Biology subjects

Menger, J.

Publications and source records attributed to Menger, J..

4 recordsLinked to original sources

bioRxiv2025

mmContext: an open framework for multimodal contrastive learning of omics and text data

SummaryMultimodal approaches are increasingly leveraged for integrating omics data with textual biological knowledge. Yet there is still no accessible, standardized framework that enables systematic comparison of omics representations with different text encoders within a unified workflow. We present mmContext, a lightweight and extensible multimodal embedding framework built on top of the open-source Sentence Transformers library. The software allows researchers to train or apply models that jointly embed omics and text data using any numeric representation stored in an AnnData .obsm layer and any text encoder available in Hugging Face. mmContext supports integration of diverse biological text sources and provides pipelines for training, evaluation, and data preparation. We train and evaluate models for a RNA-Seq and text integration task, and demonstrate their utility through zero-shot classification of cell types and diseases across four independent datasets. By releasing all models, datasets, and tutorials openly, mmContext enables reproducible and accessible multimodal learning for omics-text integration. Availability and implementationPretrained checkpoints and full source code for our custom MMContextEncoder are available on Hugging Face huggingface.co/jo-mengr. The Python package github.com/mengerj/mmcontext provides the model implementation and training and evaluation scripts for custom training.

bioinformatics↗

bioRxiv2025

Adding layers of information to scRNA-seq data using pre-trained language models

Pre-trained language models promise to enrich single-cell analyses with contextual information from large biomedical text corpora, but it remains unclear how to optimally align this knowledge with quantitative scRNA-seq data. To address this, we construct text-based training datasets from both scRNA-seq data and biomedical literature targeted to the experimental setting at hand. We then fine-tune lightweight encoder-only biomedical language models to learn a shared, literature-enriched representation. Controlled evaluations across immune and developmental datasets show that this representation preserves cell identity while adding robust and interpretable contextual layers of functional, disease-associated, and developmental information to single-cell analysis workflows.

bioinformatics↗

bioRxiv2020

Landscape configuration of an Amazonian island-like ecosystem drives population structure and genetic diversity of a habitat-specialist bird

ContextAmazonian white-sand ecosystems (campinas) are open vegetation patches which form a natural island-like system in a matrix of tropical rainforest. Due to their clear distinction from the surrounding matrix, the spatial characteristics of campina patches may affect the genetic diversity and composition of their specialized organisms such as the small and endemic passerine Elaenia ruficeps. ObjectivesHere, we estimate the relative contribution of the current extension, configuration and geographical context of campina patches to the patterns of genetic diversity and population structure of E. ruficeps. MethodsWe sampled individuals of E. ruficeps from three landscapes in Central Amazonia with contrasting campina spatial distribution, from landscapes with large and connected patches to landscapes with small and isolated patches. We estimate population structure, genetic diversity, and contemporary and historical migration within and among the three landscapes and used landscape metrics as predictor variables. Furthermore, we estimate genetic isolation by distance and resistance among individuals sampled within landscapes. ResultsWe identified three genetically distinct populations with asymmetrical gene flow among landscapes and a decreasing migration rate with distance. Within each landscape, we found low genetic differentiation without genetic isolation by distance nor by resistance. In contrast, we found differentiation and spatial correlation between landscapes. ConclusionsOur results uncover population dynamics of E. ruficeps through time. Together with previous studies, this suggests that both regional context and landscape structure shape the connectivity among populations of campina specialist birds, and that Amazonian landscapes, together with their associated biota, have responded to recent climatic changes.

ecology↗

bioRxiv2020

Contrasting impacts of urban and farmland cover on flying insect biomass

Recent studies report declines in biomass, abundance and diversity of terrestrial insect groups. While anthropogenic land use is one likely contributor to this decline, studies assessing land cover as a driver of insect dynamics are rare and mostly restricted in spatial scale and types of land cover. In this study, we used rooftop-mounted car nets in a citizen science project ( InsectMobile) to allow for large-scale geographic sampling of flying insects across Denmark and parts of Germany. Citizen scientists sampled insects along 278 10 km routes in urban, farmland and semi-natural (grassland, wetland and forest) landscapes in the summer of 2018. We assessed the importance of local to landscape-scale effects and land use intensity by relating insect biomass to land cover in buffers of 50, 250, 500 and 1000 m along the routes. We found a negative association of urban cover and a positive association of farmland on insect biomass at a landscape-scale (1000 m buffer) in both countries. In Denmark, we also found positive effects of all semi-natural land covers, i.e. grassland (largest at the landscape-scale, 1000 m), forests (largest at intermediate scales, 250 m), and wetlands (largest at the local-scale, 50 m). The negative association of insect biomass with urban land cover and positive association with farmland were not clearly modified by any variable associated with land use intensity. Our results show that land cover has an impact on flying insect biomass with the magnitude of this effect varying across spatial scales. Since we consistently found negative effects of urban land cover, our findings highlight the need for the conservation of semi-natural areas, such as wetlands, grasslands and forests, in Europe.

ecology↗