Biology subjects

Breininger, K.

Publications and source records attributed to Breininger, K..

4 recordsLinked to original sources

bioRxiv2026

Probabilistic mouse-human brain correspondence by multimodal optimal transport

The mouse is the principal model for brain mechanism and disease, allowing experiments that cannot be performed in humans. However, findings often translate poorly because homologous regions differ in relative size and some human territories have no clear mouse counterpart. Here we present OTTER, which learns mouse-human brain correspondence as a probabilistic, parcel-resolution coupling using multimodal fused Gromov-Wasserstein optimal transport, integrating functional and structural connectivity with spatial position and curated homologies. OTTER recovers established homologues on a transcriptomic benchmark and preserves broad cross-species organisation along the cortical areal hierarchy. Applying the coupling to human functional connectivity reveals a graded decline in mouse-based reconstruction across evolutionarily expanded association cortex, with the lowest values in lateral prefrontal territory. Finally, the bidirectional map generates testable human predictions from mouse experiments and ranks mouse circuits corresponding to human clinical targets.

neuroscience↗

bioRxiv2024

High-accuracy prediction of 5-year progression-free melanoma survival using spatial proteomics and few-shot learning on primary tissue

Emerging techniques in imaging-based spatial proteomics (ISP) enable in-depth insights into the architecture and protein abundance of tissue(s). Explainable machine learning (xML) models promise to yield substantial advances in ISP data-based diagnosis and prognosis. However, a clinical application of these new possibilities predicting the course of a tumor has not been suggested yet. Here, we use a few-shot learning workflow on histological multi-antigen images to predict 5-year progression-free survival (PFS) in melanoma. We address the problem of a relatively small cohort (n = 22), by utilizing a pre-trained convolutional neural network (CNN) model, which we further pre-train on a proxy task for which more samples were available (n = 39) before fine-tuning for PFS prediction. Our approach yielded a model achieving an accuracy of more than 90%, outperforming baseline models trained on clinical data by around 10%. Using an xML technique, we identified immune infiltration and proteins associated with tumor progression as crucial predictors. This indicated that our models PFS predictions are not only highly accurate but also grounded in a relevant biological background.

bioinformatics↗

bioRxiv2022

Cytologic Scoring of Equine Exercise-Induced Pulmonary Hemorrhage (EIPH): Performance of Human Experts and a Deep Learning-Based Algorithm

Exercise-induced pulmonary hemorrhage (EIPH) is a relevant respiratory disease in sport horses which can be diagnosed by examination of bronchoalveolar lavage fluid (BALF) cells using the total hemosiderin score (THS). The aim of this study was to evaluate the diagnostic accuracy and reproducibility of trained annotators and to validate a deep learning-based algorithm for the THS. Digitized, iron-stained cytological specimens were prepared from 52 equine BALF samples. Ten annotators produced a THS for each slide according to published methods. The reference methods for comparing annotators and algorithmic performance included a ground truth dataset, the mean annotators THSs, and chemical iron measurements. Results of the study showed that annotators had marked inter-observer variability of the THS, which was mostly due to a systematic error between annotators in grading the intracytoplasmatic hemosiderin content of individual macrophages. Regarding overall measurement error between the annotators, 87.7% of the variance of the could be reduced by using standardized grades based on the ground truth. The algorithm was highly consistent with the ground truth in assigning hemosiderin grades. Compared to the ground truth THS, annotators had an accuracy of diagnosing EIPH (THS of < or [≥] 75) of 75.7% whereas the algorithm had an accuracy of 92.3% with no marked difference in correlation to chemical iron measurements. The results show that deep learning-based algorithms are useful for improving reproducibility and routine applicability of the THS. For THS by experts, a diagnostic uncertainty interval of 40 to 110 is proposed. THSs within this interval have insufficient reproducibility regarding the EIPH diagnosis.

pathology↗

bioRxiv2021

Computer-Assisted Mitotic Count Using a Deep Learning-based Algorithm Improves Inter-Observer Reproducibility and Accuracy in canine cutaneous mast cell tumors

The mitotic count (MC) is an important histological parameter for prognostication of malignant neoplasms. However, it has inter- and intra-observer discrepancies due to difficulties in selecting the region of interest (MC-ROI) and in identifying/classifying mitotic figures (MFs). Recent progress in the field of artificial intelligence has allowed the development of high-performance algorithms that may improve standardization of the MC. As algorithmic predictions are not flawless, the computer-assisted review by pathologists may ensure reliability. In the present study we have compared partial (MC-ROI preselection) and full (additional visualization of MF candidate proposal and display of algorithmic confidence values) computer-assisted MC analysis to the routine (unaided) MC analysis by 23 pathologists for whole slide images of 50 canine cutaneous mast cell tumors (ccMCTs). Algorithmic predictions aimed to assist pathologists in detecting mitotic hotspot locations, reducing omission of MF and improving classification against imposters. The inter-observer consistency for the MC significantly increased with computer assistance (interobserver correlation coefficient, ICC = 0.92) compared to the unaided approach (ICC = 0.70). Classification into prognostic stratifications had a higher accuracy with computer assistance. The algorithmically preselected MC-ROIs had a consistently higher MCs than the manually selected MC-ROIs. Compared to a ground truth (developed with immunohistochemistry for phosphohistone H3), pathologist performance in detecting individual MF was augmented when using computer assistance (F1-score of 0.68 increased to 0.79) with a reduction in false negatives by 38%. The results of this study prove that computer assistance may lead to a more reproducible and accurate MCs in ccMCTs.

pathology↗