Our research focuses on computational analysis of complex natural and social systems. There is a great demand for targeted computational techniques to extract information and insights from rich data collections based on clever combinations of human and machine intelligence. We blend elements from fields such as machine learning/AI, probabilistic programming, statistical ecology, and data science, and drive open developer communities that help to translate latest theoretical advances into accessible methods to inform modeling, experimentation, and decision-making. For a full list of publications check this page.
Early prediction of liver disease using conventional risk factors and gut microbiome-augmented gradient boosting
Liu Y, Meric G, Havulinna A, Teo S, Ruuskanen M, Sanders J, Zhu Q, Tripathi A, Verspoor K, Cheng S, Jain M, Jousilahti P, Vazquez-Baeza Y, Loomba R, Lahti L, Niiranen T, Salomaa V, Knight R & Inouye M.
10.1101/2020.06.24.20138933 | URL
OGUs enable effective, phylogeny-aware analysis of even shallow metagenome community structures
Zhu Q, Huang S, Gonzalez A, McGrath I, McDonald D, Haiminen N, Armstrong G, Vázquez-Baeza Y, Yu J, Kuczynski J, Sepich-Poore G, Swafford A, Das P, Shaffer J, Lejzerowicz F, Belda-Ferre P, Havulinna A, Méric G, Niiranen T, Lahti L, Salomaa V, Kim H, Jain M, Inouye M, Gilbert J & Knight R.
10.1101/2021.04.04.438427 | URL