Oncogenomics Research - Scientific background

Oncogenomics Research

AI-Driven Cancer Genomics • Precision Medicine • Cloud Computing

Research Focus

My research interests lie at the convergence of artificial intelligence and oncogenomics, leveraging cloud-scale computing for AI resources and . This interdisciplinary approach combines my technical expertise in cloud architecture with emerging opportunities in precision oncology.

Current Research Areas

AI

AI-Driven Cancer Genomics Analysis

Developing machine learning pipelines for the analysis of large-scale cancer genomic datasets, utilizing cloud computing infrastructure to process multi-omics data and identify novel therapeutic targets.

Cloud

Cloud-Based Genomic Data Processing

Architecting scalable cloud solutions for genomic data storage, processing, and analysis, enabling researchers to handle the computational demands of modern cancer genomics research.

PM

Precision Medicine Applications

Exploring the application of artificial intelligence in personalizing cancer treatment strategies based on individual genomic profiles and tumor characteristics.

Research Projects

Current and planned research initiatives at the intersection of AI and genomics

ARTEMIS: Advanced Real-Time Expert Medical Intelligent System

Active

Developing of a cutting-edge AI-powered cancer treatment recommendation system using Amazon Web Services (AWS) and Nova multimodal AI capabilities. This comprehensive solution processes diverse patient data types including medical imaging, genomic sequences, and clinical records to deliver personalized treatment recommendations.

Key Achievements

  • -Implemented advanced RAG (Retrieval-Augmented Generation) system integrating current medical protocols and research
  • -Developed automated clinical workflow agents using Amazon Bedrock
  • -Created robust testing framework ensuring 85%+ recommendation accuracy
  • -Engineered HIPAA-compliant architecture with end-to-end security
  • -Integrated continuous prompt optimization for enhanced accuracy

Technologies

AWS BedrockAmazon NovaLambdaStep FunctionsOpenSearchCloudwatchS3

Cloud Infrastructure for Oncogenomics Research

Active

Designing and implementing scalable cloud architectures specifically optimized for cancer genomics workflows, enabling efficient processing of large-scale sequencing data and facilitating collaborative research.

Technologies

AWSKubernetesTerraformGATKNextflow

Oncogenomics Learning Hub

Explore comprehensive courses in cancer genomics, molecular mechanisms, and AI applications in precision medicine.

Oncogenomics Learning Hub

Comprehensive Cancer Genomics Education

Access our full collection of courses covering advanced molecular mechanisms, AI applications in precision medicine, and fundamental cancer genomics concepts. Each course features interactive presentations, audio narrations, and laboratory exercises.

Explore All Courses

Laboratory

Practical applications and laboratory techniques in cancer genomics research.

Cancer Cell Line Characterization

Oncogene and Tumor Suppressor Analysis

Research Methodology

My approach combines computational genomics with cloud-native architectures, enabling scalable analysis of large genomic datasets. I leverage machine learning to identify patterns in cancer genomics data that traditional methods might miss.

The integration of AWS cloud services allows for processing of multi-terabyte genomic datasets, while advanced AI models help identify potential therapeutic targets and biomarkers for precision medicine applications.

Key Technologies

AI/ML Frameworks

  • - TensorFlow & PyTorch
  • - Scikit-learn
  • - Transformers
  • - BioPython

Cloud & Infrastructure

  • - AWS SageMaker
  • - AWS Batch
  • - Kubernetes
  • - Docker

Genomics Tools

  • - GATK
  • - Nextflow
  • - Bioconductor
  • - IGV

Data Analysis

  • - R & Python
  • - Jupyter Notebooks
  • - Apache Spark
  • - SQL/NoSQL

Collaborations & Future Work

Open to Collaboration

I'm actively seeking collaborations with cancer research institutions, biotechnology companies, and academic researchers working at the intersection of genomics, artificial intelligence, and cloud computing.

Areas of Interest:

  • - Multi-omics data integration
  • - Drug discovery pipelines
  • - Biomarker identification
  • - Clinical trial optimization

Oncogenomics Learning Hub

Research publications and conference presentations will be listed here as they become available. Currently preparing manuscripts on AI applications in cancer genomics and cloud architectures for precision medicine.

Advancing Precision Medicine Through AI

Interested in collaborating on cutting-edge research that combines artificial intelligence with cancer genomics? Let's explore how we can accelerate the development of personalized treatments and improve patient outcomes together.

Contact for Collaboration