Study uses Apple ML to accurately detect and classify lung cancer.
problem Accurate diagnosis and sub-classification of non-small cell lung cancer.
method Evaluation of Apple Create ML module on histopathological images.
result 100% detection and successful subclassification of non-small cell lung cancer.
Researchers construct hyperbolic 4-manifolds using a 120-cell model.
problem Challenging to construct explicit hyperbolic 4-manifolds of small volume.
method Using small cover theory and classification of 120-cell small covers.
result Classification of all orientable 4-dimensional small covers over the 120-cell.
New deep learning method classifies cancer cells from small training sets.
problem Small training sets in medical imaging.
method Hybrid of transfer learning and GANs.
result 90-99% accuracy in classifying healthy and cancer cells.
This paper proposes using flying platforms for efficient small-cell base station association in 5G networks.
problem Efficient association and placement of backhaul hubs for small-cell base stations in 5G networks.
method Formulates the association problem considering backhaul link and NFP limitations, then presents a distributed greedy search solution.
result Observes favorable performance via numerical comparison with optimal exhaustive search algorithm.
RL-based power control improves VoLTE performance in indoor small cells.
problem Improving VoLTE performance in indoor small cells with RL.
method RL-based closed-loop power control algorithm.
result Significant improvement in voice retainability and MOS.
CST-YOLO improves blood cell detection with YOLOv7 and CNN-Swin Transformer.
problem Small-scale object detection in blood cells.
method YOLOv7 architecture enhanced with CNN-Swin Transformer, W-ELAN, MCS, CatConv.
result CST-YOLO achieves 92.7%, 95.6%, and 91.1% mAP@0.5 on three blood cell datasets.
Constructs a cell decomposition for the Fulton MacPherson operad FM_2.
problem Cellular decomposition of the Fulton MacPherson operad FM_2.
method Indexed by trees with two colors and cacti operad cells, compatible with operad composition.
result Computes generating functions for cell counts, algebraic.
Minimal example found for two finite CW-complexes sharing a common covering.
problem Finding the minimum number of 2-cells in two finite CW-complexes that share a common covering.
method Constructing an almost minimal example with two 2-cells in each complex.
result Minimal example with two 2-cells in each complex.
Newborn hippocampal cells in epilepsy are mostly abnormal, arising from a small subset of progenitors.
problem Understanding the origin of abnormal newborn hippocampal cells in epilepsy.
method Clonal analysis of Brainbow-labeled dentate granule cell progenitors in mice with status epilepticus.
result A small number of progenitors produce the majority of abnormal cells, suggesting pathological changes in progenitors or their microenvironments.
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.
Automatically counts microglial cells in rat spinal cord images, providing precise counts and uncertainty estimates.
problem Counting microglial cells in small, heterogeneous datasets is time-consuming and requires extensive training.
method Pre-processing to filter images, designing a non-parametric, non-linear kernel counter, providing uncertainty estimation.
result The method can provide precise counts and uncertainty estimates in small datasets, even with expert opinions.
Efficient algorithm for cell detection and segmentation in crowded images.
problem Instance segmentation in biological images with overlapping cells.
method Column generation approach with exact optimization and odd set inequalities.
result Rapid and accurate instance segmentation on multiple datasets.
Two complexes share a common covering but not a finite one.
problem Common covering of complexes with specific properties.
method Constructing CW-complexes K and L with a common covering.
result K is homeomorphic to a complex with a single 2-cell.
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.
Study identifies biomarkers for lung cancer in female non-smokers.
problem Identifying prognostic biomarkers for stage III NSCLC in non-smoking females.
method Gene expression profiling and XGBoost machine learning algorithm.
result Top biomarkers validated in literature, with AUC score of 0.835.
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.
Let P be the right-angled dodecahedron or 120-cell in hyperbolic space, and let W be the group generated by reflections across codimension-one faces of P. We prove that if Gamma is a torsion-free subgroup of minimal index in W, then the corresponding hyperbolic manifold H^n/Gamma is determined up to homeomorphism by Ga…
A new method for energy-efficient file delivery in small cell networks.
problem Efficient resource management in femto-caching with time-variant statistical properties.
method Formulates a resource allocation problem as a stochastic knapsack problem and a multi-armed bandit problem, developing solutions for each.
result The proposed method maximizes the accumulated utility over the horizon, especially suitable for networks with time-variant statistical properties.
New task aligns molecular structure with gene expression changes.
problem Modeling the relationship between chemical structure and gene expression changes.
method Developed a cross-modal small molecule retrieval task and a coordinated deep learning approach to align chemical structure and gene expression profiles.
result Demonstrated the feasibility of the new task and highlighted the limitations of current data and systems.
This abstract reviews recent methods for predicting protein-ligand binding affinity.
problem Predicting protein-ligand binding affinity for various applications in life sciences.
method Traditional and deep learning models for binding affinity prediction.
result Improved predictive performance of AI-driven models.
Deep learning model classifies drug effects based on structure and cell responses.
problem Limited ability to classify chemicals based on their modes of action.
method Integrative deep learning architecture combining molecular structures and cell responses.
result Improved classification performance, reducing error by 4.6%.
Collaborative filtering predicts drug responses from gene expression data.
problem Predicting drug responses from large gene expression datasets with limited samples.
method Low-rank matrix factorization and latent linear regression.
result The proposed method outperforms state-of-the-art methods in predicting drug-gene associations.
Quantum machine learning classifies lung cancer subtypes.
problem Accurately classify Adenocarcinoma vs Squamous cell carcinoma patients.
method Amalgamation of classical and quantum machine learning models, feature selection, QCrush data representation, Quantum Boltzmann Machine.
result Successfully classified 104 non-small cell lung cancer patients.
Autoencoder identifies cancer cells from normal ones using gene expression data.
problem Distinguishing between normal and cancer cells using gene expression profiles.
method Autoencoder trained on large tumor dataset to capture latent representations, using HPC toolkit for efficiency.
result Autoencoder node saliency identifies key differentiating features between normal and cancer cells.
Probability Density Estimation (PDE) is a multivariate discrimination technique based on sampling signal and background densities defined by event samples from data or Monte-Carlo (MC) simulations in a multi-dimensional phase space. In this paper, we present a modification of the PDE method that uses a self-adapting bi…
ANNs speed up radiation transport solving by 4x with minimal error.
problem Solving large-scale radiation transport problems efficiently.
method Artificial Neural Networks (ANNs) replacing Gaussian Elimination.
result ANNs reduce solution time by at least 4x with 1-3% error.
Built the smallest non-commensurable hyperbolic 4-manifold.
problem Finding the smallest non-commensurable hyperbolic 4-manifold.
method Gluing copies of a polytope to build the manifold.
result The constructed manifold has twice the minimal volume.
Model predicts drug response in tumors using genomic profiles.
problem Challenges in translating genomic insights to tumor-specific drug response predictions.
method Deep neural network (DNN) model trained on mutation and expression profiles of cancer cell lines.
result Achieved mean squared error of 1.96 for predicting IC50 values of 265 drugs.
New method learns transport cost from subset correspondence.
problem Aligning multiple datasets with accurate cost functions.
method End-to-end optimizer (OT-SI) that differentiates through Sinkhorn algorithm.
result Substantially outperforms state-of-the-art benchmarks.
We extend the canonical cell decomposition due to Epstein and Penner of a hyperbolic manifold with cusps to the strictly convex setting. It follows that a sufficiently small deformation of the holonomy of a finite volume strictly convex real projective manifold is the holonomy of some nearby projective structure with r…
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.
SRCA reduces high-dimensional data to lower dimensions while preserving geometric structures.
problem High-dimensional datasets with underlying geometric structures.
method Spherical Rotation Component Analysis (SRCA) incorporating geometric loss functions.
result SRCA provides a low-rank spherical representation of data with general theoretic guarantees.
We introduce and study the Hermitian matrix model with potential V(x)=x^2/2-stx/(1-tx), which enumerates the number of linear chord diagrams of fixed genus with specified numbers of backbones generated by s and chords generated by t. For the one-cut solution, the partition function, correlators and free energies are co…
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.
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.
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.
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…
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.
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.
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.
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.
Proposes CXNs for neural network computations on cell complexes.
problem Performing neural network computations on complex topological spaces.
method Introduces a message passing scheme and a unified encoder-decoder framework for cell complexes.
result Generalizes message passing to cell complexes and provides a cell2vec representation.
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.
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 …
Cataloging the neuronal cell types that comprise circuitry of individual brain regions is a major goal of modern neuroscience and the BRAIN initiative. Single-cell RNA sequencing can now be used to measure the gene expression profiles of individual neurons and to categorize neurons based on their gene expression profil…
Decomposition theory explores topological spaces and their quotient spaces.
problem Understanding the topology of quotient spaces given a decomposition.
method Analyzing upper semi-continuous decompositions and their shrinkability.
result An upper semi-continuous decomposition yields a homeomorphic quotient space under certain conditions.
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.