Improved GPLVM model for single-cell RNA-seq data.
problem Lack of effective scalable models for clustering cell types in large-scale single-cell RNA-seq data.
method Introduces amortized stochastic variational Bayesian GPLVM (BGPLVM) tailored for single-cell RNA-seq.
result Matches the performance of scVI on synthetic and real-world datasets and reveals more interpretable latent structures.
New model identifies cell-specific genes for cancer prognosis.
problem No statistical model to integrate multiscale cancer data.
method Bayesian generalized promotion time cure models (GPTCMs).
result Improves cancer prognosis by identifying cell-specific genes.
Generative model predicts cell and nuclear structure from images.
problem Predicting subcellular structures from microscopy images.
method Conditional generative model using autoencoders.
result Photo-realistic cell images generated with probabilistic interpretation.
New metric scores perturbations across populations, not cells, improving model comparison.
problem Single-cell perturbation data overlaps, making per-cell accuracy unreliable.
method Average per-cell probability vectors over all cells of a perturbation to form a population profile and rank candidate perturbations.
result Classifier Discrimination Score (CDS) identifies true perturbation more reliably than pseudobulk-based scores.
Bayesian model learns cell types and gene networks from two data views.
problem Estimating cell types and their regulatory networks from single-cell gene expression and epigenetic data.
method Symphony Bayesian hierarchical multi-view mixture model with Variational EM inference.
result Symphony outperforms other methods in learning cell types and regulatory networks.
A new method infers neuronal cell types and their gene expression profiles from brain imaging data.
problem Lack of spatial information in single-cell RNA sequencing data.
method Spatial point process mixture model applied to in situ hybridization images.
result Inferred cell types and gene expression profiles validated with single-cell RNA sequencing data.
Proposes CCCVAE for better single-cell clustering with cell-cell communication.
problem Improving single-cell RNA sequencing clustering by incorporating cell-cell communication.
method Integrates cell-cell communication into a variational autoencoder framework.
result Empirical results show CCCVAE outperforms standard VAEs in clustering performance.
SimCD simultaneously clusters cells and identifies differential gene expression in scRNA-seq data.
problem Separate clustering and differential expression analysis for scRNA-seq data leads to suboptimal results.
method Develops SimCD, a unified hierarchical gamma-negative binomial model for simultaneous cell clustering and differential expression analysis.
result SimCD outperforms existing methods in discovering cell clusters and capturing dynamic expression changes.
New model clusters cells and individuals, revealing genetic influences on cell types.
problem Clustering nested data with group-level and observation-level variables.
method Nested Atoms Model (NAM), Bayesian nonparametric approach.
result Identifies clusters of genetically similar individuals with homogeneous cell-type profiles.
Framework detects and classifies multi-label RBC images from microscopic images.
problem Challenges in separating touching or overlapping cells for classification.
method Region proposal model + CNN feature extraction + multi-label prediction networks.
result Framework achieves good performance in automatic cell detection and classification.
MarkerMap selects key genes for cell type analysis in single-cell RNA-seq.
problem Selecting informative genes from large single-cell RNA-seq datasets is challenging and computationally intensive.
method MarkerMap is a generative model that identifies minimal gene sets explaining cell type variability.
result MarkerMap outperforms existing methods in both supervised and unsupervised marker selection.
New model generates realistic single-cell gene expression data.
problem Generating realistic single-cell gene expression profiles is challenging.
method scLDM, a latent diffusion model using Diffusion Transformers and linear interpolants.
result Superior performance in generating realistic single-cell gene expression data.
Neural network predicts electrochemical cell faults with 53% less error.
problem Predicting faults in electrochemical cells to avoid safety hazards and reduce costs.
method Self-supervised encoder-decoder neural network that learns degradation from operating conditions.
result Predicted voltage with 53% less error than parametric models, 64% faster fault prediction.
Algorithm discovers dynamic cell structures for better neural network performance.
problem Finding optimal neural network architectures for diverse data samples and time steps.
method Combines recurrent and recursive neural networks to dynamically search for customized cell structures.
result Achieves better prediction accuracy compared to existing models.
Proposes a model for identifying 4G cells with network throughput problems.
problem Challenges in identifying 4G cells with network throughput issues due to network complexity and privacy concerns.
method Data-driven model using clustering and Deep Neural Networks (DNNs). Model parameters are learned from a small number of expert-labeled data. Multiple clustering models capture common features for problematic cells.
result The proposed model outperforms a simple classifier in identifying cells with network throughput problems.
New model reconstructs cell differentiation paths from single-cell RNA data.
problem Reconstructing dynamic biological phenomena from noisy, heterogeneous, and sparse single-cell RNA-seq data.
method Developed a generative model using Dirichlet diffusion tree and Markov chain Monte Carlo sampler.
result Recovered latent trajectories from simulated single-cell transcriptomes.
Modeling curvature-sensitive cells in visual cortex with geometric structures.
problem Understanding the functional architecture of curvature-sensitive cells in the visual cortex.
method Geometric model based on Engel structure and SIM(2) symmetry.
result Identified SIM(2) as the natural symmetry group for curvature-sensitive cells.
The process of morphogenesis, which can be defined as an evolution of the form of an organism, is one of the most intriguing mysteries in the life sciences. It is clear, that gene expression patterns cannot explain the development of the precise geometry of an organism and its parts in space. Here, we suggest a set of …
Efficiently learns HMMs across multiple cell types using spectral methods.
problem Learning parameters of large HMMs for comparative epigenomics is computationally challenging.
method Developed a latent variable model and an efficient spectral algorithm exploiting tree structure of hidden states.
result Provided sample complexity bounds and experimentally validated on nine human cell types.
Deep CNN model predicts neuronal cell health from images.
problem Predicting the biological activity of chemical compounds on neuronal cells.
method Deep convolutional neural network (CNN) with residual connections.
result Achieved 99.6% accuracy in distinguishing treated from untreated cells.
Researchers infer gene activity in dividing cells, accounting for protein inheritance and division history.
problem Inferring protein production kinetics in dividing cells due to protein inheritance and division history.
method Adapted conditional normalizing flows to approximate intractable likelihoods from simulated data.
result Glc3 gene is mostly inactive under stress, with brief and transient expression.
Hexagon grid patterns emerge from conformal isometry in grid cell neural networks.
problem Understanding the algebraic, geometric, and topological properties of grid cells.
method Investigating recurrent neural network models of grid cells, focusing on Lie group and Lie algebra representations, conformal isometry, and hexagon periodic patterns.
result Conformal isometry leads to hexagon periodic patterns in grid cell responses and accurate path integration.
NESS improves neighbor embedding for smooth cell-state transitions in single-cell data.
problem Challenges in extracting smooth, low-dimensional representations from noisy single-cell data.
method Builds on PCS framework to develop NESS, a stable machine learning approach.
result NESS consistently yields useful biological insights across diverse single-cell datasets.
Extends NAS to learn both intra-cell and inter-cell architectures for language modeling.
problem Limited NAS systems restrict search to recurrent or convolutional cells.
method Designs a joint learning method to perform intra-cell and inter-cell NAS simultaneously.
result Significantly outperforms a strong baseline on PTB and WikiText data.
Better neural arithmetic logic units improve cell counting model generalization.
problem Neural networks struggle with high cell counts outside training data range.
method Introduced Neural Arithmetic Logic Units (NALU) for arithmetic operations in existing architectures.
result Improved cell counting accuracy for higher numeric ranges with better generalization.
Graph Attention Networks predict disease state from single-cell data.
problem Predicting disease state from single-cell data.
method Graph Attention Networks (GAT) for learning from both features and graph structures.
result Achieved 92% accuracy in predicting MS from single-cell data.
Kernel testing compares cell states in single-cell data.
problem Comparing non-linear cell states in single-cell data.
method Kernel-based testing framework for non-linear distribution comparison.
result Identifies subtle population variations in cell states.
Deep model interprets gene expression from single-cell RNA sequencing.
problem Interpreting gene expression levels from single-cell RNA sequencing data.
method Probabilistic model with neural network conditional distributions, variational inference, stochastic optimization.
result The model outperforms state-of-the-art methods for differential expression analysis.
GENOT matches cells across data modalities using neural OT solvers.
problem Scalability, privacy, and out-of-sample estimation issues in traditional OT solvers.
method Learn stochastic maps, parameterize OT maps, relax mass conservation, integrate quadratic solvers.
result Demonstrates significant potential for enhancing therapeutic strategies.
ARMA cell simplifies neural autoregressive modeling for time series.
problem Complex RNN cells are not always necessary and can be inferior.
method Introduces ARMA cell, a simpler, modular approach for neural time series modeling.
result The ARMA cell is competitive with popular alternatives in performance.
Study pentagon growth with laser-cut models.
problem Explore topological and geometric properties of pentagon cell growth.
method Cell growth process in Euclidean plane, physical representations created with laser cutter.
result Aesthetic and geometric insights from pentagon growth models.
HSSE framework embeds single-cell RNA-seq data at multiple scales.
problem Capturing heterogeneous local structure in single-cell RNA-seq data.
method Hierarchical sheaf spectral embedding (HSSE) framework.
result HSSE achieves competitive or improved performance in single-cell RNA-seq data representation learning.
A deep model detects differentially expressed genes from single-cell RNA seq data.
problem Detecting differentially expressed genes from single-cell RNA sequencing data.
method Probabilistic model with neural network conditional distributions, variational inference, stochastic optimization.
result The model outperforms state-of-the-art methods for differential expression detection.
sgdGMF efficiently estimates generalized matrix factorization models for single-cell RNA sequencing data.
problem Challenges in dimensionality reduction for large single-cell RNA sequencing datasets.
method Scalable adaptive stochastic gradient descent algorithm for generalized matrix factorization models.
result sgdGMF outperforms existing methods in scalability and accuracy for large datasets.
Mathematical model describes how red blood cells return to equilibrium.
problem How red blood cells regain equilibrium after deformation.
method Gradient flow of the Canham-Helfrich functional, proving global existence and convergence for spheres and axisymmetric tori.
result Global existence and convergence of smooth solutions for spheres and axisymmetric tori under specific energy conditions.
Few-shot cell segmentation from diverse sources to target domain.
problem Efficient cell segmentation from limited annotated images.
method Meta-learning combining cross-domain tasks and invariant representation.
result Promising results from 1-10-shot learning on public databases.
RVAE detects and repairs corrupted cells in mixed-type tabular data.
problem Outlier detection and repair in mixed-type tabular data.
method Robust Variational Autoencoder (RVAE) learns the joint distribution of clean data and identifies outlier cells.
result RVAE outperforms state-of-the-art methods in cell outlier detection and repair for tabular data.
DISPR uses diffusion models to predict 3D cell shapes from 2D images.
problem Predicting 3D cell shapes from 2D microscopy images.
method Diffusion model trained to predict 3D shapes from 2D microscopy images as a prior.
result Adding DISPR predictions to minority cell classes improves classification accuracy.
This study reviews and evaluates clustering methods for single-cell RNA-seq data.
problem Identifying and characterizing novel cell types from single-cell RNA-seq data.
method Review and performance comparison of clustering methods.
result Performance comparison experiments on two datasets.
Forest Fire Clustering discovers cell types from single-cell data.
problem Discovering cell types from large-scale single-cell sequencing data.
method Iterative label propagation and parallelized Monte Carlo simulation.
result Forest Fire Clustering outperforms state-of-the-art methods on diverse benchmarks.
New method learns complex cell networks from millions of cells.
problem Existing methods fail to scale to large datasets.
method Multi-axis Gaussian graphical models.
result Method scales to millions of cells in minutes.
Matching cells over time has long been the most difficult step in cell tracking. In this paper, we approach this problem by recasting it as a classification problem. We construct a feature set for each cell, and compute a feature difference vector between a cell in the current frame and a cell in a previous frame. Then…
A new model for complex cells accounts for insensitivity to image shifts.
problem Complex cells' response to image shifts.
method Linear combination of Gaussian derivatives at a single position.
result Maximum response insensitive to small shifts of the image.
Input-cell attention improves RNN saliency detection over time.
problem Vanishing saliency in RNNs makes them unreliable for detecting important features at arbitrary time intervals.
method Proposes input-cell attention, a novel RNN cell structure that uses a fixed-size matrix embedding to attend to different inputs from current or previous time steps.
result Input-cell attention produces a saliency map that can detect important features regardless of their occurrence in time.
The study identifies all possible vector field structures on specific 2D shapes.
problem Optimal discrete gradient vector fields on surfaces with 1-2 critical cells.
method Analysis of discrete vector fields on 2D shapes with minimal critical cells.
result All possible structures of discrete Morse functions on specified shapes.
Multi-StyleGAN simulates live cell microscopy imagery.
problem Costly and complex live cell experiments.
method Generative adversarial network (GAN) synthesizing multi-domain time-lapse images.
result Captures biophysical factors and time dependencies in cell imagery.
Method improves microbial biomass yield estimation from noisy data.
problem Estimating microbial biomass yields from noisy cell counts and substrate measurements.
method Probabilistic macrochemical modeling to relax cell weight assumptions and improve robustness.
result Model provides accurate uncertainty estimates of key parameters.
The paper explores constructing an invariant for s-move 3-cells using 2-cell decompositions.
problem Creating an invariant for s-move 3-cells.
method Using elementary 3-expansions and 2-cell decompositions, the paper constructs an invariant.
result The method provides a sequence of 2-cells to decompose s-move 3-cells.