bioRxiv

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A multifaceted approach for obstructive sleep apnea classification from ECG signal using deep learning

bioRxiv Jan 1, 2025
Obstructive sleep apnea (OSA) is a common sleep disorder associated with increased cardiovascular and neurocognitive risks. While polysomnography remains the clinical gold standard for diagnosis, it is costly and unsuitable for large-scale or real-ti...
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Predicting Protein Crystal Solvent Content from Patterson Maps Using Machine Learning

bioRxiv Jan 1, 2025
Estimating the solvent content of protein crystals is fundamental to identifying the correct symmetry and phasing of the unit cell. Typically, the number of molecules in the asymmetric unit is not known and probabilistic methods are used based on sta...
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Machine Learning Models Based on Histological Images from Healthy Donors Identify ImageQTLs and Predict Chronological Age

bioRxiv Jan 1, 2025
Histological images offer a wealth of data. Mining these data holds significant potential for enhancing disease diagnosis and prognosis, though challenges remain, especially in non-cancer contexts. In this study, we developed a statistical framework ...
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Biomarker Discovery via Integrative Multi-omics for Children exposed to Humidifier Disinfectant

bioRxiv Jan 1, 2025
Exposure to humidifier disinfectants has been linked to an array of pulmonary disorders and diminished lung functionality particularly reduced Forced Vital Capacity (FVC). This investigation sought to identify diagnostic biomarkers for early detectio...
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Breaking Free from the Acquisition Dogma for Volume Electron Microscopy

bioRxiv Jan 1, 2025
Volume electron microscopy (VEM) enables three-dimensional (3D) visualization of thick specimens through serial sectioning and nanometer-resolution imaging. In an effort to overcome daunting challenges in subsequent data analysis, people tend to empl...
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Interpretable biophysical neural networks of transcriptional activation domains separate roles of protein abundance and coactivator binding

bioRxiv Jan 1, 2025
Deep neural networks have improved the accuracy of many difficult prediction tasks in biology, but it remains challenging to interpret these networks and learn molecular mechanisms. Here, we address the interpretability challenges associated with pre...
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Automated analysis of C. elegans behavior by LabGym: an open-source, AI-powered platform

bioRxiv Jan 1, 2025
The genetic tractability, well-mapped circuitry, and diverse behavioral repertoire of the nematode C. elegans make it an ideal model for physiological and behavioral studies. A wide range of methods has been developed for analyzing C. elegans behavio...
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MetaChrome: An Open-Source, User-Friendly Tool for Automated Metaphase Chromosome Analysis

bioRxiv Jan 1, 2025
DNA Fluorescence In Situ Hybridization (FISH) is an essential technique to study chromosome biology and genetics, enabling precise visualization of specific genomic loci to study structural abnormalities, gene mapping, and chromosomal rearrangements....
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Machine learning and multi-omic analysis reveal contrasting recombination landscape of A and C subgenomes of winter oilseed rape

bioRxiv Jan 1, 2025
Meiotic recombination is essential for generating genetic diversity, driving plant evolution, and enabling crop improvement, yet its uneven distribution across genomes constrains breeding efforts. Here, we investigated the multi-omic landmarks that s...
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Histolytics: A Panoptic Spatial Analysis Framework for Interpretable Histopathology

bioRxiv Jan 1, 2025
Quantifying spatial organization in hematoxylin and eosin (H&E)–stained whole-slide images (WSIs) is essential for uncovering tissue-level patterns relevant to pathology. We present Histolytics, an open-source, scalable Python framework for interpret...
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