GeneHusArticles & Blogs
🩺 Predicting Cancer Outcomes with Radiomics and AI in Radiology
Quantitative imaging biomarkers and machine learning for precision oncology: how radiomics and AI extract prognostic signals from medical images to help tailor treatment, identify high-risk patients, and support personalized cancer care.
Read Article →🛰️ From Satellites to Predictions: Machine Learning for Malaria Outbreak Forecasting
A spatio-temporal AI framework for translating climate data into actionable disease surveillance—linking rainfall, temperature, humidity, and vegetation to mosquito-driven malaria transmission and outbreak timing.
Read Article →⚠️ Every AI Tool Is Also a Potential Weapon
How the first major study on malicious AI use foresaw today’s dual-use reality—and why generative models turned a known risk into a mass-access problem that benefits alone cannot explain away.
Read Article →📊 Making Sense of Health Signals: A Machine Learning Approach to Digital Epidemiology
Statistical learning for noisy time-series analysis, hypothesis testing, and early disease signal detection in digital epidemiology. Explores how machine learning can extract actionable surveillance signals from imperfect health data—supporting proactive public health response before outbreaks fully materialize.
Read Article →🧬 Genomic Data to Clinical Insight: Deep Learning Models for Cancer Type Prediction
A computational pipeline linking high-dimensional gene expression to diagnostic classification: normalization, variance-based feature selection, neural network modeling, and statistically grounded evaluation. Demonstrates how deep learning transforms raw genomic measurements into clinically relevant cancer type predictions—with links to reproducible portfolio work in computational oncology.
Read Article →⚖️ The Politics of Climate Change: Why Justice Can't Be Optional
A just transition is not only about cutting emissions—it requires fairness, dignity, and equity for people and planet. Synthesizes climate, energy, and environmental justice within SDG and Paris Agreement contexts, arguing that equity must be embedded in policy design rather than appended after technical decarbonization plans.
Read Article →🌍 Why Smarter Algorithms Can Still Fail the Climate
A governance-focused essay on climate machine learning: why benchmark accuracy and innovation narratives are not enough when models sit inside electricity markets, supply chains, and institutions with misaligned incentives. Draws on high-impact ML-for-climate surveys and argues for auditable standards, independent evaluation, and outcome-based accountability—not just better predictions.
Read Article →🛰️ Graph Neural Networks for Weather Forecasting: A Critical Analysis of GraphCast
A structured review of graph neural networks for medium-range global weather prediction, centered on the GraphCast paradigm: graph formulation, training on reanalysis data, skill versus NWP baselines, and open questions around uncertainty, extremes, and robustness under climate shift—with links to reproducible portfolio work on AI-based global forecasting.
Read Article →🔮 A Bayesian Machine Learning Framework for Modeling Infectious Disease Outbreaks
Describes a Bayesian ML pipeline for epidemic time series: feature construction, probabilistic count models, posterior inference, and posterior predictive forecasts that expose uncertainty—contrasted with deterministic single-point predictions—plus discussion of strengths, limits, and links to an open implementation for outbreak prediction.
Read Article →🏛️ AI Is Moving Faster Than Governments Can Respond
An AI governance essay distinguishing technical safety from institutional governance: anticipatory regulation, capacity gaps, voluntary “responsible AI” versus enforceable accountability, and concrete mechanisms—risk-tiered rules, audit infrastructure, and international coordination—grounded in leading policy and FAccT-era literature.
Read Article →♻️ Can AI Fight Climate Change Without Worsening It?
Explores the tension between AI as climate infrastructure (grids, disasters, land use) and the hidden environmental cost of compute, hardware, and rebound effects; argues for lifecycle disclosure, impact assessment, equity in deployment, and governance that rewards verified emissions outcomes rather than green narratives alone.
Read Article →👁️ 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.
Read Article →🦟 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.
Read Article →🧬 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.
Read Article →📘 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.
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