N. Haiminen

AutoXAI4Omics: an Automated Explainable AI tool for Omics and tabular data

AutoXAI4Omics automates and explains ML pipelines for omics and tabular data, enabling interpretable insights for biomarker discovery and phenotype prediction.

Development and evaluation of statistical and Artificial Intelligence approaches with microbial shotgun metagenomics data as an untargeted screening tool for use in food production

This study evaluates AI and statistical methods on microbial shotgun metagenomics data to detect anomalies in fluid milk, showing explainable AI can classify anomalous samples and identify microbial drivers.

Bacterial metatranscriptomes in wastewater can differentiate virally infected human populations

The paper shows that bacterial metatranscriptomes in wastewater can distinguish SARS-CoV-2 infected from uninfected human populations without directly detecting viral transcripts.

Multi-omics profiling of Earth's biomes reveals patterns of diversity and co-occurrence in microbial and metabolite composition across environments

The paper performs a multi-omics meta-analysis of microbial and metabolite data from diverse environments, revealing patterns of diversity and co-occurrence across biomes.

Bacterial modification of the host glycosaminoglycan heparan sulfate modulates SARS-CoV-2 infectivity

This study shows that commensal bacteria modify heparan sulfate, reducing SARS-CoV-2 spike protein binding and that this bacterial presence decreases with age, increasing COVID-19 susceptibility.

Monitoring the microbiome for food safety and quality using deep shotgun sequencing

The paper uses deep shotgun sequencing to analyze food microbiomes, identifying microbial genera and observing shifts related to ingredient composition, but finds limited correlation between RNA sequencing and Salmonella growth.