Nana Safo Duker

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

A curated multi-project lab of reproducible AI/ML and statistical genomics workflows. Each project is a complete pipeline focused on a specific precision medicine problem—from variant pathogenicity prediction and transcription-factor binding modeling to 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 an end-to-end approach to next-generation precision medicine—combining data-driven insight, biological interpretability, and clinical relevance.

Data Science & Predictive Analytics - Multi-Domain Project Portfolio

A collection of end-to-end analytics projects spanning real-world domains including marketing analytics, customer intelligence, cybersecurity, NLP, HR insights, fraud detection, energy analysis, ML education, compensation science, and 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 modeling, or NLP)
4. Insight Generation & Communication

The structure makes it easy to open any folder, understand the problem, run the notebook/script, and reproduce the full workflow. It’s both 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

A curated set of clinical data science and health analytics projects demonstrating real-world applications of machine learning, statistical modeling, and evidence-based analysis across healthcare. Projects span disease prediction and risk stratification, 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 reuse and reproducibility, with clear structure, well-documented analysis steps, and modular code aligned with best practices in clinical AI and health informatics.

Differential Gene Expression Dashboard - Interactive Discovery from Gene Expression Data

The Advanced Differential Gene Expression Dashboard turns raw RNA-seq or microarray output into actionable insight. Designed for computational biologists and researchers, it provides a data-agnostic workflow to visualize, filter, and interpret gene regulation patterns quickly.

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 portfolio.

👁️ 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.