AI ML in Cancer Genomics Hands-on Training With Research Projects + Paper Publication Support

ADMISSIONS OPEN - AI & Machine Learning in Cancer Genomics Hands-on Training

Learn how modern cancer research is actually done using AI, real genomic data & publishable projects

Real TCGA/ICGC datasets | Multi-omics | Deep Learning | GNNs | Transformers | Paper Publication mentoring | 100% Placement Assistance

3,6 & 12 Months Project Work - Get Work Experience letter

STARTS 3rd MARCH 2026

AI in Cancer Genomics is a research-focused, hands-on training program designed to help life science, biotech, and data science learners master cancer genomics analysis using Machine Learning & Deep Learning.

This is not a theory-heavy course. You will work on real TCGA / ICGC cancer datasets, build ML & DL models, and complete a capstone research project with paper publication guidance.

Program Details:

• Program Name: AI in Cancer Genomics – Hands-on Training + Project Work + Paper Publication
• Duration: 35 Days
  Format: Hands-on training + project mentoring
• Mode: Online LIVE + Recorded Sessions
• Time: 7:00 pm to 8:00 PM IST (Mon-Fri)
• Datasets: TCGA, ICGC, COSMIC, CCLE, GDSC + clinical annotations
• Outcome: Portfolio-ready project + manuscript drafting guidance + certificate

Why is this training different?

Most courses stop at tools or theory. 

This program trains you to think and work like a cancer genomics researcher.

• End-to-end cancer genomics pipeline (FASTQ → BAM → VCF → ML models)
• Real cancer datasets (TCGA, ICGC, GDSC, CCLE)
• AI + ML + Deep Learning — not just basic bioinformatics
• Multi-omics learning (mutation, expression, CNV, clinical data)
• Capstone project designed for publication
• Mentor-guided project & paper writing support
• Industry + research aligned curriculum
• Ethical AI & regulatory perspective (FDA, ICMR)

👉 You don’t just learn, you build, analyze, interpret & publish.

Who Is This Program For? (Eligibility)

This program is ideal for:

• Life Science / Biotechnology / Genetics / Microbiology / Bioinformatics students
• MSc / PhD scholars aiming for cancer genomics or AI-based research
• Biologists transitioning into AI & Data Science
• Bioinformaticians wanting to upskill into ML/DL
• Data scientists interested in healthcare & cancer research
• Working professionals in pharma, diagnostics, CROs, or research labs

Prerequisites 

• Basic biology knowledge (genes, DNA, mutations)
• No prior ML/DL expertise required, we start from fundamentals
• Python basics helpful but covered in training

What Will You Learn?

By the end of this program, you will be able to:

• Analyze cancer genomic datasets independently
• Build ML & DL models for cancer classification & prediction
• Perform mutation analysis, survival analysis & subtype classification
• Work with multi-omics cancer data
• Apply AI in precision oncology & drug response prediction
• Design a research-grade project
• Prepare a manuscript suitable for journal/conference submission

DOWNLOAD BROCHURE FOR COMPLETE PROJECT & COURSE DETAILS

Course Structure & Modules

🔹 Module 1 — Foundations (Genomics + Python + ML Basics)

Days 1–9

• Cancer genomics fundamentals
• Genomic alterations: SNVs, INDELs, CNVs, SVs
• Tumor heterogeneity & clonal evolution
• File formats: FASTQ, BAM, VCF, GTF, BED
• Tools: GATK, bcftools, samtools, cBioPortal, Xenabrowser
• Python for genomics (NumPy, Pandas, Biopython)
• End-to-end genomics pipeline
• Statistics for genomics
• ML basics & feature engineering
• TCGA / ICGC dataset exploration (hands-on)

🔹 Module 2 — Machine Learning for Cancer Genomics (Intermediate)

Days 11–18

• SVM, Random Forest, XGBoost
• Model evaluation: ROC-AUC, F1, Recall
• PCA, t-SNE, UMAP
• Cancer sample clustering
• Survival analysis (Kaplan-Meier, Cox regression)
• Mutational signatures (COSMIC, SigProfiler)
• Multi-omics integration
• Project: Cancer subtype classification using TCGA data

🔹 Module 3 — Deep Learning for Cancer Genomics

Days 19–26

• Deep learning fundamentals (PyTorch / TensorFlow)
• CNNs for genomic sequences
• RNNs & LSTMs for mutation prediction
• Transformers in genomics (DNABERT, GenomicBERT)
• Variational Autoencoders (VAE)
• Graph Neural Networks (GNNs) for gene networks
• Project: DL model for cancer vs normal classification

🔹 Module 4 — Advanced Cancer Genomics & Clinical AI

Days 27–33

• Driver mutation prediction
• Tumor microenvironment analysis
• Single-cell cancer genomics + ML
• Drug response & toxicity prediction
• Precision oncology pipelines
• AI in clinical diagnostics & liquid biopsy
• Explainable AI (SHAP, LIME)
• Ethics, bias & regulatory standards (FDA, ICMR)

Capstone Project & Research Work

Days 34–35 + Project Phase

You will:

• Select a research-oriented cancer genomics problem
• Work on real patient-derived datasets
• Build ML/DL models
• Perform statistical & biological interpretation
• Create figures, results & discussion
• Receive paper writing & publication guidance

Paper Publication Support

• Journal/conference shortlisting guidance
• Manuscript structure & writing support
• Figure & result presentation review
• Ethical & reproducibility guidance
• Publication-ready project mentoring

• Publication acceptance depends on journal review — we guide you end-to-end.

Tools & Technologies You’ll Use

• Python, NumPy, Pandas, Biopython
• GATK, samtools, bcftools
• TCGA, ICGC, COSMIC, GDSC, CCLE
• Scikit-learn, XGBoost
• PyTorch / TensorFlow
• SHAP, LIME
• cBioPortal, Xenabrowser

What You Get?

• Live + recorded expert-led sessions
• Hands-on coding & real data practice
• Capstone research project
• Paper publication mentorship
• Certificate of completion
• Career guidance for PhD, research & industry roles

Career Outcomes

• After this program, you can target roles like:
• Cancer Bioinformatician
• Computational Biologist
• Genomics Data Scientist
• AI Research Associate
• Precision Oncology Analyst
• PhD / Research Fellow (Cancer Genomics)

Ready to Take the Next Step in AI-Driven Cancer Genomics?

This program is designed for learners who want more than surface-level bioinformatics and aim to build real research depth, work with complex cancer data, and contribute to meaningful scientific outcomes.

Whether your goal is a PhD, research role, or advanced industry position, this program equips you with the skills, project experience, and mentorship required to move forward with confidence.

Frequently Asked Questions (FAQs)

1. Is this an AI course or a Machine Learning course?

This program covers the full AI spectrum, including:

• Classical Machine Learning
• Deep Learning
• Advanced AI architectures such as Transformers, GNNs, and VAEs
  All methods are taught in the context of cancer genomics.

2. Will I work with real cancer genomics data?

Yes. Hands-on training and projects involve real public cancer genomics datasets, including TCGA-derived datasets, COSMIC, CCLE, GDSC, and related public resources used in cancer research.

3. Can beginners join this program?

Yes. No advanced AI or bioinformatics background is required. The program starts with foundational concepts in genomics, Python, statistics, and ML, and gradually progresses to advanced AI topics.

4. Is a paper publication guaranteed?

Publication cannot be guaranteed, as acceptance depends on peer review.
However, participants receive end-to-end guidance on project design, analysis, writing, and journal or conference selection.

5. How much time commitment is required?

• Training modules follow a structured schedule
• Project work duration depends on the selected track (3 / 6 / 12 months)
  The program is suitable for students, researchers, and working professionals with proper planning.

6. Whom to contact if you face difficulty during the training & project program?

For any academic, technical, or administrative support during the training and project program, you may reach out to us at:

📧 cst@biotecnika.org
📧 info@biotecnika.org

Our support team will assist you at every stage of the program.

Build research depth. Work with real cancer data. Create publishable outcomes.

✔ Hands-on AI & ML training
✔ Independent research project (3 / 6 / 12 months)
✔ Mentor-guided learning
✔ Paper publication support

👉 Enroll Now