13 papers · ranked by Valyu relevance
Christian Hallgrimson, Y. Lydia Li, Claire A. Shou, Ben Cardoen + 5 more
Single-molecule localization microscopy (SMLM) achieves nanoscale imaging of complex protein structures in the cell. However, the ability to capture structural variability across cell conditions (cell lines, gene expression, treatment) from 3D point cloud SMLM data remains limited. We present siMILe, a…
Simon Grouard, Christian Esposito, Jean El Khoury, Valérie Ducret + 11 more
Accurate prediction of patient outcomes remains a major challenge in oncology. While recent machine learning (ML) approaches often rely on bulk omics lacking spatial resolution or histology-based multiple instance learning (MIL), spatial transcriptomics (SpT) provides a unique opportunity to capture both molecular…
Salome Kazeminia, Muhammed Furkan Dasdelen, Bastian Rieck, Carsten Marr
Microscopic images of cells and tissues are central to disease diagnosis. In computational pathology, multiple instance learning (MIL) has emerged as a key paradigm for analyzing numerous images within a single patient sample. While the representative distribution of cells in a sample is important for diagnosis…
Chun-I Wu, Kalyan Banda, Elizabeth M. Swisher, Heba Sailem
Whole slide images (WSIs) contain hierarchical information from cellular to tissue architecture but their gigapixel scale poses major memory and computational challenges. Existing multi-scale graph and transformer models capture complex WSI features effectively but struggle with efficiency. We propose an Adaptive…
Shaimae I. Elhajjajy, Zhiping Weng
RNA-binding proteins (RBPs) are critical regulators of the human transcriptome, but the binding patterns of most RBPs are insufficiently characterized. While sequence context facilitates RBP binding specificity, its precise contribution remains unclear. Existing computational methods to decipher RBP binding patterns…
Ibrahim Alsaggaf, Daniel Buchan, Cen Wan
Cell-type identification plays a fundamental role in single-cell RNA-Seq analytics. Thanks to the recent success of the contrastive learning paradigm, the accuracy of automatic cell-type identification has also been improved. In this work, we propose a novel contrastive learning-based cell-type identification method…
Jorge Vergara, Cristian Perez-Gallardo, Ricardo Velasco, Dilan Martinez + 5 more
Accurate three-dimensional (3D) nuclear instance segmentation is a prerequisite for quantitative phenotyping in volumetric microscopy, yet remains challenging in densely packed tissues, irregular nuclear morphologies, and across heterogeneous imaging modalities. Here we present NucVerse3D, a deep-learning framework for…
Jinyu Zhang
Single-cell multi-omics technologies can measure multiple molecular layers within the same cell, such as gene expression and chromatin accessibility (scRNA-seq + scATAC-seq) or gene expression and cell-surface protein abundances (scRNA-seq + ADT), providing a multidimensional view of cellular states and regulatory…
Yan Gao, Yan Cui
Large-scale clinical and biomedical datasets increasingly contain both diverse subgroup attributes (e.g., demographic or clinical subgroups) and multiple prediction targets. Although various machine learning approaches can address subgroup differences or multi-target prediction, they often consider these aspects…
Niall Rodgers
Palaeontology has seen widespread and growing use of machine learning to classify and analyse large datasets of fossils. However, palaeontology is a challenging field in which to apply machine learning. Datasets may be small or unlabelled, images may be complex and different from standard datasets and palaeontologists…
Eiram Mahera Sheikh, Alaa Tharwat, Constanze Schwan, Wolfram Schenck
Pretrained cell segmentation models have simplified and accelerated microscopy image analysis, but they often perform poorly on challenging datasets. Although these models can be adapted to new datasets with only a few annotated images, the effectiveness of fine-tuning depends critically on which images are selected…
Bingxian Xu, Yiyang Zhang, Franziska Michor
Integrating diverse molecular modalities to obtain a comprehensive view of cellular identity remains a major challenge in single-cell biology. A fundamental but underappreciated obstacle is structural mismatch — the phenomenon in which the neighborhood structure of a cell differs depending on which molecular modality…
Marium H. Alvi, Ryley P. Nathaniel, Karmen Rai, Liya Ma
Adapting behaviour when reward contingencies change is a core function of cognitive control, but the underlying trial-by-trial computations are hard to observe when tasks cue each rule or allow only one switch per session. We developed the Feature-Rule Switching Task (FRST), in which common marmosets (Callithrix…