Biology subjects

Heinlein, B.

Publications and source records attributed to Heinlein, B..

2 recordsLinked to original sources

bioRxiv2026

The chemistry of families: Linking body odor composition to olfactory perception

Body odors inform human kin recognition. However, underlying chemical mechanisms and how they relate to body odor perception are still unclear. The present study therefore simultaneously examined perceptual body odor similarity according to human raters, its chemical basis and the link between the two in human families. The study included 33 raters who assessed body odors collected from 60 odor donors belonging to 15 biological families. Each family comprised a mother, a father and one adolescent same-sex sibling-dyad (8 sister dyads, 7 brother dyads). Body odor sampling was conducted with cotton pads for presentation to human raters and with thermal desorption (TD) tubes for Gas Chromatography-Mass Spectrometry (GC-MS) analysis. Relatedness and sex predicted perceived body odor similarity in parent-offspring versus unrelated control dyads. Analyses of chemical profiles revealed greater body odor similarity within families than between unrelated individuals. Parallel to the perceptual results, relatedness and sex predicted chemical similarity based on whole odor profiles. Finally, a machine-learning prediction model integrating perceptual and chemical data, revealed a small but significant positive association between chemical similarity of body odor and perceived olfactory similarity according to human raters, suggesting that odor perception is partly informed by chemical characteristics. Overall, the present study extends our understanding of human chemosensory communication and supports the notion of a family-specific olfactory signature.

ecology↗

bioRxiv2021

Combining dictionary- and rule-based approximate entity linking with tuned BioBERT

Chemical named entity recognition (NER) is a significant step for many downstream applications like entity linking for the chemical text-mining pipeline. However, the identification of chemical entities in a biomedical text is a challenging task due to the diverse morphology of chemical entities and the different types of chemical nomenclature. In this work, we describe our approach that was submitted for BioCreative version 7 challenge Track 2, focusing on the Chemical Identification task for identifying chemical entities and entity linking, using MeSH. For this purpose, we have applied a two-stage approach as follows (a) usage of fine-tuned BioBERT for identification of chemical entities (b) semantic approximate search in MeSH and PubChem databases for entity linking. There was some friction between the two approaches, as our rule-based approach did not harmonise optimally with partially recognized words forwarded by the BERT component. For our future work, we aim to resolve the issue of the artefacts arising from BERT tokenizers and develop joint learning of chemical named entity recognition and entity linking using pre-trained transformer-based models and compare their performance with our preliminary approach. Next, we will improve the efficiency of our approximate search in reference databases during entity linking. This task is non-trivial as it entails determining similarity scores of large sets of trees with respect to a query tree. Ideally, this will enable flexible parametrization and rule selection for the entity linking search.

bioinformatics↗