Nana Safo Duker

AI/ML Bioinformatics & Precision Medicine - Integrated, Multi-Omics Project Portfolio

A curated, multi-project laboratory showcasing advanced AI, machine learning, and statistical genomics workflows developed by Nana Safo-Duker. Each project folder is a complete, reproducible pipeline addressing a distinct precision medicine challenge ranging from genomic variant pathogenicity prediction to transcription-factor binding modeling, gene expression inference and biomarker discovery.

This portfolio integrates:

• Deep learning architectures (CNNs, RNNs, Transformers) for genomic sequence analysis
• Statistical genetics and biostatistical modeling for population-level insights
• Explainable machine learning to interpret biological mechanisms
• Cloud-ready, scalable bioinformatics pipelines
• Clinical data science methodologies aligned with real-world healthcare applications

Together, these projects demonstrate a comprehensive framework for next-generation precision medicine, combining data-driven insights, mechanistic biological understanding, and clinical relevance.

Data Science & Predictive Analytics - Multi Domain Project Portfolio

This repository brings together ten fully developed, end-to-end analytics projects covering a wide spectrum of real-world domains including marketing analytics, customer intelligence, cybersecurity, NLP, HR insights, fraud detection, energy analysis, machine learning education, compensation science, and unicorn startup analytics.

Each project is built using a consistent, reproducible workflow:

1. Data Cleaning & Feature Engineering
2. Exploratory Data Analysis (EDA)
3. Predictive Modeling (ML, statistical modelling, or NLP)
4. Insight Generation & Communication

The structure makes it easy to open any folder, understand the problem, run the notebook or script and immediately reproduce the full analytical pipeline. This portfolio serves both as a practical learning resource and a demonstration of applied data science across diverse, high-impact domains.

Clinical Data Science & Health Analytics - Translational AI in Public Health Portfolio

This repository is a curated collection of clinical data science and health analytics projects designed to demonstrate real-world applications of machine learning, statistical modeling, and evidence based analysis across healthcare problems. Each project tackles a different aspect of modern clinical informatics from disease prediction and risk stratification to treatment adherence modeling, patient behavior analysis, and population level health insights.

The portfolio includes:

• End-to-end predictive modeling pipelines for chronic disease, metabolic conditions, and clinical outcomes
• Risk scoring systems and stratification frameworks inspired by real-world clinical decision support tools
• Behavioral and lifestyle analytics that surface patient-level patterns influencing disease progression
• Treatment initiation and adherence modeling for evaluating clinical intervention effectiveness
• Data cleaning, feature engineering, and statistical validation to ensure replicable and interpretable insights

Each project is organized for ease of reuse and reproducibility, with clear folder structures, well documented analysis steps, and modular code that aligns with best practices in clinical AI and health informatics.

Differential Gene Expression Dashboard - Interactive Discovery from Gene Expression Data

Unlock critical biological insights faster. The Advanced Differential Gene Expression Dashboard transforms raw RNA-seq or microarray output into actionable knowledge. Designed for computational biologists and researchers, this tool provides a data-agnostic pipeline to immediately visualize, filter, and interpret gene regulation patterns.

Key capabilities:

• Flexible data ingestion with customizable column mapping that adapts to any CSV structure or experimental design
• Interactive visualizations including volcano and scatter plots that spotlight differentially regulated genes
• Advanced filtering, thresholding, and regulation analysis to pinpoint specific biological signatures
• Built-in statistical summaries and one-click exports that keep research transparent and reproducible

The dashboard pairs intuitive UI patterns with rigorous analytics, giving scientists a consistent exploratory experience that mirrors the broader project portfolios.

👁️ Wearable Sensors for Brain Disorder Detection Through Eye Movements: A Comprehensive Research Review

How eye-tracking wearables are transforming neurological diagnostics from Parkinson's to Alzheimer's. This research review examines innovative wearable, high resolution eye movement sensors capable of detecting neurological disorders in real time, potentially reshaping how clinicians screen for conditions like Parkinson's disease, Alzheimer's disease, concussion, and traumatic brain injury.

🦟 Simulating and Fitting Malaria Transmission Model in Madagascar: Impact of Insecticide-Treated Nets

An applied research post on modeling disease transmission and evaluating ITN interventions using R. This study investigates the epidemiological impact of ITNs under varying resistance scenarios using a modified Susceptible, Infected, Recovered (SIR) model with integrated vector dynamics, simulating malaria transmission in Madagascar and comparing outcomes across different intervention scenarios.

🧬 Predicting Gene Expression from DNA Sequence Using Deep Learning Models

A breakthrough in computational biology exploring how advanced neural network architectures can learn the complex regulatory grammar embedded in DNA. This research demonstrates how artificial intelligence can decipher how genes are turned on or off a feat with profound implications for precision medicine, functional genomics, and therapeutic innovation.

📘 From Retina to Risk: Predicting Cardiovascular Health Through Deep Learning

A comprehensive analysis of Nature Biomedical Engineering (2018), exploring how retinal imaging and deep learning can predict systemic cardiovascular health. Highlights AI's role in early disease detection and precision health, demonstrating that deep learning models can predict key cardiovascular risk factors directly from retinal fundus photographs.