Software

E2P Simulator (Effect-to-Prediction Simulator) is an open-source web tool for evaluating real-world utility of biomarkers and prediction models (Karvelis et al., 2025). I built it to address critical gaps in research practices and to place measurement reliability and outcome base rates at the center of the interpretation of findings and research planning.

E2P Simulator allows researchers to explore the relationships among effect size (Cohen’s d, Pearson’s r, Odds Ratios), discriminative ability (ROC-AUC, sensitivity, specificity), real-world predictive value (PPV, NPV, PR-AUC), and clinical utility measures (Net Benefit), while visually conveying how all of them relate to the same underlying data distribution. It also includes multivariable simulators to estimate performance when combining multiple predictors, sample size calculators for training multivariable models, and a module for exploring how changes in measurement reliability and outcome base rates affect model calibration.

Similar to how power analysis tools (such as G*Power) help researchers plan for statistical significance, E2P Simulator helps plan for practical significance. This enables more informed research decisions and efficient resource allocation in precision medicine, psychiatry, and other fields focused on predictive modeling and personalization.

For its application in precision psychiatry see Karvelis et al., 2026 (preprint).

www.e2p-simulator.com

Biomedical evidence is spread across many disconnected databases, slowing down research and hiding important insights. To address this,I have been building an ai co-scientist: it can answer complex biomedical questions by querying and synthesizing existing evidence across many databases, such as Open Targets, ChEMBL, ClinicalTrials.gov, PubMed, UniProt, and so on.

Give it a try: https://ai-co-scientist-1041150291753.us-central1.run.app

I’m interested in knowledge graphs as a potentially important future infrastructure for agentic science. I’m currently exploring, developing and testing a robust, provenance-aware pipeline for constructing knowledge graphs from open-access literature across compounds, targets, and indications.

Here’s one of them: Psychedelics Knowledge Graph

A fun weekend project to explore what newer coding tools can do in rapid game prototyping. This racing game is actually quite serious - I designed it for studying individual differences in associative learning and probabilistic inference (cars with different colors move in different pattersn and different probabilities).

Hyperlane Blitz