Marcin P. Joachimiak

This study shows that filtering genomes based on coherent genotype-phenotype relationships improves cross-dataset prediction of microbial carbon utilisation, outperforming larger but less coherent datasets.

MicroGrowAgents is an agentic AI system that designs microbial growth media using structured knowledge and experimental design, identifying optimal conditions for Methylorubrum extorquens AM1.

KG-Microbe constructs modular knowledge graphs to integrate microbial data, enabling analysis of complex interactions in health and environment.

The paper defines AI-readiness criteria for biomedical data, encompassing seven dimensions, and evaluates them across flagship datasets to ensure ethical and explainable AI practices.

MAK improves biclustering by correcting size bias and enhancing biological enrichment, finding novel biclusters in yeast data.

The paper uses word embeddings from PubMed to predict which protein kinases are relevant for treating specific cancers using machine learning.

KG-COVID-19 creates customizable knowledge graphs for COVID-19 response by integrating siloed biomedical data into structured formats for various research applications.

The paper semantically integrates LOINC-encoded laboratory data with the Human Phenotype Ontology to identify asthma biomarkers from EHR data.

Open-source KBase platform unites reference genomes and biochemistry, over 70 apps, Jupyter-based Narratives with provenance, and a Docker SDK for reproducible plant and microbial systems biology.