Deep learning predicts personalized drug responses from medical data.
problem Predicting personalized drug responses from medical data.
method Deep learning algorithms applied to large datasets.
result Deep learning improves prediction of personalized drug responses.
New model learns continuous disease progression from RNA-seq data.
problem Continuous disease progression not captured by discrete categories.
method Covariate latent variable models for learning a low-dimensional data representation.
result Identifies genes stratifying patients on an immune-response trajectory.
Copula-based fusion improves breast cancer risk stratification.
problem Combining clinical and genomic risk scores using simple rules fails to capture their joint relationship.
method Used copulas to model the joint relationship between clinical and genomic risk scores.
result Copula-based fusion improves risk stratification, identifying subgroups with the worst prognosis.
We prove the existence of Verdier stratifications for sets definable in any o-minimal structure on (R, +, .). It is also shown that the Verdier condition (w) implies the Whitney condition (b) in o-minimal structures on (R, +, .). As a consequence the Whitney Stratification Theorem holds. The existence of (wf)-stratific…
The paper studies HKKN stratifications for non-compact spaces and proves convexity properties.
problem Proving convexity properties of moment maps for non-compact subsets.
method Algebraic and analytical study of HKKN stratifications for a vector space and compact Kähler manifold, then applying to non-compact subsets.
result Convexity properties of moment maps for invariant subsets are proven.
Stratifies representation varieties of twisted Hopf links.
problem Stratifying representation varieties of twisted Hopf links.
method Using stratification of AGLr(C)-representation varieties of the fundamental group of the complement of a twisted Hopf link. result Explicit description and computation of motives for ranks 1 and 2.
The paper defines a stratification for Lie groupoids in a tame topology context.
problem Presenting a tame topology counterpart to canonical stratification of Lie groupoids.
method Using Shiota's isotopy lemma and approximation theorem, the paper defines a canonical Whitney stratification of definable Lie groupoids into invariant strata.
result A canonical Whitney stratification of the Lie groupoid into definable strata invariant under the groupoid action.
Generalizes results for lambda-connections and Higgs bundles.
problem Understanding the Bialynicki-Birula stratification of lambda-connections.
method Analyzes the Bialynicki-Birula decomposition and its relation to Morse and partial oper stratifications.
result Fibers of the Morse and partial oper stratifications are transverse at the base point and are half-dimensional affine spaces.
Paper confirms MCS spaces are equivalent to CS sets.
problem Understanding the equivalence of MCS and CS sets.
method Analyzing the MCS stratification and intrinsic stratification.
result MCS spaces are equivalent to CS sets with respect to their stratification.
Alexandrov spaces have a special stratification that maps to spheres.
problem Characterizing the structure of Alexandrov spaces.
method Extremal stratification and space of directions analysis.
result Alexandrov spaces are homeomorphic to spheres in their space of directions.
Hidden stratification causes machine learning models to fail on rare but important patient subgroups.
problem Machine learning models fail on rare patient subgroups not identified during training or testing.
method Assessed techniques for measuring and describing hidden stratification effects on multiple medical imaging datasets.
result Evidence of hidden stratification leading to over 20% performance differences on clinically important subsets.
Investigates properties of moment maps and stratifications on Lie groups.
problem Understanding moment maps and stratifications on real reductive Lie groups.
method Functorial, algebraic approach to moment map and Kirwan-Ness stratification.
result Properties and properties of moment maps and stratifications established.
Optimizes biharmonic map regularity using stratification methods.
problem Improving the known almost optimal regularity of biharmonic maps.
method Quantitative stratification method.
result Optimal regularity results for minimizing biharmonic maps.
New stratification reveals intrinsic singularity types of orbit spaces.
problem Understanding the intrinsic structure of orbit spaces under Lie group actions.
method Introduced the isostabilizer decomposition and established a map to Klein strata.
result A new canonical stratification on the manifold clarifies the relationship with classical structures.
The aim of this paper is to compare stratifications of moduli spaces given by group actions in the case of similarity of matrices introduced by Arnold and the author's stratification by projective orbifolds, and its relation to deformations o elements in the moduli space.
Combines k-means and hill climbing for stratification and allocation.
problem Optimizing stratification and sample allocation for complex surveys.
method Combining k-means type algorithms with hill climbing.
result Multi-stage combination algorithms generally perform well compared to recent methods.
A new method uses gene interaction networks to predict gene functions.
problem Predicting gene functions from gene interactions.
method Context graph kernel approach in a machine learning framework.
result The proposed method outperforms linkage-assumption-based methods.
Social media enhances or diminishes scientific status, depending on usage.
problem Impact of social media on scientific stratification and mobility.
method Logistic Attribution Analysis combining statistical and machine learning methods.
result Social media promotes stratification and mobility, but beyond a threshold, it negatively impacts status.
Study clarifies variance of stratification estimators for causal effects.
problem Estimating average causal effects with discrete covariates.
method Combines insights from potential outcomes, causal diagrams, and structural models.
result Derives expressions for the variance of stratification estimators.
We give a geometric proof of existence of Whitney stratifications of definable sets in o-minimal structures.
This paper provides a stratification of semi-algebraic sets in the plane with finitely many geodesic segments.
problem How to stratify semi-algebraic sets in the plane with finitely many geodesic segments.
method Develops a semi-algebraic stratification of a real semi-algebraic set in the plane with open cells having the finiteness property.
result Provides insights for high-dimensional stratifications of semi-algebraic sets in connection with geodesics.
We study the topology of the inertia space of a smooth G-manifold M where G is a compact Lie group. We construct an explicit Whitney stratification of the inertia space, demonstrating that the inertia space is a triangulable differentiable stratified space. In addition, we demonstrate a de Rham theorem for differ…
The paper stratifies projective measured laminations and identifies a group of transformations.
problem Stratifying the space of projective measured laminations.
method Introducing a natural stratification and proving rigidity results.
result The group of self-homeomorphisms preserving the stratification is identified with the extended mapping class group.
Study lifts Schubert stratification to Spinn+1, revealing new Bruhat cells.
problem Lifting Schubert stratification to Spinn+1. method Explicit parameterizations of Bruhat cells using minimal-length permutations.
result Stratification of Spinn+1 reveals new geometric structure. VEGN uses graph neural networks to predict disease-causing mutations from genetic variants.
problem Identifying disease-causing mutations from millions of genetic variants.
method VEGN employs a graph neural network on a heterogeneous graph of genes and variants, learning gene-gene interactions.
result VEGN outperforms existing state-of-the-art models in variant effect prediction.
The paper describes a stratification of a compactified Hurwitz space using combinatorial trees.
problem Describing the boundary stratification of a compactified Hurwitz space.
method Using decorated trees to describe the boundary strata and their containment relations.
result The boundary strata of the compactified Hurwitz space are in bijection with decorated trees, and containment is given by edge contraction.
The paper studies harmonic map flows and proves rectifiability of singular sets.
problem Understanding the structure of singular sets in harmonic map flows.
method Investigates the stratification theory for suitable solutions using tangent measures.
result Each time slice of the singular set is rectifiable.
GSAE autoencoder models gene sets for better cancer subtype and prognosis analysis.
problem Inter-gene set associations not considered in gene set-based analyses.
method Gene superset autoencoder model incorporating prior gene sets.
result Gene supersets retain biological features and are reproducible for cancer subtype and prognosis.
A new method for identifying significant gene subsets improves disease prediction.
problem Identifying significant subsets of genes for disease prediction.
method Kernel gene shaving using influence function of kernel CCA.
result The proposed method outperformed three popular gene selection methods.
EpiRL learns to detect gene-gene interactions.
problem Computational challenges in epistasis detection.
method Modeling epistasis as a Markov Decision Process and using reinforcement learning.
result EpiRL discovers highly interacted genes.
The complement of a complex hyperplane arrangement is known to be homotopic to a minimal CW complex. There are several approaches to the minimality. In this paper, we restrict our attention to real two dimensional cases, and introduce the "dual" objects so called minimal stratifications. The strata are explicitly descr…
Let G be a Lie group, and let (M,ω) be a symplectic manifold. If G admits a Hamiltonian action on (M,ω) with momentum map μ, then M, the zero-level set of μ, the orbit space, and the corresponding symplectic quotient all have induced stratifications. We push this setting into the language of differential …
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.
New method expands seed genes to functionally related clusters.
problem Discovering functionally related genes lacking GO terms.
method Semi-supervised learning with positive and unlabeled examples.
result LPU approaches significantly outperform existing methods.
The paper offers simple, near-optimal algorithms for multi-group learning.
problem Learning predictors within subgroups of a population, addressing fairness and hidden stratification.
method Studies the structure of solutions and provides simple, near-optimal algorithms.
result Simple and near-optimal algorithms for multi-group learning.
A new method for joint eQTL mapping and gene network estimation.
problem Discovering SNP-gene relationships and gene-gene relationships in gene expression regulation.
method L1-2 regularized multi-task graphical lasso (L1-2 GLasso).
result Competitive performance on capturing true sparse structures of eQTL mapping and gene network.
New method handles correlated genes for better genomic prediction.
problem Technical issues with highly correlated genes in prediction models.
method Grouping algorithm that treats correlated genes as a group and uses their common patterns.
result Significantly outperforms standard models in prediction and feature selection.
Popular online enrichment analysis tools from the field of molecular systems biology provide users with the ability to submit their experimental results as gene sets for individual analysis. Such queries are kept private, and have never before been considered as a resource for integrative analysis. By harnessing gene s…
VGAE learns gene-disease associations from networks, predicting disease-genes.
problem Predicting gene-disease associations from disease-gene networks.
method Introducing VGAE, a variational graph auto-encoder for disease-gene prediction.
result VGAE and C-VGAE outperform baseline methods in disease-gene prediction.
Elucidating the genetic basis of human diseases is a central goal of genetics and molecular biology. While traditional linkage analysis and modern high-throughput techniques often provide long lists of tens or hundreds of disease gene candidates, the identification of disease genes among the candidates remains time-con…
Decomposes smooth manifolds into algebraic submanifolds.
problem Understanding the structure of smooth manifolds induced by continuous selections.
method Generic continuous selection of smooth functions provides stratification of the manifold.
result Stratification leads to local topological structure with nondegenerate critical points.
Proposes a method to identify relevant genes in autism-related diseases using auxiliary information.
problem Identifying relevant genes in autism-related diseases from diverse data sources.
method Uses logistic regression to filter irrelevant genes and clusters relevant genes into cohesive groups using adjacency matrix.
result Superior performance and robustness in finite samples observed in simulation studies.
The monster tower is a tower of spaces over a specified base; each space in the tower is a parameter space for curvilinear data up to a specified order. We describe and analyze a natural stratification of these spaces.
Study chaotic dynamics in social stratification models leading to thermalization and turbulence.
problem Understanding social stratification dynamics through chaotic nonlinear systems.
method Modeling social network links with oscillators and energies, studying Hamiltonian evolution and nonlinear interactions.
result Chaotic dynamics leads to dynamical thermalization and Kolmogorov-Zakharov turbulence, with implications for wealth inequality.
Study identifies and estimates treatment effect heterogeneity within principal stratification subpopulations.
problem Causal inference with intermediate outcomes and treatment effect heterogeneity.
method Proposes a novel doubly cross-fit doubly robust machine learner to efficiently learn conditional principal causal effects under principal ignorability.
result Demonstrates informative patterns of treatment effect heterogeneity within the always-survivor subpopulation in an acute lung injury trial.
Study almost rigidity of super Ricci flow with non-negative Muller quantity.
problem Almost rigidity properties of super Ricci flow with non-negative Muller quantity.
method Almost splitting and quantitative stratification theorems established by Bamler for Ricci flow.
result Obtained almost constancy for a certain integral quantity concerning scalar curvature at an almost self-similar point.
We consider a Morse function f and a Morse-Smale gradient-like vector field X on a compact connected oriented 3-manifold M such that f has only one critical point of index 3. Based on Laudenbach's ideas, we will show that the flow of X can be isotoped into one so that the trajectory spaces of the new flow pro…
New method improves compatibility of risk stratification models without sacrificing accuracy.
problem Compatibility issues arise when updating clinical machine learning models.
method Proposes rank-based compatibility measure and new loss function.
result Increased compatibility of models by 0.019 with no loss in discriminative performance.