AI-Driven Genomic Profiling for Personalized Therapy – Comprehensive Study Notes

Page 1

• Document Type & Purpose
  • NTCC (Non–Teaching Credit Course) Report prepared in partial fulfilment of B.Sc. Biotechnology (Hons.) with Research.
  • Institution: Amity Institute of Biotechnology, Amity University Uttar Pradesh.
• Principal Elements
  • Project Title : AI-Driven Genomic Profiling for Personalized Therapy.
  • Student : Aarya Mohan.
  • Guide : Dr. Navaneet Chaturvedi (Assistant Professor, AIB).
  • Batch : ${2024 ext{–}2028}$.

Page 2

• Administrative Metadata
  • Course : B.Sc. Biotechnology (Hons.) with Research.
  • Semester : $2^{ ext{nd}}$.
  • Enrolment No. : A005155124042.
  • Training Duration : ${33}$ days.
  • Signatures required from Internal Faculty Coordinator (IFC) and Student.

Page 3 – Certificate

• Affirms that the work is original and unsubmitted elsewhere.
• Signed by Dr. Navaneet Chaturvedi; includes institute address (Noida-$201301$).

Page 4 – Plagiarism Certificate

• Checked via TURNITIN.
• Overall similarity: {5 ext{ ig(}\%\big)}}.
• Confirms adherence to Amity plagiarism policy; signed by plagiarism in-charge.

Page 5 – Turnitin Report Snapshot

• Overall similarity {5 ext{ hinspace ext{%}}} distributed as:
  • Internet sources 3 ext{ hinspace ext{%}}.
  • Publications 0 ext{ hinspace ext{%}}.
  • Submitted works 4 ext{ hinspace ext{%}}.
• Integrity Flags : $0$ → no suspicious manipulations.

Page 6 – Declaration

• Student Aarya Mohan confirms independent completion under Dr. Navaneet Chaturvedi.
• Submitted for the academic cycle ${2024 ext{–}2028}$.
• Signed with date, place, enrolment no.

Page 7 – Acknowledgment

• Gratitude expressed to:
  • Dr. V. Pooja (Head, AIB).
  • Dr. Navaneet Chaturvedi for guidance & critical feedback.
  • Peers for ideas and motivation.

Page 8 – Table of Contents

• Chapter 1 – Introduction (pp. $11 ext{–}14$) covers: AI & genomics, DeepVariant, SpliceAI, AlphaFold, PRS, phenotype prediction, therapy selection, multi-omics integration, and challenges.
• Chapter 2 – Biological Evaluation (pp. $14 ext{–}17$) explains GANs/VAEs foundations, synthetic data, augmentation for rare disease, privacy-preserving analysis, multimodal integration, drug development, limitations & ethics.
• Chapter 3 – Methodology (pp. $17 ext{–}26$): 3D bioprinting, CAR-T simulations, patient stratification, biomarker pipeline, deep generative evaluation, NLP & SuppKG, multimodal/federated learning, tools, XAI, ethics.
• Chapter 4 – Conclusion (p. $27$).
• References (p. $28$).
• Figures 1–10 & Tables 1–2 enumerated with page references.

Page 9 – Project Information

• Internship: $02/06/2025$ → $04/07/2025$ (${33}$ days).
• Project Objective : Study how AI analyses individual genetic profiles to tailor treatments and improve therapeutic precision.
• Signatures required from student, industry guide, and faculty.

Page 10 – Abstract (Key Points)

• AI Modalities : Machine Learning (ML), Deep Learning (DL), Natural Language Processing (NLP).
• Data Types Analysed : Whole Genome/Exome, RNA-seq, multi-omics.
• Highlighted AI Tools : DeepVariant, AlphaFold, SuppKG.
• Generative Models : VAEs & GANs for data augmentation & synthetic datasets.
• Integrated Innovations : 3D bioprinting for tumor microenvironment, mRNA-engineered T cell therapies.
• Challenges : Data heterogeneity, interpretability, bias, under-representation.
• Ethical Emphasis : Explainable AI (XAI), regulatory oversight; goal → predictive, preventive, precise healthcare.

Page 11 – 1.0 Introduction (Condensed)

• Traditional vs. Personalized Medicine : Shift from population-level protocols to patient-specific interventions.
• AI Accelerators : ML & DL interpret complex biomedical data (genomics, EHRs, multi-omics).
• Rare Disease Impact : Example – Kothinti $[1]$ shows AI reduces diagnostic odyssey for heterogeneous phenotypes.
• Generative AI for Decision Support : Ghebrehiwet et al. $[2]$ demonstrate proactive, evolving therapeutic strategies.
• Key Barriers : Transparency, privacy, bias, evolving regulation.
• Bottom-Line : AI is becoming a cornerstone of future medicine.

Page 12 – 1.1 to 1.4 Highlights

• DeepVariant : Converts aligned reads into image-like tensors; CNN classifies variant/non-variant across Illumina, PacBio, ONT platforms – outperforms heuristic pipelines.
• SpliceAI : Predicts impact of substitutions on canonical & cryptic splice sites; leverages >$10^{5}$ introns for training.
• AlphaFold2 : Near-experimental $3 ext{D}$ protein structures, closing genotype–phenotype gap; aids allosteric drug design.

Page 13 – 1.5 to 1.8 Highlights

• Polygenic Risk Scores (PRS) : AI (Random Forest, Gradient Boosting, SVM) integrates thousands of GWAS variants + lifestyle + epigenomics for individualized risk.
• Phenotype Prediction : Models simulate disease trajectories (e.g., phenylketonuria).
• Therapy Selection : IBM Watson, Deep Genomics analyse CYP450 variants; oncology AI predicts response to checkpoint inhibitors.
• Multi-Omics Integration : AI fuses methylation, proteomics, metabolomics → network graphs/heat-maps for clinician insight.

Page 14 – 1.9 Challenges & 2.0 Prelude

• Key Obstacles :
  • Data bias (over-representation of European ancestry).
  • Interpretability (“black-box” DL).
  • Privacy (necessitating federated learning).
• Biological Evaluation Intro : Emergence of GANs/VAEs for synthetic, privacy-preserving biomedical simulation.

Page 15 – 2.1 & 2.2 Foundations

• GAN Mechanics : Generator vs. Discriminator → zero-sum learning reproduces data distribution.
• VAE Mechanics : Probabilistic latent space $\rightarrow$ sampling $\rightarrow$ decoder; ensures diversity.
• Synthetic Omics : GANs create single-cell RNA-seq profiles to enable in silico drug screening.
• Validation Steps : Clustering fidelity, pathway enrichment, downstream task performance.

Page 16 – 2.3 & 2.4

• Rare Disease Augmentation : Conditional GANs expand scarce tumour imaging sets; VAEs simulate biochemical assays for genotype–phenotype studies.
• Differentially-Private GANs (DP-GANs) : Inject calibrated noise; enable multi-institutional sharing without re-identification risk.

Page 17 – 2.5 to 2.8

• Multimodal Generative Models : Cross-domain GANs align gene expression $\leftrightarrow$ histology → digital twins.
• Drug Development : Molecule-generating GANs design compounds with target-specific binding; transcriptomic GANs classify tumour subtypes.
• Current Limitations : Mode collapse, interpretability, biological constraint enforcement.
• Future Outlook : Causal generative models & robust ethical guidelines for synthetic data.

Page 18 – 3.1 Cancer & Immunotherapy Modelling

• 3D Bioprinting : Bio-inks with cells + ECM printed layer-wise to replicate heterogeneity & vasculature (following Datta $[4]$).
• CAR-T Simulations : Transformer networks predict T-cell kinetics; reinforcement learning optimizes cytokine release (IL-12 + IL-18 synergy from Olivera $[8]$).
• Figures 1 & 2 illustrate printing pipeline & CAR-T workflow.

Page 19 – 3.2 Patient Stratification & Biomarkers

• ML Models Used : Random Forests, Gradient Boosting, SVM trained on SNVs, imaging, clinical data.
• Outcome : Assign patients to optimal therapy cohorts; lower ADRs.
• Biomarker Pipeline : ANOVA/Chi-square $\rightarrow$ t-SNE/UMAP $\rightarrow$ GSVA; validated by Kothinti $[1]$ & Vadapalli $[9]$.
• Figure 3: Workflow.
• Figure 4: Heat-map of top $10$ biomarkers; VEGFA & PD-1 expression across subtypes.

Page 20 – 3.3 Synthetic Evaluation

• GAN/Autoencoder Architecture (Fig. 5): Generates anomaly-free biological datasets.
• Evaluation Metrics (Table 1) : Accuracy parity, F1-score, KL-divergence, biological plausibility rating.

Page 21 – 3.4 NLP & SuppKG

• BioBERT / SciSpacy Pipeline : NER for genes, drugs, supplements; dependency parsing extracts interaction verbs.
• SuppKG Knowledge Graph : Nodes = entities; edges = biological relations; graph analytics (PageRank) reveal high-impact interactions; aids polypharmacy risk alerts (Fig. 6).

Page 22 – 3.5 Multimodal & Federated AI

• Multimodal Model (Fig. 7) : CNN + RNN + Transformer fusion; early vs. late fusion strategies; improves holistic predictions (stroke, cancer, etc.).
• Federated Learning (Fig. 8) : Local model training $\rightarrow$ secure weight aggregation (FedAvg); complies with GDPR/HIPAA while scaling cohort size.

Page 23 – 3.6 Tools Stack (Table 2)

• Python (Pandas/NumPy) : ETL & preprocessing.
• TensorFlow/PyTorch : Model development.
• SpaCy/BioBERT : NLP mining.
• Neo4j/RDFLib/SuppKG : Knowledge graph.
• Matplotlib/Seaborn : Visualization.

Page 24 – 3.7 Validation & XAI

• Biological Cross-Checks : Predictions mapped to TCGA, DrugBank, GeneCards; wet-lab comparisons where available.
• SHAP/LIME : Feature-level & instance-level explanation; Fig. 9 depicts SHAP summary where NIHSS on admission ranks highest.

Page 25 – 3.8 Ethics & Bias (Fig. 10)

• Bias Audits : Re-weighting, stratified sampling, fairness-aware loss.
• Privacy Measures : Encryption, synthetic replacement, federated architecture; compliance with global regulations.
• Ethical Framework : Transparency, accountability, beneficence; aligned with Abujaber & Nashwan $[2024]$.

Page 26 – 4.0 Conclusion (Synopsis)

• AI is actively transforming precision medicine by bridging genomic discovery and clinical action.
• Generative & predictive models accelerate rare disease diagnosis, cancer immunotherapy, and multi-omics interpretation.
• Tools such as Methylartist & SuppKG expand epigenetic/drug-interaction insight.
• Ethical stewardship (privacy, bias, explainability) remains essential for equitable deployment.
• Future → intelligent, compassionate, inclusive healthcare systems.

Page 27 – 5.0 References (Key Citations)

• $[1]$ Kothinti R.R., $2025$ – AI tailoring treatment for rare disorders.
• $[2]$ Ghebrehiwet I. et al., $2024$ – Systematic review on generative AI for personalized medicine.
• $[3]$ Chen Y-T. et al., $2021$ – lncRNA & obesity resistance.
• $[4]$ Datta P. et al., $2020$ – 3D bioprinting cancer microenvironment.
• $[5]$ Abhishek A.B., $2025$ – AI-powered genomic solutions.
• $[6]$ Amirineni S., $2024$ – AI in personalized medicine (comprehensive review).
• $[7]$ Schutte D. et al., $2022$ – SuppKG for drug-supplement interactions.
• $[8]$ Olivera I. et al., $2023$ – IL-12 & IL-18 mRNA synergy in T-cells.
• $[9]$ Vadapalli S. et al., $2022$ – AI/ML with gene expression & variant data.
• $[10]$ Cheetham S.W. et al., $2022$ – Methylartist visualisation tools.