Despite the importance of protein-ligand affinity ranking in drug discovery, existing deep learning models struggle to distinguish hard inactives that are structurally similar to active compounds but biologically inactive. We theoretically show this ... read more
Real-world object size is a fundamental dimension of visual cognition, supporting effective interaction with the environment and object manipulation. However, neural mechanisms encoding size have largely been inferred from extreme size comparisons, l... read more
Identifying robust neuroimaging markers associated with schizophrenia is essential for advancing research and informing clinical understanding. However, a major obstacle to clinical translation is the limited ability of neuroimaging-based classificat... read more
Quantification of root anatomical traits such as cortical aerenchyma is key to understanding rice adaptation to diverse water regimes. Recently, the role of aerenchyma in regulating methane emissions has been demonstrated, making it a target for clim... read more
Microplastics are increasingly recognized as emerging contaminants in terrestrial ecosystems, yet their mechanistic impacts on soil multifunctionality remain poorly understood. Here, we evaluated the influence of two microplastic types, polyethylene ... read more
Urination, a vital and conserved process of emptying urine from the urinary bladder in mammals, requires precise coordination between the bladder and external urethral sphincter (EUS) that is tightly controlled by a complex neural network. However, t... read more
Automated spatial segmentation models can enrich spatio-molecular omics analyses by providing a link to relevant biological structures. We developed segmentation models that use label-free autofluorescence (AF) microscopy to recognize multicellular f... read more
Systematic assessment of organelle architectures, termed the organellome, offers valuable insights into cellular states and pathomechanisms, but remains largely uncharted. Here, we present a deep phenotypic learning based on vision transformers, resu... read more
MLMarker is a machine learning tool that computes continuous tissue similarity scores for proteomics data, addressing the challenge of interpreting complex or sparse datasets. Trained on 34 healthy tissues, its Random Forest model generates probabili... read more
The (semi-)automated screening of publications for diverse quality and transparency criteria is at the core of systematic literature assessment. Typically, the assessment process involves two initial reviewers and one additional reviewer for cases th... read more
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