer mutation datasets
UNSUPERVISED LEARNING ALGORITHMS
UNSUPERVISED LEARNING ALGORITHMS
NEURAL NETWORKS AND DEEP LEARNING BASICS
- Clustering techniques: k-means, hierarchical clustering
- Dimensionality reduction methods: PCA, t-SNE
- Identifying patterns in high-dimensional genomic data
- Hands-on: clustering tumor gene expression data
NEURAL NETWORKS AND DEEP LEARNING BASICS
DATA PREPROCESSING AND FEATURE ENGINEERING
NEURAL NETWORKS AND DEEP LEARNING BASICS
- Introduction to neural networks, layers, and activation functions
- Convolutional and recurrent neural networks overview
- Applications of deep learning in genomics and cancer research
- Practical: simple neural network for variant prediction
DATA PREPROCESSING AND FEATURE ENGINEERING
DATA PREPROCESSING AND FEATURE ENGINEERING
DATA PREPROCESSING AND FEATURE ENGINEERING
- Cleaning, normalizing, and transforming genomic datasets
- Feature selection and extraction for AI models
- Handling missing data and noise in real-world datasets
- Hands-on: preprocessing mutation and gene expression data
MODEL EVALUATION AND VALIDATION
DATA PREPROCESSING AND FEATURE ENGINEERING
DATA PREPROCESSING AND FEATURE ENGINEERING
- Metrics for classification and regression models: accuracy, precision, recall, ROC-AU
- Cross-validation, overfitting, and model tuning
- Interpreting AI model outputs for clinical relevance
- Practical: evaluating AI model performance on cancer datasets
Phase 3: AI in Cancer Genetics - 3 months
AI IN TUMOR GENETICS
VARIANT CLASSIFICATION USING AI
VARIANT CLASSIFICATION USING AI
- Application of AI to understand tumor genome dynamics
- Identifying driver mutations and gene signatures
- AI-assisted prediction of tumor behavior and progression
- Hands-on: analyzing tumor mutation datasets
VARIANT CLASSIFICATION USING AI
VARIANT CLASSIFICATION USING AI
VARIANT CLASSIFICATION USING AI
- AI approaches to classify germline and somatic variants
- Pathogenicity prediction and clinical significance assignment
- Integration of multiple genomic features into models
- Practical: building a variant classification workflow
CANCER SUBTYPE PREDICTION
VARIANT CLASSIFICATION USING AI
BIOMARKER DISCOVERY USING AI TOOLS
- AI-based prediction of cancer subtypes using gene expression and mutation profiles
- Biomarker identification for subtype-specific therapy
- Case studies: breast, colorectal, and brain cancers
- Hands-on: training models to predict cancer subtypes
BIOMARKER DISCOVERY USING AI TOOLS
CAPSTONE PROECT: COMPREHENSIVE AI APPLICAITON
BIOMARKER DISCOVERY USING AI TOOLS
- AI techniques for identifying diagnostic and prognostic biomarkers
- Integrating multi-omics data: genomics, transcriptomics, and epigenomics
- Validation of AI-discovered biomarkers using real datasets
- Practical: discovering potential biomarkers in cancer datasets
AI WORKFLOW ON REAL CANCER GENETICS DATASETS
CAPSTONE PROECT: COMPREHENSIVE AI APPLICAITON
CAPSTONE PROECT: COMPREHENSIVE AI APPLICAITON
- End-to-end workflow: data acquisition → preprocessing → model training → interpretation
- Applying learned AI techniques on real-world cancer datasets
- Hands-on project: complete workflow from raw data to actionable insights
- Interpretation and Reporting of Results
CAPSTONE PROECT: COMPREHENSIVE AI APPLICAITON
CAPSTONE PROECT: COMPREHENSIVE AI APPLICAITON
CAPSTONE PROECT: COMPREHENSIVE AI APPLICAITON
- Independent project integrating knowledge from Modules 1 & 2
- Designing and executing a full AI solution for a cancer genetics problem
- Deliverables include report, model, and presentation of gene-level insights
- Mentorship and feedback provided throughout the project
GD in AI in Cancer Genetics
$1998 for 9 months full program
- Phase 1 – $648 for 3 months
- Phase 2 – $648 for 3 months
- Phase 3 – $648 for 3 months
- Registration Fee: $54
TOTAL TUITION FEE PAYABLE IS $1998